WO2011126576A2 - Genetic signatures in the envelope glycoprotein of hiv-1 - Google Patents

Abstract

The present invention relates, in general, to HIV-1 and, in particular, to immunogens that elicit broadly neutralizing antibodies against HIV-1, and compositions comprising same. The invention further relates to methods of inducing the production of such antibodies in a subject.

Description

GENETIC SIGNATURES IN THE ENVELOPE

GLYCOPROTEIN OF HIV-1

This application claims priority from U.S. Provisional Application No. 61/322,663, filed April 9, 2010, the entire content of which is incorporated herein by reference.

This invention was made with government support under Grant No.

AI067854 awarded by the National Institutes of Health. The government has certain rights in the invention.

TECHNICAL FIELD

The present invention relates, in general, to HIV-1 and, in particular, to immunogens that elicit broadly neutralizing antibodies against HIV-1 , and compositions comprising same. The invention further relates to methods of inducing the production of such antibodies in a subject.

BACKGROUND

Elicitation of broadly cross-reactive neutralizing antibody (NAb) responses is a high priority for HIV-1 vaccines [1-4]. Many candidate immunogens elicit strong NAb responses against highly neutralization-sensitive strains of HIV-1 ; however, these vaccine-elicited antibodies neutralize very few circulating strains [5-7] and have not afforded protection in past human efficacy trials [8-10]. A recently completed efficacy trial in Thailand (RV144), in which a modest reduction in the rate of HIV-1 infection was observed [11 ], provides hope that with further improvements a more acceptable level of efficacy is obtainable. It is too soon to know whether NAbs might have contributed to the observed efficacy in RV144. Based on immunogenicity data from earlier phase I and II clinical trials of this and related vaccines [4,12], improved NAb responses may be one way to achieve greater protection. Such improvements are likely to require novel vaccine designs. Most current efforts to design NAb-based HIV-1 vaccine immunogens are guided in part by knowledge of the molecular structure of the viral Envelope (Env) glycoproteins that serve as the sole targets for NAbs [13-16]. These Env glycoproteins consist of a surface gpl20 and transmembrane gp41 that associate non-covalently and assemble into a trimeric complex of gpl20-gp41 heterodimers on the virus surface, where the mature Env trimer spike mediates virus entry into host cells [17-19]. Entry is mediated by successive binding of gpl20 to its cellular CD4 receptor and an obligatory coreceptor, most often the chemokine receptor CCR5, triggering conformational changes that permit gp41 to induce membrane fusion [18-20]. Env trimers and their individual constituents are genetically variable, conformationally flexible and heavily glycosylated, making them difficult targets for NAbs [1 ,2,19,21]. Because fitness constraints do not permit the virus to evolve to become completely resistant to neutralization

[22,23], certain NAb epitopes remain vulnerable that are of particular interest for vaccine development. Some of these epitopes are well studied, whereas others remain unknown or only partially characterized [2,4,24].

The structural complexity of Env requires sophisticated methods for the analysis of NAb epitopes. X-ray crystallography and cryo-electron tomography, together with data from mutagenesis and biophysical studies, have been used to illuminate several vulnerable regions in great detail. Examples of how this information is used for novel immunogen designs include the optimization and stabilization of epitopes in the receptor and coreceptor binding regions of gpl20 [25-27]. Other examples include innovative structural variants of gp41 [28-30] and optimal mimics of gpl20 and gp41 epitopes recognized by broadly neutralizing monoclonal antibodies (mAbs) [31-35]. Although these new design efforts are in early stages of testing, none so far have yielded substantial improvements.

Many new concepts for NAb-inducing vaccines based on HIV-1 Env are being explored. These concepts are complicated by inconsistencies between the antigenic and immunogenic properties of key epitopes. For example, Env antigens that possess high affinity epitopes for broadly neutralizing mAbs fail to elicit these types of antibodies [36-39, 28-30]. Also, gpl20 antigens similar to those that performed poorly as early vaccine candidates contain epitopes that are capable of absorbing-out a substantial fraction of broadly NAbs in sera from a subset of HIV- 1 -infected individuals [40-43]. Some B cell responses might be down regulated by self-tolerance mechanisms, as has been suggested for epitopes in the membrane proximal external region (MPER) of gp41 [44,45]. Other B cell responses might be down regulated by immunosuppressive properties of gpl20 [46-48]. Although it remains unclear why some of the most attractive Env epitopes are poor immunogens, the potent neutralizing activities of a subset of human mAbs [49,50] and sera from HIV- 1 -infected individuals [51] suggest it might be possible to design better vaccine immunogens.

A greater understanding of the antigenic and immunogenic properties of Env should facilitate the discovery of an effective HIV-1 vaccine. New insights are being gained from the use of computational analyses of large neutralization datasets derived from assays with HIV-1 -positive sera and molecularly cloned Env-pseudotyped viruses. Statistically significant associations are sought between the neutralization susceptibility of a virus and its Env amino acid sequence. Previously, several amino acid signatures in gpl20 and gp41 were identified that strongly associate with the antigenic determinants of NAbs in sera from HIV-1 -infected subjects [52,53], Such signatures could either be a consequence of direct contacts for NAbs, or reflect conformational

requirements/constraints that regulate Ab access.

Because of the distinctive lineages in HIV evolution, found at multiple levels, it is critical to correct for the phylogenetic associations among sequences when defining signatures. Not accounting for phylogeny can lead to spurious positive signals that result from lineage effects and a reduced sensitivity, as was seen when associations were sought between host HLA and amino acid substitutions at the population level [54, 55].

The present invention results, at least in part, from the use of

computational strategies to identify amino acid mutational patterns that correlate with NAb profiles independently of founder effects. Three distinct phylogenetically-corrected statistical approaches have been used. The first included modifications of the approach taken by Bhattacharya et al. [54]; new modifications enabled looking at combinations of sites and amino acids within sites. Two novel statistical strategies for defining signatures were added, conditional mutual information and a modified decision forest approach. The computational signature identification methods were tested by accurately identifying a subset of the known determinants of the epitope for broadly neutralizing mAb bl2. These methods were then applied in a reciprocal fashion to determine whether amino acid signatures in the Env proteins from HIV-1 infected individuals with particularly broad NAb responses could be identified relative to individuals who do not elicit broad responses. The findings suggest that broadly NAb responses are determined in part by features in the CD4-induced (CD4i) co-receptor binding site (CoRbs) of gpl20.

SUMMARY OF THE INVENTION

In general, the present invention relates to HIV-1. More specifically, the invention relates to immunogens that elicit broadly neutralizing antibodies against HIV-1 , and to compositions comprising same. The invention further relates to methods of inducing the production of broadly neutralizing antibodies in a subject.

Objects and advantages of the present invention will be clear from the description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent application file contains at least one drawing executed in color. Copies of this patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

Figure 1. Maximum Likelihood tree of the Env sequences showing ancestral states and amino acid in the end taxa for position 185. This tree illustrates the distribution of bl2 sensitive (magenta) and resistant (gray) Envs among the different subtypes and recombinant lineages, and their genetic relationships. Envs (n = 319) are included in the tree, of which 251 are matched to phenotypes. Among the 68 without a phenotype is the blinded test set; bl 2 sensitivity values were obtained for 56 of these in the test set. There are many recombinant sequences included in the tree (as with essentially all HIV population trees), limiting the accuracy of the reconstruction of the evolutionary history. The phylogenetic corrections utilized for signature analysis are, however, dependent on the local region of the tree and the ancestral states near the tips of the branches, reducing the impact of inter-subtype recombination on the analyses. An Aspartic acid (D) at position 185 is strongly associated with bl2 susceptibility (D versus not D [written as !D]) in the top contingency table. The phylogenetically corrected signature analysis supports this association, indicating it is not merely an artifact of one or more clades within the tree being more or less susceptible to bl2. Thus, when an Env has mutated towards a D in this position (!D to D), the Env tends to be susceptible to bl2, but when it moves away from it, (D to !D), the Env tends to be resistant. This tree is displayed with midpoint rooting.

Figures 2 A and 2B. Correlates of bl 2 sensitivity. Fig. 2 A) Counts of bl 2 sensitive and resistant viruses grouped by subtype, intersubtype recombinant and circulating recombinant forms (C Fs). Subtypes D (1 Env) and G (n = 5), CRT 14 (n = 3), and recombinant Envs including A/CRP02 (n = 1), A/C/D/ (n = 3) and A/D (n = 4), were grouped into the "other" category. The only 2 subtype categories with greater numbers of bl 2 sensitive than resistant Envs were subtype B and B/C recombinants (in these two cases the green bars are higher than purple). A Kruskal-Wallace non-parametric comparison of all groups indicated that at least one subtype was distinctive (p = 0.033). A comparison of the B subtype Envs versus all others indicated that they were far more likely to be sensitive to neutralization by bl2 (Fisher's exact p-value 3.7 x 10^"5, odds ratio 3.6, 95 % confidence interval: 1.9, 6.9). Fig. 2B) sCD4 susceptibility is greater among bl2 sensitive viruses. The amount of sCD4 required for 50% neutralization was greater among bl 2 resistant viruses (Wilcoxon rank test, p= 0.0001 , median and interquartile range shown to the right of each distribution, median 9.8 μ / ύ among bl 2 resistant viruses, median 5.1 μg/ml among sensitive viruses).

Furthermore, among just the bl 2 sensitive viruses, the levels required for 50% neutralization by sCD4 and bl 2 were correlated (linear regression of log values, p = 0.003, R = 0.29, data not shown).

Figure 3. Alignment of bl2 signature positions from each sequence with particular amino acids associated with bl 2 resistance and sensitivity. This alignment includes the 7 non-contiguous positions found using the contingency table approach with defined resistant/susceptibility patterns: positions 173, 185, 268, 364, 369, 461 , and 651. The 5 signature sites identified by the decision forest strategy are a subset of these 7 sites. The positions are aligned to the consensus of the susceptible viruses, which in each case is an amino acid that was associated with bl 2 susceptibility, as shown in dark green at the top of each column. The 7 positions were extracted from each sequence. If the amino acid was the same as the bl 2 sensitive consensus at the top of the column, a space is left in the row. Spaces are indicative of the consensus susceptible form, where differences in sequence stand out more sharply. If the amino acid differed from the susceptible consensus, but was another amino acid associated with

susceptibility, it is shown as light green. Amino acids associated with resistance are shown in red. Amino acids that were different but not associated with either resistance or susceptibility are shown in black. The susceptible viruses are ordered from the top left column through the second column, from the most sensitive (top left) to the least sensitive (bottom right) in terms of the

concentration required for 50% neutralization. The least sensitive Envs (those require concentrations of 25-50 μg/ml of bl2) were grouped with sensitive viruses and are boxed at the bottom of the second column.

Figures 4A-4D. Structural mapping of bl 2 signature sites in gpl 20.

Fig. 4A. Locations of 8 bl 2 signature sites in a three-dimensional structure of gpl 20 (PDB code: I RZK) with VI , V2 and V3 loops modeled for visualization described previously [128]. Yellow balls mark the C-alpha positions of signature residues. Fig. 4B. Locations of 3 signature sites that occur at the bl2 binding face of gpl20. The bl2 (magenta) bound structure of gpl20 (blue), corresponding to PDB code:2NY7. The red region in gpl20 is less than 6.5 A from the bound bl2 antibody. Fig. 4C. Isosurface of the gpl20 molecule showing the difference in electrostatic potential (+0.3 kT/e) due to mutation E268R in gpl20 that results in a net positive electrostatic potential (blue) at the M2-gpl20 interface region. Isosurface (+/-1 kT/e ) of the bl2 molecule showing the positive (blue) and negative (red) electrostatic potentials indicating bl 2 is highly electropositive (overall charge of +12). Fig. 4D. An illustration to capture how position 651 could impact binding to bl2 through an allosteric pathway involving the gpl 20- gp41 interface. X-ray structure of bl2 (marked in magenta) bound to a liganded gpl 20 core protein (PDB code: 2NY7) with a monomer gp41 that was homology- modeled based on the NMR structure of SIV gp41 [129] that is more

representative of a post-fusion conformation. The region of gpl20 that is in contact with bl2 is marked in red. The disulfide bridged loop region of the gp41 molecule that is expected to interact with gpl20 was placed in close proximity to the region where the N- and C-termini of gpl 20 come in close contact. This model is useful for illustration but does not represent the actual gpl20-gp41 interaction, which is not yet resolved. A yellow ball indicates position 651 in gp41. Green balls are used to show the covarying sites at positions 84, 169, 429 and 432 in gpl 20, and position 602 in gp41. Silver balls in the model capture sites in gpl 20 and gp41 that have been shown through past experimental studies to influence gpl 20- gp41 assembly.

Figures 5 A and 5B. Clustered heatmaps of sera and the test panel.

Figs. 5A. K-means clustering of serum samples and virus isolates in the test panel, k=3. A 90% threshold for stability was used as a minimum criterion for defining robust clusters in the sera, given re-sampling noise due to experimental variation and bootstrap re-sampling of the test panel of Envs. 75% was used for the clustering the Envs in the figure, and these clusters were not subsequently used for analysis. The color keys on the top and on the left indicate the clusters and their statistical robustness: red, blue and yellow correspond to the three clusters, with each robust cluster boxed. Blends of the three primary colors indicate how often in the re-sampling tests for a given serum or Env the sample falls in different clusters. The intensity of the color indicates how frequently each falls in its primary cluster. In the heat map, darker red indicates that the serum neutralized the virus potently, progressively lighter colors through yellow indicate increasing resistance, and cream color is completely resistant. Fig. 5B. K-means clustering of serum samples and virus isolates in the test panel, k= 2; again a 90% threshold for stability was used for the sera, 75% for the viral Envs.

Figure 6. Maximum Likelihood tree of the Env sequences showing ancestral states and amino acid in the end taxa for position 185. This tree illustrates the distribution Envs organized into a phylogeny as sampled from potent (magenta) or weak (gray) sera. The Envs used in the test panel of pseudoviruses were included along with the Envs from the serum samples in the tree; the taxa without a magenta or dark gray mark are from the test panel. The evolution of the signature site 419 K with respect to the phylogenetic tree is highlighted. Arg (R) is the most common amino acid in this position, and K is very rarely an ancestral state.

Figure 7. Alignment of signature sites that were associated with potent NAb responses. Unlike the alignment in Figure 3 that only included signature positions, this alignment captures short contiguous regions of Env near the CoRbs. Signature amino acids are highlighted using the same color scheme and organization as the heatmap in Figure 5A. Red highlights are amino acids that associate with potent sera; yellow highlights are amino acids that associate with weak sera. A vaccine strain selected on the basis of Envs in potent neutralizing sera from H IV- 1 -infected individuals might ideally capture as many of the red positions and as few of the yellow as possible (e.g., CH0219.e4 and

CH080510.e.p2). CH0219.e4 (see Figs. 13 and 14) might be particularly promising because it also has short variable loops (data not shown). Position 186 was identified using CMI and thus does not have specific amino acids associated with the serological behavior; however, both E and N seem particularly enriched in the group with the highest cross reactivity (cluster III).

Figure 8. The four signature sites in the CCR.5 CoR region shown in a crystallographic three-dimensional structure of gpl20 complexed with CD4 and the CD4i-specific mAb 17b (PDB code: lRZK). The yellow balls mark the C- alpha positions of the signature residues. Three regions in gpl20 are indicated: the inner domain in light blue, the outer domain, dark blue; and the bridging sheet, brown. Definitions for these regions are based on the X-ray study of Kwong et al.

[127]. CD4 is marked in green. The light and heavy chains of 17b are marked in light and dark magenta, respectively.

Figure 9. CMI sites: HXB2 positive 163,182,665.

Figure 10. Signature sensitivity score correlates with bl 2 sensitivity.

Figure 1 1. Genetic signatures of broadly NAb responses. Shown is how sera from HIV-infected individuals were tested for neutralizing activity against genetically diverse strains HIV. Results among the serum samples were used to construct a "heat-map" to identify common patterns of reactivity. Novel computational analyses were used to compare these patterns to Env sequences in the serum samples in an effort to identify genetic signatures that associated with potent neutralizing antibody responses. Five signatures were identified.

Figure 12. Signatures on an X-ray-crystal structure of gpl20. Shown is the location of four of the signatures identified in Fig. 1 1 on a crystal structure of ligated gpl 20 (the fifth signature is not shown because it is in a region of gpl20 that is not present in the crystal structure). All 5 signatures reside in the CD4i region of gpl20 that is reconized by monoclonal antibody 17b (17b is the pink ribbob structure in the figure).

Figure 13. Sequence information for 0219 Env, including gpl60 encoding sequence with start and stop codons shown, gpl 60 amino acid sequence, gpl 60 codon optimized encoding sequence, gpl40 amino acid sequence and gpl40 codon optimized encoding sequences.

Figure 14. SDS-PAGE and Western Blot of purified HV 13341

(CH0219 e4ENV gpl40C).

DETAILED DESCRIPTION OF THE INVENTION

An increase in knowledge of the molecular and antigenic structure of the HIV-1 envelope glycoproteins (Env) has yielded important new insights for vaccine design but translating this information to an immunogen that elicits broadly neutralizing antibodies has been difficult. The present invention is based, at least in part, on the use of phylogenetically-corrected statistical methods to identify amino acid signature patterns in Env that are associated with the neutralizing potency of the serum from which they were derived. The utility of methods for defining signature amino acid mutation patterns that correlate with neutralization phenotype was examined by analyzing Env sequences from 251 clonal viruses that were differentially sensitive to neutralization by the well- characterized gpl20-specific monoclonal antibody, bl2. Ten bl2-neutralization signatures sites were identified, including key variable amino acid positions that occur in the bl2-binding surface of gpl20, and positions in the V2 region, known to impact bl2 sensitivity. Other signatures were identified in gpl20 and gp41 that may reflect an impact of quaternary structure on the bl2 epitope. A simple algorithm based on the bl2 signature pattern^' was predictive of bl2

sensitivity/resistance in an additional blinded panel of 57 viruses. As described in the Example that follows, these computational methods were applied to defining signature patterns in Env proteins based on the magnitude and breadth of neutralizing antibody responses in HIV- 1 -infected individuals. An analysis was made of a checkerboard-style neutralization dataset comprising a multi-subtype panel of 25 clonal Env-pseudotyped viruses tested with sera from 69 HIV- 1 -infected individuals from whom a serum gpl60 sequence was derived by single genome amplification. Distinct clusters of sera with high and low neutralization potencies were identified (see Example below). Mutational patterns in six amino acid signature positions in serum Env sequences were strongly associated with either the high or low potency cluster. Five were in the CD4-induced coreceptor binding site of gpl20, suggesting an important role for this region in the elicitation of broadly neutralizing antibody responses against HIV-1.

An Env that retains the full amino acid signature associated with potent antibody responses (see Fig. 7) represents a preferred vaccine antigen. As will be clear from the Example, CH0129.e4 and CH080510.ep2 are strains that retain such signature positions. The gpl 60 and gpl40 sequences (including codon optimized DNA encoding sequences) for CH0219.e4 are set forth in Fig. 13. CH0219.e4 is a particularly preferred vaccine antigen because it has short variable loops.

The present invention thus relates to HIV Envs that retain the signature (preferably, the full amino acid signature) associated with potent antibody responses (e.g., the CH0219.e4 Env) and methods of using same as vaccine immunogens. The invention further relates to such Envs for use as diagnostic targets in diagnostic tests. The invention further relates to the use of wildtype (WT) virus sequences (e.g., CH0219.e4 sequences) in the preparation of a polyvalent HIV-1 vaccine (U.S. Provisional Application No. 61/282,526, filed February 25, 2010). Sequences that can be included in such a polyvalent vaccine for B cell response include env and for T helper and cytotoxic T cell response include gag, pol, nef and tat sequences (U.S. Application No. 1 1/990, 222, filed Aug. 23, 2006). The vaccine antigens (immunogens) of the invention (e.g. Envs sequences that retain the signature associated with potent antibody responses) can be chemically synthesized and purified using methods well known in the art. The immunogens can also be synthesized by well-known recombinant DNA techniques. Nucleic acids encoding the immunogens of the invention can be used as components of, for example, a DNA vaccine wherein the encoding sequence is administered as naked DNA or, for example, a minigene encoding the

immunogen can be present in a viral vector. The encoding sequence can be present, for example, in a replicating or non-replicating adenoviral vector, an adeno-associated virus vector, an attenuated mycobacterium tuberculosis vector, a Bacillus Calmette Guerin (BCG) vector, a vaccinia or Modified Vaccinia Ankara (MVA) vector, another pox virus vector, recombinant polio and other enteric virus vector, Salmonella species bacterial vector, Shigella species bacterial vector, Venezuelean Equine Encephalitis Virus (VEE) vector, a Semliki Forest Virus vector, or a Tobacco Mosaic Virus vector. The encoding sequence, can also be expressed as a DNA plasmid with, for example, an active promoter such as a CMV promoter. Other live vectors can also be used to express the sequences of the invention. Expression of the immunogen of the invention can be induced in a patient's own cells, by introduction into those cells of nucleic acids that encode the immunogen, preferably, using codons and promoters that optimize expression in human cells. Examples of methods of making and using DNA vaccines are disclosed in, for example, U.S. Pat. Nos. 5,580,859, 5,589,466, and 5,703,055.

The invention includes compositions comprising an immunologically effective amount of the immunogen of the invention (e.g., the gpl60 or gpl40 sequence set forth in Fig. 13) or fragment thereof (e.g., gp41 , gpl20, either alone or associated with lipids, or fragments of gpl20), or nucleic acid sequence encoding same, in a pharmaceutically acceptable delivery system. The compositions can be used for prevention and/or treatment of immunodeficiency virus infection 9e.g., in a human). The compositions of the invention can be formulated using adjuvants (e.g., alum, AS021 (from GSK), oligo CpGs, MF59 or Emulsigen), emulsifiers, pharmaceutically-acceptable carriers or other ingredients routinely provided in vaccine compositions. Optimum formulations can be readily designed by one of ordinary skill in the art and can include formulations for immediate release and/or for sustained release, and for induction of systemic immunity and/or induction of localized mucosal immunity (e.g, the formulation can be designed for intranasal administration). The present compositions can be administered by any convenient route including subcutaneous, intranasal, intrarectal, intravaginal, oral, intramuscular, or other parenteral or enteral route, or combinations thereof. The immunogens can be administered in an amount sufficient to induce an immune response, e.g., as a single dose or multiple doses. Optimum immunization schedules can be readily determined by the ordinarily skilled artisan and can vary with the patient, the composition and the effect sought.

Examples of compositions and administration regimens of the invention include consensus or mosaic gag genes and consensus or mosaic nef genes and consensus or mosaic pol genes and consensus Env with an Env that retains the above-described signature or mosaic Env with an Env that retains the above- described signature, expressed as, for example, a DNA prime recombinant Vesicular stomatitis virus boost and a recombinant Env protein boost for antibody, a poxvirus prime such as NYVAC and a protein Env oligomer boost, or fragment thereof, or DNA prime recombinant adenovirus boost and Env protein boost, or, for just antibody induction, only the recombinant envelope gpl20 or gpl40 as a protein in an adjuvant. (See U.S. Application No. 10/572,638, PCT/US2006/032907, U.S. Application nos. 1 1/990,222 and 12/192,015.)

The invention contemplates the direct use of both the immunogen of the invention and/or nucleic acid encoding same and/or the immunogen expressed as a minigene in the vectors indicated above. For example, a minigene encoding the immunogen can be used as a prime and/or boost.

It will be appreciated from a reading of this disclosure that the whole Envelope gene can be used or portions thereof (i.e., as minigenes). In the case of expressed proteins, protein subunits can be used. As pointed out above, the invention also relates to diagnostic targets and diagnostic tests. For example, a signature-retaining Env of the invention can be expressed by transient or stable transfection of mammalian cells (or they can be expressed, for example, as recombinant Vaccinia virus proteins). The protein can be used in ELISA, Luminex bead test, or other diagnostic tests to detect antibodies to the transmitted/founder virus in a biological sample from a patient at the earliest stage of HIV infection.

The present invention also relates to antibodies specific for signature- retaining Envs of the invention, and fragments of such antibodies, and to methods of using same to inhibit infection of cells of a subject by HIV-1. The method comprises administering to the subject (e.g., a human subject) the HIV-1 specific antibody, or fragment thereof, in an amount and under conditions such that the antibody, or fragment thereof, inhibits infection.

In accordance with the invention, the antibodies can be administered prior to contact of the subject or the subject's immune system/cells with HIV-1 or after infection of vulnerable cells. Administration prior to contact or shortly thereafter can maximize inhibition of infection of vulnerable cells of the subject (e.g., T-cells).

As indicated above, either the intact antibody or fragment (e.g., antigen binding fragment) thereof can be used in the method of the present invention.

Exemplary functional fragments (regions) include scFv, Fv, Fab', Fab and F(ab')₂ fragments. Single chain antibodies can also be used. Techniques for preparing suitable fragments and single chain antibodies are well known in the art. (See, for example, USPs 5,855,866; 5,877,289; 5,965,132; 6,093,399; 6,261,535;

6,004,555; 7,417,125 and 7,078,491 and WO 98/45331.)

The antibodies, and fragments thereof, described above can be formulated as a composition (e.g., a pharmaceutical composition). Suitable compositions can comprise the antibody (or antibody fragment) dissolved or dispersed in a pharmaceutically acceptable carrier (e.g., an aqueous medium). The compositions can be sterile and can in an injectable form. The antibodies (and fragments thereof) can also be formulated as a composition appropriate for topical administration to the skin or mucosa. Such compositions can take the form of liquids, ointments, creams, gels, pastes or aerosols. Standard formulation techniques can be used in preparing suitable compositions. The antibodies can be formulated so as to be administered as a post-coital douche or with a condom.

The antibodies and antibody fragments of the invention show their utility for prophylaxis in, for example, the following settings:

i) in the setting of anticipated known exposure to HIV-1 infection, the antibodies described herein (or binding fragments thereof) can be administered prophylactically (e.g., IV or topically) as a microbiocide,

ii) in the setting of known or suspected exposure, such as occurs in the setting of rape victims, or commercial sex workers, or in any sexual transmission with out condom protection, the antibodies described herein (or fragments thereof) can be administered as post-exposure prophylaxis, e.g., IV or topically, and

iii) in the setting of Acute HIV infection (AHI), antibodies described herein (or binding fragments thereof) can be administered as a treatment for AHI to control the initial viral load and preserve the CD4+ T cell pool and prevent CD4+ T cell destruction.

Suitable dose ranges can depend, for example, on the antibody and on the nature of the formulation and route of administration. Optimum doses can be determined by one skilled in the art without undue experimentation. Doses of antibodies in the range of 1 Ong to 20 μg/ml can be suitable.

The present invention also includes nucleic acid sequences encoding the antibodies, or fragments thereof, described herein. The nucleic acid sequences can be present in an expression vector operably linked to a promoter. The invention further relates to isolated cells comprising such a vector and to a method of making the antibodies, or fragments thereof, comprising culturing such cells under conditions such that the nucleic acid sequence is expressed and the antibody, or fragment, is produced.

Certain aspects of the invention can be described in greater detail in the non-limiting Example that follows. (U.S. Application entitled "Methods For The Generation Of Monoclonal Antibodies Derived From Human B Cells", filed April 9, 2010, Atty. Docket 01579-1561, is incorporated herein by reference.)

EXAMPLE

Experimental Details

5 Viruses, serum samples and mAb bl2. All viruses were used as

molecularly cloned Env-pseudotyped viruses that expressed the entire g l60 of the designated strain. The multisubtype panel of viruses used for analysis of bl2 neutralization is described in Tables 7 and 8. The 25 viruses used to assess the neutralizing activity of HIV- 1 -positive serum samples were isolated from sexually0 acquired infections and were sampled early in infection to closely resembled

transmitted/founder viruses. Among these, isolates 6535.3, QH0692.42,

SC422661.8, PVO.4, AC10.0.29 and RHPA4259.7 belong to a recommended panel of subtype B reference strains [110]. Isolates Dul 56.12, Dul72.17, Du422.1 , ZM197M.PB7 and ZM214M.PL15 belong to a recommended panel of5 subtype C reference strains [11 1]. Isolates Q23.17, Q842.dl2, Q168.a2,

Q259.d2.17, Q461.e2 and Q769.d22 are subtype A reference strains [112].

Isolates BB1006-1 1.C3.1601 , BB1054-07.TC4.1499, 700010040.C9.4520 and WEAU-dl 5.410.787 are subtype B clones that were confirmed by single genome amplification (SGA) and sequencing analysis to be true transmitted/early founder o Envs [56], as were C subtype isolates Cel 086_B2, Ce0393_C3, Cel 176_A3 and Ce2010_F5 [1 13]. These latter 25 viruses utilized CCR5 as their major coreceptor and were considered to possess a tier 2 neutralization phenotype [1 14].

Serum samples were obtained from HIV- 1 -infected subjects who were enrolled in clinical protocols of the Center for HIV/AIDS Vaccine Immunology 5 (CHAVI). All subjects were chronically infected at the time of enrollment. The precise length of time of infection was not known. The mAb bl2 was provided by Quality Biologicals, Inc. (Gaithersburg, MD) as a complete IgG molecule.

SGA amplification and sequencing of gpl60 genes. The SGA methods o used here were described previously [1 15] and result in sequences that are not corrupted by recombination during amplification. Viral RNA was prepared from 400 μΐ of patient plasma and eluted into 60 μΐ of elution buffer using EZ1 Virus Mini Kit V2.0 (Qiagen, Valencia, CA). Viral cDNA was prepared with 20 μΐ of vRNA and 80 pmol of primer 1.R3.B3R (5'- ACTACTTGAAGCACTCAAGGCAAGCTTTATTG-3 ') in a 50 μΐ volume using Superscript III (Invitrogen; Carlsbad, CA). SGA of the cDNA was performed using nested PCR to obtain the rev/env cassette and to avoid artificial

recombination and resampling of the viral genomes [1 16]. The cDNA was diluted 1 :3, 1 :9 and 1 :27 (8 reactions per dilution) to determine a dilution with a positive rate of 20% or less. Each diluted cDNA (1 μΐ) was used for the first round amplification with primers 07For7

(5 ' C AAAT A YAAAA ATTC AAAATTTTCGGGTTTATTACAG-3 ') and 2.R3.B6R (5 '-TGAAGCACTCAAGGCAAGCTTTATTGAGGC-3 '). First round PCR was carried out with 1 unit of Platinum Taq Polymerase High Fidelity (Invitrogen; Carlsbad, CA) and 10 pmol of each primer in a 20 μΐ volume. First round PCR products (2 μΐ) were used for a second round of PCR with primers VIF1 (5 ' -GGGTTTATTACAGGGACAGCAG AG-3 ') and Low2c (5'- TGAGGCTTAAGCAGTGGGTTCC-3 '). The second round PCR used 2.5 units of Platinum Taq Polymerase High Fidelity and 20 pmol of each primer in a 50 μΐ volume. PCR thermocycling conditions were as follows for both rounds of PCR: one cycle at 94°C for 2 minutes; 35 cycles of denaturing step at 94°C for 15 seconds, an annealing step at 60°C for 30 seconds, an extension step at 68 °C for 4 minutes, and one cycle at 68°C for 10 minutes. PCR products were visualized on a 1% agarose gel and purified with the QiaQuick PCR Purification kit (Qiagen; Valencia, CA). Sequence analysis of env PCR products was performed on both DNA strands by cycle-sequencing and dye terminator methods using an ABI 3730x1 genetic analyzer (Applied Biosystems; Foster City, CA). Individual overlapping sequence fragments for each env SGA were assembled and edited using the Sequencher program 4.7 (Gene Codes, Ann Arbor, MI). The newly obtained env sequences were aligned with standard sequences for each subtype and circulating recombinant form (CRF) from the LANL database (http://www.hiv.lanl.gov/content/index) using CLUSTAL W [1 17]. Manual adjustment for optimal alignment was performed by using MASE [1 18].

Alignments were used for the initial subtyping analysis using the SIMPLOT software [1 19]. Bootscan analysis was also performed to confirm the breakpoints of identified recombinant sequences using SIMPLOT. All sequences were further validated with RIP and HIV Blast (www.hiv.lanl.gov). Subtyping and

recombination discrepancies between the methods were carefully considered and resolved.

Neutralization assay. Neutralization was measured as reductions in luciferase (Luc) reporter gene expression after a single round of infection with Env-pseudotyped viruses as described [1 10]. Briefly, 200 TCID50 of virus was incubated with serial 3-fold dilutions of test sample in duplicate in a total volume of 150 μΐ for 1 hr at 37°C in 96-well flat-bottom culture plates. Freshly trypsinized TZM-bl cells (10,000 cells in 100 μΐ of growth medium containing 37.5 μg/ml DEAE dextran) were added to each well. One set of control wells received cells plus virus (virus control) and another set received cells only (background control). After a 48-hour incubation, 100 μΐ of cells was transferred to a 96-well black solid plates (Costar) for measurements of luminescence using the Britelite Luminescence Reporter Gene Assay System (PerkinElmer Life Sciences). Neutralization titers are either the 50% inhibitory dilution (ID50, serum samples) or 50% inhibitory concentration (IC50, mAb bl2) at which relative luminescence units (RLU) were reduced by 50% compared to virus control wells after subtraction of background RLUs. Assay stocks of molecularly cloned Env-pseudotyped viruses were prepared by cotransfecting 293T/17 cells with an Env-expressing plasmid and an env-minus backbone plasmid (pSG3Aenv) as described [1 10].

Definitions of neutralization sensitivity. To conduct Env sequence signature analyses with the goal of identifying mutational patterns that correlate with neutralization phenotypes, neutralization phenotypes needed first to be defined. For mAb, bl 2 the Envs were initially defined based on whether or not a 50% reduction in RLU could be achieved at the highest concentration of bl2 used; if not, the Env was considered bl2 resistant. This provided a Boolean neutralization sensitive/resistant phenotype to use as a basis for comparing the 251 Envs tested with bl2. Later, a comparison was made of the levels of neutralization-sensitivity with the patterns in the bl2 signature sites by using IC50 values.

Defining a serological phenotype based on a profile of potency of neutralization against a panel of viruses was more complex. It was first necessary to group H IV- 1 -positive serum samples that exhibited similar neutralization profiles against a panel of 25 viruses. To achieve this, a k-means clustering strategy with added features was used to assess the robustness of the clusters, that factors in the uncertainty that results from limited sampling and inter-assay variability (the impact of experimental noise was explored using a smooth bootstrap). Sampling limitations were explored by re-sampling either by rows or columns 1000 times, using a random-with-replacement bootstrap strategy. The impact of inter-assay variation was explored by a smooth bootstrap, re-sampling from a Gaussian model of noise centered on zero and based on a limited number of repeat data values. Noise was adding back to the original scores based on the model. The k-means clusters were then re-estimated 1000 times with noise added back [120]. Using these two strategies it was found that no more than k=3 distinctive clusters of sera were statistically justified, in that 2 or more sera were assigned to each of the three clusters with 90% confidence. Defining more than k=3 clusters was not justified using this criteria. Sera that were not assigned to a cluster 90% of the time were considered indeterminate; clustering patterns were generally more sensitive to sampling than inter-assay variability. To describe the NAb reactivity pattern of the 3 sera clusters in a Boolean framework (there are two categories, high versus low) for signature pattern analyses, a comparison was made of Envs that were members of each of the robust serological clusters to all other Envs in the study. For example, the Env sequences associated with the strongest sera (cluster III) were compared to the remaining Envs by combining those that were in clusters I and II and those that were poorly resolved. In a second analysis, k was set to k=2 and just the statistically robust high and low clusters were compared, excluding the intermediate values from the comparison.

Computational methods: alignments, phylogenetic and signature analyses. Alignments used for signature analysis were generated with GeneCutter

(www.hiv.lanl.gov) to provide codon-aligned DNA for phylogenetic analysis. Phylogenetically corrected methods were used to identify all signature sites; the contingency table method illustrated in Fig. 1 and Fig. 6 was described in detail in [54]. The reason phylogenetic corrections are critical is that observed patterns in data can result either from correlations imposed by the initial historical emergence of a lineage of viruses (founder effects), or in the case of HIV- 1 , a consequence of recent biological interactions. Not accounting for founder effects can lead to erroneous statistical conclusions [52]. The sequence of the virus depends on its full evolutionary history, while causal correlations are manifest in correlations with recent changes (Fig. 1 and Fig. 6). The separation of the two effects, i.e. a phylogenetic correction, is needed to estimate the impact of recent changes on phenotype, requiring statistical reconstruction the genealogical relationships between the viruses and a maximum likelihood estimate of recent ancestral forms of the viruses. This is implemented through maximum likelihood phylogenies. A large sample size is essential to power explorations of associations between phenotype and mutational patterns. Thus phylogenetic reconstruction becomes technically challenging because the number of possible relationships grows factorially with the number of sequences sampled, where even heuristic searches fail to find reasonable models without extensive computation. To improve the maximum likelihood tree reconstructions, the phylogenic code was adapted to new high performance computing platforms (http://www.lanl.gov/roadrunner/).

Felsenstein first developed the method of phylogenetically independent contrasts [121,122] to address similar problems, i.e., obtaining phylogenetic corrections when looking for correlations of mutational patterns with quantitative data. This method was applied to look at whether variable loop length and the number of PNLGs correlated with potent NAb responses. Because these quantities do not diffuse randomly through the phylogeny, the application of this method is an approximation. Moreover, because hypervariable loop lengths and the number of PNLGs vary rapidly within infected individuals, a phylogenetic correction at the population level is less essential in this framework. Simple Spearman correlation tests were performed to explore these quantitative measures.

Conditional Mutual Analysis (CMI) based Signatures: Conditional mutual information (CMI) was used as a second computational method to identify positions that exhibit an association between mutation and phenotype

(neutralization sensitivity) that is independent of phylogenetic lineage. CMI [123] generalizes the conventional mutual information measure [123] that quantifies the association between two objects, e.g., mutation and phenotype. CMI also quantifies the association between two objects but it conditions the association on a third object, in this case the ancestral state. CMI sums over the associations conditioned on different ancestral states, and so is potentially more sensitive for detecting associations than the contingency table analysis that involves one ancestor state at a time. On the other hand, if the biological signal exists only for some ancestral states and not others, the extra noise added may reduce the power of the test. The statistical significance of a CMI value at any given position was assessed by fixing the ancestral state to each candidate ancestor state in turn, and permuting the relation between mutation and phenotype 1000 times in order to break any potential association. The distribution of CMI values for such permuted data was used to determine p-values, whereas q-values were obtained from these using the method of Storey and Tibshirani [124]. To be inclusive, a cutoff of q < 0.2 was used to identify statistically interesting sites, such that a 20% false positive rate was expected among the identified signatures.

Ensemble Learning Technique Using Classification Trees. To model sequence changes across sites, an ensemble learning technique using classification trees was employed [125]. As with the CMI and contingency table approaches, a sensitive/resistant neutralization category was compared to phylogenetic signals. This neutralization quantity indicates when a virus is neutralized by a fixed amount of bl2 antibody. A change observed between an observed amino acid and the corresponding position in the inferred parent sequence provides one phylogenetic signal. Changes toward or away from each observed or inferred amino acid across all of the envelope protein sequences served as the set of phylogenetic signals. Signals are conditioned on the ancestor amino acid; thus, any given position can be an instance of the signal, not an instance of the signal, or not applicable for the signal.

To form a decision tree, a signal was first identified that best separated sequences into resistant and sensitive neutralization sets. Each set was then partitioned into two more sets using further signals that best track the

neutralization phenotype. This refinement procedure was repeated until no additional signals improved the classification. It was necessary that the classification tree handle the absence of signals as well as their Boolean state in order to avoid phylogenetic artifacts. Prediction was performed by taking a tree and following a main signal, secondary signal, tertiary signal, and so on, according to signal values derived from new data. Even in the absence of mutational signals, a decision tree would still provide a prediction on the basis of whether resistant or sensitive viruses were more common in a training set.

It is conceivable that coordinated mutations or reversions could occur in a universal way across viruses (case 1). Alternatively, the interplay of viruses and hosts could result in different patterns of coordinated sequence change (case 2). To address the possibility that there can be context dependence on unmeasured quantities (i.e., virus behavior groups formed by some unknown process), a subset of the full training data (75%) was randomly sampled when building decision trees, performing 140 interactions of decision tree building with different training set samples. 75% of the data was chosen as a trade-off between statistical power (ability to see any group behavior) and diversity (ability to see several groups). One hundred forty iterations were chosen for computational feasibility.

Evaluation of the performance of the decision tree models needed to be separate from the construction of the training data. Thus, before iterating the training set sampling and tree building, 5 sensitive and 5 resistant viruses were reserved for testing purposes. Good models from the 140 decision tree builds were defined as those models that perform better than 60% (instead of the expectation of 50% for random guesses) on this reserve dataset.

Any one of the 140 training samples and resulting decision trees could represent either case 1 or case 2, as described above. Therefore, the full process of reserving a random test set and generating 140 models to ^'hit' each test set was iterated 32 times. For each test set, on average 10 of the 140 models were obtained that were predictive to at least 60% accuracy. A majority vote of these model predictions was noted for each test set. A "majority vote" was conducted across the 32 test sets to provide the final neutralization prediction. Next, mutational patterns were identified that recurred most often at the top-level splits in the subset of good models across all runs. These provided another strategy for defining amino acid signatures of that correlate with neutralization phenotype (Table 1 ).

Unlike other decision forest or bootstrap aggregation approaches (a.k.a. bagging) [126], cross-validation within the training set was effected and the trees were pruned back before using them. This may limit overall accuracy, but it has the advantage that any decision tree model could be interpreted without overtly over-fitting a particular training data set.

Exploring whether the ability to find b!2-related patterns in the signature data was specific for the bl2 signatures. Positions 655 and 651 exhibit high levels of co-variation with sites in gpl20 that either directly interact with bl 2, or may be important for gp41/gpl20 interactions. To test whether sites 655 and 651 were not being over-interpreted and that it would not be possible to find bl2- related patterns in virtually any random set of covarying sites in Env if a hard enough examination of the literature were made, three positions with comparable Shannon entropy to sites 651 and 655 were examined that were not associated with bl 2 by the analysis. The question then asked was whether the sets of sites that co-varied with these 3 random sites have the potential to offer reasonable hypotheses for the bl2 sensitivity. Unlike 655 and 651 , these covariation sets did not suggest any direct interpretation in terms the bl2 binding surface on gpl20 [35], alanine scanning for sites relevant to bl 2 [63], or the regions implicated in gpl 20-gp41 contact and stability, thereby increasing confidence that the biological interpretations of the covariation results for gp41 positions 655 and 651 are meaningful (data not shown).

Structural mapping of signature positions. For structural mapping in gpl20, three different structures were used. Use was made of a structure with loops modeled when residue positions in loops needed to be shown. In this structure, the core of gpl20 corresponded to the X-ray structure of CD4-bound YU2 gpl20 [127], with variable loops VI V2 and V3 modeled for visualization purpose as described previously [128]. For signature positions in the bl2 binding surface of gpl20, the X-ray structure corresponding to the PDB code 2NY7 [35] was used. Finally, for spatial mapping of the signature positions in the CD4i region, the X-ray structure with a PDB code, 1RZK, [127] that was solved with a CD4-17b complex was used. In one instance a three-dimensional structure of gp41 was used to suggest the possibility of allosteric effects within the gpl20- gp41 complex. This latter gp41 structure was homology-modeled based on the NMR structure of SIV-1 gp41 structure [129]. Signature positions were mapped onto this structure based on the alignment of sequences with respect to HXB2. The positional numbering refers to HXB2. Three-dimensional images were generated using VMD [130].

Validation ofbl2 signatures. A holdout set of 56 pseudotyped Envs, for which the bl2 sensitivity was known but withheld from the analysis team, was kept aside as a blinded test set to determine if it were possible to predict the bl2 phenotype of Env-pseudotyped viruses based on either signature amino acid positions or the ensemble learning strategy. The training and test set of Envs are included in the phylogenetic tree shown in Fig. 1 ; viruses known to be bl2- sensitive are magenta, those known to be bl2-resistant are dark grey, and those used as a blinded test set are light gray. Several strategies to predict phenotype were employed, including the simple requirement of at least 4 sensitive and no more than 1 resistant amino acid in the 7 signature sites, a logistic regression based on the 7 signature sites, and the ensemble learning strategy based on the full Env alignment. A prediction of bl 2 sensitivity or resistance was made based on all three strategies (Tables 3 and 4, Table 8) for each of the 251 original training sequences and 56 test sequences.

Results

Identification of signature sites and mutational patterns associated with bl2 susceptibility. Neutralization data and Env sequences relating to the bl2 epitope that overlaps the CD4 binding site (CD4bs) of gpl20 [35] were analyzed as a means to partially validate the computational methods. The mAb bl2 was chosen for methodological validation purposes because many details regarding its epitope are known, and because it is an epitope of great interest for vaccine design. The analyses utilized genetic sequences and bl2 sensitivities of 251 clonal Env-pseudotyped viruses representing many HIV subtypes, recombinant lineages and disease stages (Fig. 1, Table 7). IC50 values were determined from neutralization curves where the highest dose of bl2 tested was either 25 μg/ml or 50 μg/ml, depending on the experiment. Viruses not neutralized at the highest dose tested are referred to here as being resistant; that is not to say, however, that some of the viruses would not have been neutralized by higher bl2

concentrations. Among the 251 viruses tested, 88 (35%) were sensitive at varying levels (Table 8), and the other 163 were resistant at the highest concentration tested.

First, potential correlates of bl2 sensitivity were examined, including viral genetic subtype, sensitivity to soluble CD4 (sCD4), and the disease stage of the donor at the time of virus isolation. Multiple subtypes were included in the study (Fig. 1). Envs that were B subtype exhibited the highest frequency of bl2 neutralization susceptibility (Fisher's exact test p = 3.6 x 10^"4, comparing B subtype to all others, Fig. 2A). In situations like this, in which there is a strong clade structure in the evolutionary tree and an enrichment of the phenotype of interest in a particular clade, it is critical to employ strategies that include phylogenetic correction to avoid spurious positives when seeking amino acid signatures. In particular, amino acids that are enriched in the B subtype because of lineage effects will have an inherent bias that can make them appear to be associated with bl2 sensitivity.

Ehvs of the target viruses were obtained and sequenced at different stages of infection. The Fiebig stage [56] for most subjects at the time the Env was sampled was experimentally determined as an indicator of stage of infection

(Table 7). When the Fiebig stage was not experimentally determined, the subjects were generally noted to be in a "chronic" or "early/acute" stage at the time the sample was obtained (Table 7). When the subjects were broken into categories of "chronic" (grouping those in Fiebig stages VI or V/VI, with those noted to be in chronic infection) and "early" (grouping Fiebig stages I-V, with those noted to be in acute or early infection) there was no difference between bl2 sensitivity or resistance, nor was there any correlation between bl2 sensitivity and the series of Fiebig stages (data not shown). Thus the results from this cross-sectional examination of bl2 resistance at different stages of infection suggests that the emergence of b 12 resistance over time that was observed in a longitudinal study in a small number of subjects [57] may not be a common pattern. Finally, consistent with previous findings [58], Envs that were susceptible to bl2 neutralization were more sensitive to neutralization by sCD4 (p = 0.00013, Wilcoxon rank sum test, Fig. 2B). Among just the bl2 sensitive viruses, there was a weak correlation between the neutralizing potencies of bl2 and sCD4 (Kendall tau Rank Correlation: p= 0.0015, tau = 0.23, data not shown).

The signature analyses strategies identified ten bl 2 amino acid signatures in Env. Associations with a q value (false positive rate) < 0.2 are presented in Tables 1 and 2. Seven signatures (6 in gpl20 and 1 in gp41) were identified by phylogenetically corrected contingency table analysis [54]. Specific amino acid mutational patterns in each position formed the basis of contingency table analysis; these are noted in Tables 1 and 2. An example of a single amino acid contingency analysis through the maximum likelihood tree, Aspartic Acid (D) at position 185, is illustrated in Fig. 1. The simple uncorrected Fisher's exact p value for this amino acid (p < 10^"8) indicated that a D in position 185 is highly associated with bl2 sensitivity. The low p-values for the patterns of change and stability relative to the most recent ancestral state as estimated through the maximum likelihood tree, showed that mutation away from D in resistant viruses (p = 0.0005), and towards D in sensitive viruses (p= 0.0004) were also associated with bl 2 sensitivity, providing assurance that the profound association with 185D and bl2 sensitivity was not simply an artifact of shared lineages (Fig. 1 , Tables 3 and 4). The low q values (Table 2, q = 0.06 and q = 0.04, respectively) indicate that these low p-values are not expected by chance alone, despite the very large number of tests performed (i.e., every amino acid found in every position in Env, and all combinations of up to three amino acids in every position). An analysis was also made of all potential N-linked glycosylation sites (the amino acid pattern NX[ST]) for associations with bl2 activity. None had a q-value < 0.2, and the only one that showed borderline significance was found at position 149 (noted in Table 2). Finally, the bl2/gpl20 interface was explored more deeply, including all combinations of amino acids in pairs of sites in this region. Single sites accounted for most of the statistically significant signatures (Table 2). (A listing of these sites is included in Table 9).

Of these 7 sites defined by phylogenetically corrected contingency analyses, 5 were also identified as bl2 signatures by an ensemble learning technique using classification trees, while 3 were also identified by conditional mutual information (CMI) analysis (Tables 1). The best predictors from the ensemble learning approach included a subset of the most significant amino acids in the contingency table (Tables 1 and 2). An additional 3 signature sites were uniquely identified by CMI analysis: 2 in gpl20 and 1 in gp41 (Tables 1 and 2). The CMI approach was used with the intent of increasing the sensitivity to capture additional sites of interest. The contingency table analyses restrict each comparison at each site to a particular amino acid or combinations of amino acids in the ancestral state, using a subset of the available data. In contrast, CMI utilizes information across all ancestral states, but does not identify particular amino acids at the site of interest, just the sites that had mutational patterns associated with resistance or susceptibility. An alignment of the three additional sites that were identified by the CMI method is provided in supplement Fig. 9. Each of these positions was relatively conserved; examining these alignments suggests the consensus amino acids 163T, 182V, and 655K are well tolerated among viruses with bl2 sensitivity, but that mutations 163A, 182E and mutations away from 655K, were enriched among resistant viruses.

It is important to remember that while these associations are statistically supported (Tables 1 and 2), any mutation in isolation may not be able to alter the phenotype of a virus in the context of a given natural strain. For example, although a change away from D at position 185 was most significantly associated with bl2 resistance, and was most predictive of the phenotype, the Env carrying the mutation remained bl2 sensitive in 13/48 (27%) natural occurrences of this pattern. Thus, the signatures identified point to the biological relevance of mutational patterns among a population of circulating viruses but are not necessarily predictive in isolation in a single strain. Despite this, the pattern among the signature sites that was evident in their alignment (Fig. 3) was associated with phenotype. For example, higher frequencies of amino acid substitutions associated with a bl2 resistant phenotype, and loss of substitutions associated with a bl2 sensitive phenotype, summed over all 7 signature sites, were strongly associated with resistance. This indicates that effects at the positions identified were cumulative. Notably, the signature sites were identified based on a simple Boolean resistant/sensitive phenotype, yet resistance-associated amino acids accumulated across these sites in viruses with diminishing bl2 sensitivity. Specifically, the left hand box in Fig. 3 includes all bl2 sensitive pseudoviruses tested, and is ordered by diminishing sensitivity. Combinations of more resistant and fewer sensitive amino acids are evident among the least sensitive viruses nearing the end of the columns. This cumulative effect was the basis for a regression analysis used later in an attempt to predict bl2 phenotype among a set of 56 hold out viruses (see below).

Structural and biological interpretation of the bl2 signature sites bl2 contact surface signatures in gp 120. Figure 4A shows the locations of the 8 gp 120 signature sites found in a three-dimensional structure of gpl20 [35, 59-61]. Three bl2 signatures (positions 364, 369 and 461) occurred in (364 and 369) or near (461) the bl2 contact surface of gpl20 [35,58]. These three sites are shown in the context of a bl 2-bound gpl20 structure in Figure 4B. Sites 364 and 369 are located in the CD4 binding loop in the outer domain of gpl20, where both sites directly contact residues in the heavy chain of bl2 in a crystallographic structure of bl2 Fab complexed with a stabilized gpl20 core molecule [35], and mutations at these positions have been shown to alter the bl2 susceptibility of multiple HIV- 1 viruses [58,62,63]. Alanine scanning showed that an N to A substitution at position 461 could diminish bl2 binding affinity more than 10-fold [63]. Because site 461 contacts CD4 and lies adjacent to residues that directly contact bl2 in the gpl20-bl2 crystal structure [35], it may affect epitope exposure.

Wu et al. identified 3 amino acid substitution patterns (S364H, P369L/T/Q and T373M) that were predicted to impact bl2 binding because of potential clashes in side chain rotomers at the bl2 contact surface [58]; two of these were among the signature sites (364 and 369). They showed that an S to H substitution at position 364 substantially increased bl2 binding and neutralization

susceptibility in several natural viruses. In contrast, the relevant substitutions at positions 369 and 373 impacted bl 2 binding to gpl20, but did not restore neutralization to several resistant natural viruses, suggesting the epitope was shielded in the functional Env trimer in these strains [58]. The analyses indicated that a P or S at position 364 was associated with susceptibility, whereas an A or H at this position was associated with resistance. In addition, an A or P at position 369 was associated with susceptibility, whereas an I, L, or Q was associated with resistance (Table 1). The T at position 369 that was predicted by Wu et al. [58] to interfere with binding is rare and was found only once in the data and that single occurrence was in a susceptible virus (Fig. 3). The third site identified by Wu et al., mutation T373M, was not found among the signature sites. In Wu et al., 373M was enriched among subtype B resistant viruses in conjunction with other mutations. In the present study, 34% of the bl2-sensitive viruses overall carried an M at position 373, whereas 28% of the resistant viruses carried an M, and no significant association was found between an M at position 373 and resistance, in fact M was slightly more common among sensitive viruses. There was a trend suggesting mutations away from T at position 373 were more common among resistant viruses (p = 0.057); however, this did not approach significance (q = 1).

V2 region bl2 signatures. Four additional signatures (sites 163, 173, 182 and 185) occur near the C-terminus of the V2 region of gpl20 (Fig. 4A). Some regions of V2 contain frequent insertions and deletions, making them difficult to align, and such regions were not included in the analyses. The signature sites identified in V2 were embedded in parts of the alignment that were conserved enough to be meaningful. Because no X-ray crystal structures of gpl20 are available with an intact V2 loop, the positions on the loop are shown on a modeled loop for visualization (Fig. 4A, see Experimental Details). Based on the crystal structure of the VI /V2 stem, positions near the C-terminal end of the V2 loop are predicted to impact the b 12 epitope [60,61]. Indeed, results from Alanine scanning mutagenesis confirm the critical importance of the V2 region for bl2 binding. For example, a D to A substitution at the signature position 185 was previously found to diminish bl2 binding affinity greater than 10-fold [63].

Moreover, a mutation in this position resulted in escape from bl2 neutralization [62]. It was also found that significantly reduced V2 loop lengths, and a reduced number of potential N-linked glycosylation sites in the V5 loop, were associated with bl2 neutralization (Table 2). A complete scan of the gain or loss of individual PNLGs throughout Env did not reveal an association with any one particular glycosylation site in bl2 binding at the statistical threshold of q < 0.2.

The bl2 signature at site 268. Site 268 is not believed to have been previously investigated for an effect on bl2 binding and neutralizing activity. This site is spatially distant from the interface of bl2 and gpl20, located approximately 30 A away [35] (Fig. 4A). Intriguingly, this signature involved a charge reversal from an acidic residue to a basic residue resulting in a +2 change at this site. Such a change could potentially have a long-range electrostatic effect, thereby impacting bl2 binding, particularly since bl2 is highly positively charged.

Therefore, electrostatic potential calculations were carried out using the Adaptive Poisson-Boltzmann Solver (APBS) to quantify the change in electrostatic contributions to the bl2 binding arising from the substitution of a negative with a positive charge at this position. APBS solves the Poisson-Boltzmann equation, a continuum model for describing electrostatic interactions numerically [64]. The recent X-ray structure of bl 2-bound to the JRFL gpl20 was used for these calculations [35], and the appropriate site-mutations were modeled in the backbone of JRFL. The overall structure was not relaxed and only the side-chain rotomer of the replaced residue was positioned in an energetically feasible position. It was found that a change from 268E to either 268R or K results in an estimated decrease of bl2 binding by 1.4 cal/mol. In Fig. 4C, the isosurface surrounding gpl20 shows the difference in electrostatic potential (+0.3 kT/e) due to the mutation E268R on gpl20; interestingly the isosurface is close to the bl2- gpl20 interface region. This figure also shows that bl2 is highly electropositive (isosurface of +/-1 kT/e) due to the charged nature of bl2 (overall charge of +12), explaining the large decrease in binding energy upon E268R mutation. This is consistent with the phenotypic directionality captured by the signature analysis.

The finding of a b 12 signature at site 268 that underwent a charge reversal prompted an exploration as to whether there are additional acidic residues in gpl20 that could undergo similar charge changes. Obviously not all charged residues are in a position to reverse their charge state to escape immune pressure. Some are highly conserved due to functional constraints. Other acidic residues may take part in critical electrostatic interactions that stabilize the structure. Often charged residues are involved in salt-bridge interactions. In this latter case it is possible that co-varying charge changes could occur simultaneously at the salt bridge forming partners (i.e.: K/R— E/D salt bridge pair becomes E/D— K/R pair); a simple continuum electrostatics model would then predict no significant effect on electrostatic binding energy. To address these possibilities, a systematic examination was made of all of the acidic residues in the gpl20 in the gpl 20-bl2 bound X-ray structure. Details of these sites are provided in Table 10. Except for positions 106 and 268, all other positions had dependencies that prevent a negative to positive change, either due to salt-bridge interactions or sequence conservation. Positions 106 and 268 are the only acidic residues in the gpl20 core that are not conserved and do not take part in a salt bridge interaction. Thus, site 268 provides a rare opportunity for a charge reversal pathway that would allow the virus to become resistant to neutralization by bl2 or other positively charged antibodies.

bl2 signatures in gp41. Two statistically significant signatures were identified in gp41. Both sites (positions 651 and 655) are in the C-heptad repeat that is expected to lie proximal to the N-heptad repeat targeted by the HIV-1 fusion inhibitor T-20 in the post-fusion conformation [65]. The C-heptad repeat also contributes to the formation of a six-hel fix bundle that mediates viral fusion with the cellular membrane [66]. Finding bl 2 signatures in gp41 is not unexpected, as mutations in gp41 are known to affect NAb epitopes in the CD4bs [67-75], including the bl2 epitope [58,68]. These mutations include amino acids at positions 569, 577, 582, 668 and 675 in gp41 that affect CD4bs epitopes; and mutations at positions 569 and 675 affect the bl2 epitope directly [58,68]. While positions 651 and 655 have not been directly implicated in bl2 binding in previous studies, those studies were based on escape mutations in single virus strains (IIIB, MN, JR-CSF, Q461 , Q769, YU-2). In contrast, this study was based on systematically identifying significant associations among 251 genetically diverse viruses. This broader scope of analysis may have led to the identification of sites in gp41 that more generally affect the bl2 epitope among global variants.

To explore the question of how sites in the gp41 C-heptad repeat that are distant from the gpl20-bl2 binding interface could influence the bl2 epitope, an identification was made of all sites within Env that significantly co-vary (hence potentially interact) with positions 655 and 651. To do this, the phylogenetically corrected contingency table approach was used to identify the sites that covaried with signature sites in Envelope. The resulting co-variation patterns for all 10 of the bl2 signature sites, including the two gp41 signature sites, are summarized in Table 11. Position 655 was found to significantly co-vary with a single position, site 185, which was also the most significant signature site in gpl20. As noted above, this site is located in the V2 region of gpl20 and has been shown to be a critical residue for bl2 binding affinity [63]. Thus, the association between mutational patterns in position 655 and bl2 neutralization could be a consequence of quaternary structural interactions, giving rise directly to the correlation between mutational patterns of position 655 and bl2 sensitivity. Alternatively, the 185-655 interactions could be driven by a relationship that is independent of the bl 2 epitope. In this latter case, the statistical association between site 655 and bl2 neutralization may be due to a correlation that is one step removed, i.e. an ancillary consequence of the direct interactions of site 185 and bl2. 655K is the most common amino acid in this position, where both K and E appear to be associated with bl2 neutralization sensitivity in the signature analysis. As an aside, O'Rouke et al. [76] studied in detail the impact of substitutions on neutralization in a site they call 655, but because they did not use standard HXB2 numbering, their site 655 is actually 653 in HXB2 and is not the signature site identified here.

Covariation patterns were more complex for site 651 , which was found to have 9 covarying sites (Table 1 1 ), 4 of which are captured in a schematic molecular diagram in Figure 4D. Site 80 and site 169 are in a region of the V2 loop for which no crystal structure is available and therefore were excluded from gpl20 in this diagram. Similarly, 3 sites were in the cytoplasmic tail and thus were not included here (sites 798, 817, and 822). Based on crystallographic data, covarying sites 429 and 432 (though not statistically supported bl2 signatures) are spatially close to the CD4 binding loop in a region that contacts bl2 [35]. A K432A substitution diminished bl2 binding affinity > 10-fold [63]. The presence of this complex chain of covarying sites in gp41 and gpl20 suggests allosteric effects, where site 651 is part of a set of spatially distant residues that modulate the gpl20-gp41 interface and thereby influence the exposure of the bl2 epitope in the quaternary configuration of Env. Receptor and coreceptor binding induce structural re-arrangements at the gpl20-gp41 interface as a requisite step for membrane fusion [18,20]. In principle, genetic changes that influence the gpl20- gp41 contact surface could have reciprocal allosteric effects on the CD4bs of gpl20. Consistent with this hypothesis, two of the 651 covarying sites (position 84 in the N-terminal CI region of gpl20; position 602 in the gp41 disulfide loop) occur in regions implicated directly in gpl20-gp41 contact and stability [77-84] (Fig. 4D). Alternatively, the mutations in site 651 that correlate with bl2 susceptibility might influence a different allosteric pathway that relies on quaternary interactions with the CD4 binding loop region (sites 429 and 432) or possibly V2 (site 169) in the context of a trimer.

Signature-based predictions ofbl2 neutralization. Three computational approaches (described above) were used to determine if it were possible to predict bl 2 neutralization phenotypes based on sequence information. Prediction strategies were developing based on the "training" set of 251 sequences used to define the original signature pattern. The three strategies were tested by predicting the bl2 phenotype of a blinded set of 56 pseudotyped Envs sequences. The first strategy applied a simple rule based on inspection of the alignment of the seven signature sites with defined amino acids shown in Figure 3. If the sequences contained at least 4 "sensitive" amino acids, and no more than 1 resistant amino acid in these seven sites, it was classified as sensitive. In the second approach, logistic regression was used to formalize the contribution of change at each site in an attempt to refine the predictive ability of the signature. The third approach was to apply an ensemble learning technique using classification trees to the amino acid changes in the full alignment, with the thought that this method could be used both for prediction of bl2 phenotype based on the full Env sequence, and for defining the particular signature positions and amino acids which contributed most to the bl2 phenotype (Table 1). When applying the three methods to the original training set 251 viruses, it was found that the simple rule based on the alignment was less predictive than the logistic regression, and the ensemble learning method was the most predictive (Table 3). When the three methods were applied to the blinded test set, the order reversed, and in this case the first simple method was the most predictive (p-value = 0.007, Table 4). The predictive power of this simple signature based strategy further supports the relevance of the bl2 signature sites and amino acids associations. The other two methods had higher rates of false negatives and were not significantly predictive (Table 4). Reason for this inadequate power are not clear but could be due to differences in the sampling of the 251 viruses and the 56 viruses that limited the predictive power of the two computational prediction methods. The full set of predictions based on the three methods and the bl2 experimental data are provided in Table 8.

As discussed previously, the signature sites were originally defined based on a simple classification of bl2 sensitive or resistant phenotype. Thus, as seen in the left hand panel in Figure 3, the cumulative number of sensitive amino acids in the 7 positions tends to decrease as bl2 sensitivity diminishes (green amino acids and agreement with the most common sensitive form), whereas resistant amino acids tend to accumulate (red amino acids). To formally test whether level of bl2 sensitivity among the sensitive viruses was correlated with the signature pattern, the signature pattern was first reduced to a single sensitivity score. This was done by subtracting the number of resistant amino acids from sensitive amino acids (red from green, in Figure 3). The signature sensitivity score was correlated with bl2 sensitivity (p=0.0006, Spearman's rho = -034, Fig. 10). Thus signature amino acids can be used to predict, with significant accuracy, both the initial sensitive and resistant classification and the level of sensitivity among bl2 sensitive viruses. Because these sites were identified after correcting for founder effects in the training set, it can be assumed that the the correlation observed is causal.

Signature analysis of Envs that elicit potent NAb responses in HIV- 1 -infected individuals.

Clustering sera according to cross-reactivity and potency. A

determination was next made as to whether the signature analyses methods could be used to identify amino acids that associate with broadly cross-reactive NAb responses in HIV- 1 -infected individuals based on heatmap clusters (Fig.5). Env sequences and neutralizing activities in sera from 69 chronically infected individuals were used for analyses. The serum samples were obtained from individuals in the United States, Malawi, South Africa, Tanzania and England and consisted of, 1 CF recombinant, 1 CRF01 AE, 1 A/G recombinant, 5 subtype A, 24 subtype B, and 37 subtype C HIV-1 infections (Fig. 6, and Table 12). These 69 serum samples were chosen from among 360 sera that were assayed against a panel of twelve viruses (6535.3, QH0692.42, SC422661.8, PV0.4, ACIO.0.29, RHPA4259.7, Dul 56.12, Dul 72.17, Du422.1 , ZM197M.PB7, ZM214 .PL15, CAP45.2.00.G3). The 69 selected samples represented a wide spectrum of neutralization potencies against these 12 viruses. For increased statistical power in terms of robust assignments of potent versus weakly cross-neutralizing sera, they were assayed against an additional multi-subtype panel of viruses, such that the total number of pseudoviruses assayed was 25 (6 subtype A, 10 subtype B, 8 C and 1 BC recombinant, all isolated early in infection, see Table 13). The final checkerboard-style results (Fig. 5) confirmed a wide spectrum of neutralization potencies, including a subset of samples that contained high titers of NAbs against a majority of viruses tested, and for contrast, a subset of comparable size that was poorly cross-neutralizing.

The combined neutralization results were clustered according to the ability of individual serum samples to neutralize the panel of 25 viruses, using a k-means strategy that factors in the robustness of the clusters according to the uncertainty that results from limiting sampling (bootstrap) and assay-to-assay variability (noise) (Fig. 5). To assess the impact of assay variability, error estimates were factored in based on a limited number of repeat experiments. To do this, error (drawn from a log-normal distribution based on the repeat data) was added to the real data, and created 1000 reconstructed data sets. This made it possible to resolve clusters that should be robust relative to inter-assay variation (Fig. 5, noise). Next, a re-sampling was made from among the 25 Envs used in the neutralization assays 1000 times to see if the clusters would be robust if a different test panel of Envs with similar, but less diverse, composition had been selected. Figure 5A shows 3 distinct clusters (k=3) that turned out to include sera with high, medium and low neutralization potencies, respectively. k=3 was the maximum number of clusters that could be meaningfully assigned, given the constraint that each cluster must contain at least 2 members, and that the members must meet the stability criteria of being associated with the assigned cluster in >90% of each of the two re-samplings experiments (i.e., "bootstrap" and "noise"). Standard k-means strategy was used to assign each serum to a single k-cluster; however, if based on re-sampling statistics described above, some sera could not be assigned to any of the k=3 clusters, they are shown as intermediate values. To make use of all 69 data points in a Boolean framework for signature analysis, including these intermediate values, three sets of signature analyses were conducted for the k=3 clusters, comparing each one of the 3 robust clusters to all other data points. A k=2 clustering was also performed that enabled a robust extreme "high" and "low" 2-cluster comparison that captured most of the data (Figure 5B). This latter signature analysis did not resolve new signature site, but did sometimes improve the statistical confidence in a given site (Table 6).

Phylogenetically corrected methods similar to those described for the bl2 sensitivity signatures were used to identify associations between serum Env sequences and distinct neutralization clusters.

Defining signature patterns in serum-derived Envs. Envs sequences from all 69 sera were scanned for patterns of mutations that correlated with particularly weak or strong neutralizing capacities. The analysis compared all single sites and all pairs of adjacent sites for signatures of either 1 amino acid or combinations of amino acids at each site. In this complete Env scan, a single signature was found in the CoRbs. This signature consisted of a pair of amino acids in which the combination of either G or S at position 412, together with N at 413, was found to be enriched in Envs from potent neutralizing sera. An examination was made for signatures in potential N-linked glycosylation sites (PNLGs) throughout Env and a single signature pattern with borderline significance was again found that was also located at position 413 in the CoRbs; in this case the PNLG was preserved in Envs from individuals with potent sera. Using the CMI approach to scan the full Env protein, an additional signature was identified at position 186 in the V2 loop.

A more in-depth exploration of regions in the receptor and coreceptor binding sites of gpl20 and in the MPER of gp41 was next performed. The sets of positions used for these analyses, and the references from which they were drawn, are listed in Table 9. These three regions were selected because antibodies against each one have each been identified in a subset of HIV infected people who possess potent cross-reactive NAb responses [85-88]. An examination was made of combinations of multiple amino acids at multiple positions in these regions of interest. This sort of in-depth exploration was neither computationally feasible with the full Env, nor was it desirable because multiple test issues would have limited the power to find weak signatures if the full Env was explored so intensively. The deeper focused analysis revealed additional signatures but only in the CoRbs (Tables 5 and 6). No correlations were found in either the CD4bs or MPER region even through these regions were also targeted for a more focused and in-depth analyses. Finally, as with bl 2 neutralization, an examination was made as to whether potent NAb responses were associated with other general features of Env that are known to affect epitope exposure, such as the extent of N- linked glycosylation and the size of the variable regions of gpl20 [89-92]. The V2 loop was shorter with fewer PNLGs in Envs from subjects with potent sera, and Envs with shorter V5 loops [93] were also correlated with potent sera. The mutational patterns in all of the signature sites are highlighted in sub-region alignments in Fig. 7, ordered and colored according to the k=3 heatmap clustering scheme shown in Fig. 5A. An Env such as CH0219.e4 might be particularly promising as a vaccine antigen, because it retains the full amino acid signature associated with potent antibody responses (Fig.7), and it also has short variable loops (Figs. 13 and 14).

Structural and biological interpretation of signature sites that correlated with potent NAb sera

The combined results of the contingency table signature analyses identified five statistically significant signature sites that resided in, or proximal to, the CCR5 CoRbs of gpl20 (Tables 5 and 6). These sites are shown in a crystallographic model of gpl20 complexed with CD4 and the CD4i-specific mAb 17b in Figure 8. Sites 419 and 421 are located in the V4 region of gpl20, immediately adjacent to the β20 strand of the bridging sheet that connects the inner and outer domains of gpl20 [35,60]. Both sites make contact with the CD4i-specific mAb 17b [60] (Fig. 8) and have been shown to be critical for CCR5 co-receptor binding [94-97]. Site 419 also makes contact with bl2 [35], whereas site 421 is involved in the binding of other CD4i-specific mAbs E51 [97] and 48d [98] as well. Sites 413 and 440 in V4 and C5, respectively, are spatially close to the bridging sheet and overlap the contact surface for 17b [60]. Site 440 has been shown to be critical for CCR5 binding [95-97]. CMI analysis identified an additional site in the V2 loop, position 186, immediately adjacent to the bl2 signature site at position 185. In addition to the position-based signature analysis, it was found that strong NAb responses were associated with serum Env proteins that had fewer PNLGs and shorter lengths in V2 (Table 6). It has been shown that V 2 stem region can impact CCR5 binding since it plays a significant role in formation of the bridging sheet [95,96]. Furthermore, site-directed mutational studies have shown that regions outside V3 loop, including site 166 (a position within V2 loop) can play a significant role in co-receptor usage/switch [93,99]. Considering the flexibility of the loop and ensuing conformational changes that take place involving VI /V2 upon CD4 binding, a position such as 186 can directly or indirectly interact with critical sites involve in the formation of bridging sheet. The fact that no other signatures were identified suggests that the CCR5 CoRbs plays a substantial and relatively consistent role in the NAb response in HIV- 1 -infected individuals.

In summary, assay technologies that utilize molecularly cloned Env- pseudotyped viruses with a defined sequence are powerful tools for dissecting molecular determinants of neutralization epitopes on HIV-1. In addition to enabling mutagenesis studies, data from assays with clonal Env-pseudotyped viruses have been used for computational analysis to identify Env amino acid signatures that associate with the antigenic recognition patterns of autologous [53] and heterologous [52] NAbs in sera from HIV-1 -infected individuals. Although not confirmed in previous studies, such signatures could be contact sites for NAbs, or they may be determinants of epitope exposure in the quaternary structure of Env spikes. Here, partial validation was obtained of a computational strategy to accurately identifying amino acid positions that are related to NAb phenotypes. Patterns of mutations in Env proteins that correlate with bl2 susceptibility were systematically studied and key positions that are known from crystallographic and mutagenesis studies to be critical sites in the bl2 epitope were successfully identified. These sites were predictive of bl2 susceptibility. In addition to this validation of the approach, new information was gained by defining the particular mutations in the natural virus population that most profoundly impact bl2 neutralization susceptibility, and by determining the relative strength of such contributions (Table 2). Thus, 7/8 gpl20 signatures were identified either directly in the contact surface for bl2, or in V2, which is known to impact bl2 binding (Tablel). Notably, mutations in position 185 in V2 were nearly equal in strength to mutations in position 461, which are the two best predictors for assessing bl2 neutralization susceptibility in natural strains. A new position, 268, was implicated in bl2 binding. This signature raised a plausible hypothesis regarding the impact of electrostatic potential at the isosurface of gpl20 on interactions with the positively charged bl2 antibody. Two additional bl2 signatures were identified in gp41 that were intriguing because they may affect exposure of the bl2 epitope in the quaternary structure of Env.

Interestingly, both were directly co-varying with sites at the bl2-gpl20 interface. Two of the ten sites identified are statistically expected to be false positives, so it is likely that two will be not be found to be relevant when experimentally tested, although each of the ten are biologically plausible.

The apparent accuracy of the bl2 susceptibility signature analysis was encouraging; however, the findings highlight both limitations and virtues of these methods. Sequence-based signatures methods cannot be expected to identify all bl2 contact residues in gpl20 [35]; this is because some of these sites are highly conserved, whereas other sites at the contact interface may have natural variation that is well tolerated by bl2. Yet other sites might reside in variable regions that cannot be aligned with confidence. In addition, since these methods start with no biological priors, they necessarily need a large number of tests that makes detecting weak signatures prohibitively data intensive. For example, two PNLGs known to affect bl 2 susceptibility [58] were not identified. One of these sites was at the base the V2 loop (position 197) and the other was in the V3 loop (position 301). The PNLG in position 197 is almost invariant, and so could not have been identified by this method, which relies on sequence variability. Position 301 (PNLG) reached borderline significance in the complete scan of Env when testing for an association between the preservation or loss of PNLGs and the bl2 neutralization (p = 0.019, q = 0.30, OR = 0.23).

Signature methods focus on sites that are most impacted by common mutational patterns found in the circulating population. Such mutational patterns are directly relevant for vaccine design considerations because it is necessary to contend with natural variation for a vaccine to succeed. Indeed, signature methods provide a useful counterpoint to crystallography, which identifies the contact surface of a protein bound by antibody, but does not provide direct information about the implications of key common natural mutations [35].

Moreover, alanine scanning [63], which explores the functional impact of mutations introduced in either conserved or variable positions, is a valuable tool, but one that is limited in terms of being able to look at the consequences of natural variation at specific sites or in combinations of sites. An additional limitation is experimental, in that some sites might require concentrations of bl2 that are higher than those used here for positive identification. Despite these limitations, the computational analysis appears useful for delineating the molecular determinants of complex neutralization epitopes on HIV-1 Env, including the identification distant sites that may impact bl2 binding though quaternary and allosteric effects. The neutralizing impact of bl2 is very specific, where slight differences in recognition sites between viruses can have major phenotypic consequences [100]. A better understanding of the impact of common natural mutations that are outside of the immediate binding surface of bl 2 may ultimately allow improved rational design strategies of vaccines that attempt to elicit potent anti-CD4bs antibodies.

Having confirmed that the computational analysis has utility for identifying molecular determinants of Env antigenicity in the context of the bl2 epitope, an effort was made to determine whether a similar computational analysis, based on Env sequences in serum samples from HIV-1 -infected individuals, could identify amino acid signatures that associate with the magnitude and breadth of the neutralizing activity of the serum samples. Any signatures identified by this analysis might be determinants of the immunogenic as well as antigenic properties of Env, although it was beyond the scope of this study to discriminate between these two immunologic properties.

For the analyses of Env sequences in serum samples that were evaluated for neutralizing activity, a single Env sequence from each individual was obtained. There was interest in leveraging the resources to increase the number of individuals studied rather than increasing the depth of characterization of infected individuals. In part a test was being made of the feasibility of the approach for scanning a large population of HIV infected individuals with the intent of finding common features of the virus harbored in them that may have given rise to a potent NAb response. Viral evolution and quasispecies complexity in chronically infected subjects clearly were potential confounding factors; the single sequence used was randomly selected from a complex viral population within each individual and may not reflect the form of the Env that gave rise to the NAbs of interest in the serum samples. Indeed, assuming that the NAb response during chronic infection is driven by multiple viral variants, these confounding factors limit the ability to identify genetic signatures. Despite this, statistically significant signatures were revealed based on an analysis of sequences from a single Env clone from a single time point from each of 69 individuals. Notably, these signatures were focused on a single biologically interesting region, the CoRbs. An unresolved issue that is an inherent consequence of this signature- defining strategy is the uncertainty regarding whether the signature amino acids reflect common features that were useful for stimulating potent NAb responses, or if instead they reflect common patterns of escape from the NAb responses in the potent sera. Experimental comparisons to resolve this are underway; strains that retain the signature positions that are associated with potent sera, like CH0219.e4 and CH080510.e.p2 (Fig. 7), are particularly interesting candidates for immunogenicity testing. The fact that five of the six signature sites identified, with one false- positive expected, were in the CoRbs of gpl20 suggests an important role for this region in generating high titers of broadly NAb responses. This region is comprised of elements of the bridging sheet and adjacent surfaces from the outer domain of gpl20, including the V3 loop, that undergo conformational changes and become exposed upon CD4 binding as an intermediate step in the membrane fusion process [60,95,96,101 -103]. It is possible that in some cases CD4i-specific mAbs contribute directly to potent cross-neutralizing ability [94,86]. The CoRbs is one of the most highly conserved and protected domains on gpl20 [85]. Rare variants of HIV- 1 exist that exhibit spontaneous exposure of CD4i epitopes; these strains tend to infect cells independently of CD4 and to be highly sensitivity to neutralization by CoR-specific antibodies [104,105]. Owing to the presence of such antibodies in HIV- 1 -infected individuals [85,86,94], a mechanism of CD4- induced exposure of the CoRbs serves as an effective strategy to evade humoral immunity— a strategy that is aided by steric constraints that prevent anti-CoR antibodies from gaining accessing to their epitopes at the virus-cell interface [106]. In a systematic thermodynamic analysis by Kwong et al., in which 20 antibodies were categorized according to where they bind on the gpl20 surface, it was found that 6 of 7 antibodies that bind gpl20 at its receptor and coreceptor binding sites exhibited unusually high binding entropy (including 17b that binds to CoRbs) [21 ]. Therefore, the signature sites identified here in the CoRbs might play an indirect role in neutralization by antibodies that induce large

conformational changes in gpl20.

The question naturally arises as to why a region of gpl20 that is so heavily guarded and difficult to target by NAbs registered in the analysis as a key determinant of potent NAb responses in HIV- 1 -infected individuals. One possibility is that the CoRbs of gpl20 has vulnerabilities that are only beginning to be recognized. For example, using a novel combination of epitope mapping techniques, Li et al. [94] reported evidence that CoRbs-specific antibodies contributed to the broadly cross-reactive neutralizing activity of serum from two HIV-1 infected individuals. In addition, CoRbs residues were implicated by alanine scanning mutagenesis as being involved to a minor extent in the epitopes for two newly described broadly neutralizing mAbs [50]. Also, vaccine-elicited CoRbs-specific antibodies correlated with viremia control in a simian-human immunodeficiency virus (SHIV) challenge model in nonhuman primates [ί07]. It also seems possible that amino acid residues in key positions in the CoRbs of gpl20 modulate the conformation of adjacent regions, such as the CD4bs, much the same as conformational changes induced by gpl20-CD4 binding modulate the CoRbs. Limited sequence variability in the CD4bs [108,109] makes this an attractive target for NAb-based vaccines. Indeed, studies have shown that the CD4bs is targeted by broadly NAbs in sera from some HIV- 1 -infected individuals [51].

It remains to be determined whether the genetic signatures of potent NAb responses identified here contribute to the immunogenicity as well as antigenicity of Env. By design, an attempt is being made to resolve signatures that impacted Env immunogenicity in natural infection. Clearly, strong antigenicity alone is generally not sufficient for the elicitation of NAbs [28-30, 36-39]. Other requirements may need to be met before B cells can be stimulated to produce NAbs against certain epitopes of interest. Although very little is known about what these requirements might be, proper Env configuration for B cell recognition and antibody affinity maturation should be considered. It will be interesting to test novel Env immunogens that naturally contain the genetic signatures identified in the study, or that introduce these signatures experimentally. At the very least, these findings suggest that greater attention should be paid to the CoRbs of gpl20 when designing novel vaccine immunogens.

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TABLES

Table 1. Sites identified as signatures of bl2 sensitivity using any of the three signature-defining approaches: contingency table, CMI, and ensemble machine learnin method.

'The CMI approach does not provide specific information regarding which amino acids give rise to the signal, although particularly distinctive substitutions can be seen by examining the data (Fig. 9). A "Yes" in the CMI column means the site was associated with bl2 sensitivity or resistance.

²The Fisher's exact contingency table is based on specific amino acids or sets of amino acids, such that the amino acids associated with signature sites are explicit; and amino acids associated with bl 2 resistance are underlined, whereas amino acids associated with bl 2 susceptibility are not underlined.

The recurrent top splits in the decision trees (the number in parentheses indicates how many times it was found) provide information about the key signature amino acid substitutions. The exclamation point (!) means "not" in these tables and figures, thus E— !E means that "E" is found in the immediate ancestral state of the sequence, and is "not E" in the sequence.

Table 2. Summary of statistics of signature sites of bl2 sensitivity.

ΉΧΒ2 position refers to the amino acid position of interest in the HXB2 reference strain (www.hiv.lanl.gov: Locator tool). ²Amino acid refers to the particular amino acid or combination of amino acids that was statistically related to bl2 resistance (underlined) or sensitivity (not underlined). An exclamation point means "not"; thus in the first line, when T is an ancestral state, Y mutates to "not Y" (!Y) with a statistically higher frequency in bl2 resistant strains than sensitive strains.

Statistic is the statistic that was used to identify the signature, by either the phylogenetically corrected contingency approach (Fisher exact test) employed as described in [54]; the conditional mutual information approach (CMI); or a comparison of all variable region loop lengths (length) and number of glycosylation sites (sequons with amino acid pattern Nx[ST]) with the bl2 neutralization values using a Spearman rank correlation test.

⁴The p-values, the q-values (false positive rates), and the odds ratios are provided. The Fisher's exact test q-values were calculated for discrete tests as implemented in [54]. For the CMI analyses, p-values were acquired by shuffling phenotypes and counting the relative frequency at which random CMIs exceeded the original CMI. The q-values were calculated using the method of [124], after stripping off the highest p values (essentially a few hundred of p-value = 1). Only associations with a q-value < 0.2 are shown. ⁵Rows and columns of the 2x2 contingency table. As an example of how to read these, in position 173, rlcl refers to row 1 column 1 and is the number of times among bl 2 sensitive viruses that Y— >!Y mutates to another amino acid (change). rlc2 refers to row 1 column 2, and it is the number of times among sensitive viruses that the ancestral state was Y and it stayed Y (stable) in the Env sequence.

⁶Strength is a measure that expresses how predictive a given signature amino acid is of the bl2 sensitive/resistant phenotype, essentially an augmented odds ratio, where each count was augmented by 1 pseudo-count to avoid issues with zeros and infinities, and strength = (rlcl+l)(r2c2+l)/(rlc2+l)(r2cl+l).

⁷Several explorations of the Env alignment were used, and this is described in the "test". In the first screen, every amino acid found in every column was tested (laa). Then combinations of 2 or more amino acids in every column were tested (>laa). Then positions known to be key for the bl2 binding site (Table 9) were specifically tested for all combinations of amino acids over all pairs of positions in the binding site (bl2). Although pairs of positions were tested, single positions essentially accounted for the signal in that analysis. Only these single site associations are shown.

⁸Some lines are shown in bold. In these lines, the change in the amino acid is associated with a reverse in the majority of cases found among sensitive or resistant viruses; thus the change in theses sites is particularly predictive of NAb phenotype.

⁹A11 PNLG sites in Env were tested for phylogenetically corrected association with bl2 sensitivity using the contingency table approach. None reached significance with a q-value of < 0.2; the glycosylation site at position 149 was the only one to reach even borderline significance and is included here for completeness.

¹⁰For the initial analysis of loop length and number of PNLGs in each loop, a phylogenetically corrected method was not used. Rather, a non-parametric Spearman's correlation test was used comparing loop length with the geometric mean 50% neutralization titer for the 25 Envs. It is reasonable to forego the phylogenetic correction in these cases because the loop lengths vary by insertion and deletion and often change dramatically within infected individuals. These parameters are less likely to be biased by phylogeny at the population level.

Table 3. Prediction strategies for bl2 sensitivity applied to the 251 pseudotyped Envs included in the signature-defining training set

Table 4. Prediction strategies for bl2 sensitivity applied to the 56 pseudotyped Envs included in the blinded test set

Table 5. Sites identified as Env signatures associated with serum neutralizing breadth and potency using the tree corrected contingency table and CMI approaches. This table is organized similarly to Table 1.

All regions are in gpl20. CoRbs, coreceptor binding site; V2, second variable region.

²Arrows are used to show the direction of the sequence change that was significant. Thus, in the three amino acids at positions 419-421, the sequence was moving from R, any amino acid, (R_K) to a sequence that was not R, any amino acid. K (!R_K) in weakly neutralizing sera. [GS]N, means either G or S at position 412 and N at 413.

Table 6. Summary of statistics of signature sites of associated with serum neutralizing breadth and potency. This table is organized similarly to Table 2.

HXB2 Amino Acid Statistic p-value q-value Odds Counts Strength Test² Position ratio

rlcl rlc2 R2cl r2c2

Sensitive Sensitive Resistant Resistant

Change Stable Change Stable

412/413 !|GS]N→[GS]N Fisher 2.1 x 10 * 0.0015 Infinity 6 0 57 81.2 2 sites, Full Env

2 deep, k=3, high vs others 413 Nx[ST|→!Nx[ST| Fisher 0.0083 0.23 10.17 10 43 8 PNLGs, Full En k=3, high vs other

419_421 R K→!R K Fisher 0.0025 0.089 9.2 13 17 25 6.7407 3 sites, 2 deep

CoRbs k= 3, low vs others

419_421 R K→!R K Fisher 0.0013 8.1 13 17 33 6.6111 3 sites, 2 deep

CoRbs, k= 2 low vs high

419 R→!R Fisher 0.044 0.1 1 5.2 9 21 25 3.9394 1 site, CoRbs, k= low vs others 419 ! - Fisher 0.044 0.1 1 5.2 9 21 25 3.9394 1 site,

CoRbs, k= 3, low vs others

440 Q→]Q Fisher 0.018 0.1 1 0 0 0 0.0417 3 sites,

CoRbs, k= 3, low vs others

440 Q→!Q Fisher 0.012 0.13 Infinity 3 6 3 sites,

CoRbs, k= 2, high vs low

186 NA CMI < 0.001 <0.001

V2 Shorter length Spearman 0.043 0.14

V2 Fewer PNLGs Spearman 0.017 0.043

V2 Fewer PNLGs 'Contrasts 0.06 0.06

V5 Shorter length 'Contrasts 0.02 0.02

Variable loop lengths and the number of glycosylations sites in each variable loop were compared as in Table IB, using a simple Speannan's rho test.. These results were validated using a phylogenetically corrected method, phylogenetic contrasts [121 ,122].

²ln this column the number of "sites" refers to the number of sites considered in combination in each test, the number "deep" refers to how amino acids at a single site were combined in each test, k = 2 or 3 refers to the k-means clusters as illustrated in Fig. 5. When k = 3, the test could either be the lowest or the highest neutralization potency cluster versus all others. When k= 2, only the high and th low clusters were compared, excluding indeterminate values. Full Env means the complete Env was scanned in the test. CoRbs mean the signature was defined in the in-depth scan of the CoRbs. No significant signatures were found in comparable in-depth scans of the CD4bs and the MPER regions.

Name Clade Country of Year Fiebig Stage Mode of Specimen Accession ARRRP origin transmission Source number cat *

Q23J7. A Kenya VI -F AF004885 10455

Q259_d2J7_ A Kenya Acute/early M-F AF407152 10459

Q769_d22_ A Kenya Acute/earty M-F AF407158 10458

Q842_d12_ A Kenya Acute/early M-F AF407160 10457 S208_A1 A Montserrat VI DQ187010

3 15_v1_c1 A Tanzania 2003 VI Hetero Plasma submitted

0260.v5_d A Tanzania 2005 VI Hetero Plasma submitted

0330_v4_c3 A Tanzania 2005 VI Hetero Plasma submitted

3365_v2_c20 A Tanzania 2004 WVI Hetero Plasma submitted

783_v0_c51 A Tanzania 2002 l or II Hetro Plasma submitted

3718_v3_c11 A Tanzania 2004 l/ll Hetero Plasma submitted

398_F1_F6_20 A Tanzania ND Hetero Plasma submitted

191955_A11 A Uganda 2007 IV heterosexual plasma submitted

9004SS_A3_4 A Uganda 2007 IV Hetero Plasma submitted

T280.5 A/CRF02_AG Cameroon Chronic ccPBMC EU513183

Q461_e2_ AD Kenya Acute/early M-F AF407156 10460

0907_v4_c12 AD Tanzania 2005 WVI Hetero Plasma submitted .

3468.v1.c12 AD Tanzania 2003 VI Hetero Plasma submitted

191084_B7_19 A1 Uganda 2007 IV Hetero Plasma submitted

3301_v2_c6 AC Tanzania 2004 early Hetro Plasma submitted

3301_v1_c24 AC Tanzania 2003 l/ll Hetero Plasma submitted

3589_v1_c4 AC Tanzania 2003 VI Hetero Plasma submitted

6540_v4_c1 ' AC Tanzania 2004 early Hetro Plasma submitted

6041_v3_c23 AC Tanzania 2004 l or II Hetro Plasma submitted

6545_v3_c13 ' AC Tanzania 2004 WVI Hetero Plasma submitted

6545_v4_c1 * AC Tanzania 2005 early Hetro Plasma submitted

477_F3_13_55 AC Tanzania VI Hetero Plasma submitted

246_F3.C10_2 AC Tanzania VI Hetero Plasma submitted

6095.v1.c10 ACD Tanzania 2003 WVI Hetero Plasma submitted

0815.v3.c3 ACD Tanzania 2004 WVI Hetero Plasma submitted

3103_v3_c10 ACD Tanzania 2004 VI Hetero Plasma submitted

270015. J5.1 AD Uganda 2006 VI Hetero Plasma submitted

192018_B1_9 AD Uganda 2007 IV Hetero Plasma submitted

193003.B10 AD Uganda 2007 VI heterosexual plasma submitted

3233.p12 AG Senegal 2001 VI Plasma submitted

1105j)17_1 AG Senegal 1999 VI Plasma submitted

2705_p18_1 AG Senegal 2000 VI Plasma submitted

2843_p5_1 AG Senegal 1999 VI Plasma submitted

3169 _p4 AG Senegal 1999 VI Plasma submitted

3226 _J>15 AG Senegal 1999 VI Plasma submitted

3273_p21_1 AG Senegal 1999 VI Plasma submitted

B01 B China Hebei 2003 VI blood plasma EU363825

B03 B C ina Hebei 2003 WVI Sexual plasma EU363827

B04 B China Hebei 2006 WVI blood plasma EU363828

BZ167J2 B Brazil VI ccPBMC GQ855764

BJOX003000J9J B China Beijin 2007 II homosex. plasma submitted

9 ΒϋΟΧ020000.03_2 B China/Beijin 2007 II homosex. plasma submitted

9

B02 B China Gans 2003 V VI blood plasma EU363826

u

B05 B ChinaHubei 2006 V/VI Blood plasma EU363829

HXB2 B France Chronic M- K03455

Bx08_16 B France VI ccPBMC GQ855765

PV0_4_ B Italy 1994 III M-M ccPBMC AY835444 11022

TR0.11. B Italy 1995 III M-M ccPBMC AY835445 11023

H022.7 B Peru VI Sexual ccPBMC EF210725

H029_12 B Peru VI Sexual ccPBMC EF210726

H030.7 B Peru VI Sexual ccPBMC EF210727

H031_7 B Peru VI Sexual ccPBMC EF210728

H035_18 B Peru VI Sexual ccPBMC EF210729

H061J4 B Peru VI Sexual ccPBMC EF210730

H079_2 B Peru VI Sexual ccPBMC EF210731

H086J B Peru VI Sexual ccPBMC EF210732

H078_1 B Peru VI Sexual ccPBMC EF210733

H077J31 B Peru VI Sexual ccPBMC EF210734

H080_23 B Peru VI Sexual ccPBMC EF210735

NKR_0512_8 B Thailand ccPBMC submitted

RPW_0510_2 B Thailand ccPBMC submitted

QH0692_42_ B Trinidad 1994 V F-M ccPBMC AY835439 11018

QH0515.1 B Trinidad 1994 IV F-M ccPBMC AY835440

SC422661_8_ B Trinidad 1995 IV F-M Plasma AY835441 11058

SC05JC11J344 B Trinidad 1993 II Heterosexual Plasma EU289200 11576

SC45_4B5_2631 B Trinidad 1995 II Heterosexual Plasma EU289201 11577

TT29P_3A1_2769 B Trinidad 1998 Heterosexual Plasma EU577190

TT31P_2F10_2792 B Trinidad 1998 II Heterosexual Plasma EU577213

SF162.LS B USA VI EU123924 10463

Bal_26 B USA VI Mother to Child Tissue (Lung) DQ318211

6101J0 B USA 1994 V M-M ccPBMC AY835434

6535_3_ B USA 1995 V M-M ccPBMC AY835438 11017

SS1196J B USA 1997 V-VI M-M ccPBMC AY835442

BG1168J B USA 1996 III M-M ccPBMC AY835443

AC10_0_29_ B USA 1998 III M-M ccPBMC AY835446 11024

MN.3 B USA VI submitted

RHPA4259_7_ B USA 2000 ≤v M-F Plasma AY835447 11036

THR0 156J8. B USA 2000 II M-M Plasma AY835448 11037

REJ04541_67_ B USA 2001 II F-M Plasma AY835449 11035

TRJ04551_58_ B USA 2001 II M-M Plasma AY835450 11034

WITO4160_33_ B USA 2000 II F-M Plasma AY835451 11033

CAAN5342_A2_ B USA 2004 ≤VI M-M Plasma AY835452 11038

ACH320-W61 D-TCLA B submitted

1006.1 LC3J601 B USA 1997 III Plasma EU289183 11560

1012_11_TC21_3257 B USA 1997 III Plasma EU289184 11559

1054_07_TC4J499 B USA 1997 II Plasma EU289185 11561

1056_10_TA11_1826 B USA 1998 II Plasma EU289186 11562

1058_11_B11_1550 B USA 1998 IV Plasma EU289187 11563

1059_09JW_1460 B USA 1998 III Plasma EU289188 11564

62357J4_D3_4589 B USA 1996 II Plasma EU289189 11565

6240_08_TA5_4622 B USA 1995 II Plasma EU289190 11567

6244_13_B5_4576 B USA 1996 II Plasma EU289191 11566

63358_04_P3_4013 B USA 1997 II Plasma EU289192 11568

704809221.1B3 c South Africa 2007 l/ll sexual Plasma submitted 706010018JE3 C South Africa 2007 VI sexual Plasma submitted

706010164.1A7 C South Africa 2007 IV sexual Plasma submitted

7048092211B3_Rev_ C South Africa 2007 I or II Hetero plasma submitted

7060101641A7_Rev_ c South Africa 2007 I or II Hetero plasma submitted

98_v3_ca c Tanzania 2004 earty Hetro Plasma submitted

6644_v2_c33 c Tanzania 2004 V/VI Hetero Plasma submitted

0O41.v3.c18 c Tanzania 2004 WVI Hetero Plasma submitted

0921_v2_c14 c Tanzania 2004 V/VI Hetero Plasma submitted

3168_v4_c10 c Tanzania 2005 V/VI Hetero Plasma submitted

3637_v5_c3 c Tanzania 2005 V/VI Hetero Plasma submitted

3728_v2_c6 c Tanzania 2004 lll/IV Hetero Plasma submitted

3873_v1_c24 c Tanzania 2003 V/VI Hetero Plasma submitted

6022_v7_c24 c Tanzania 2006 earty Hetro Plasma submitted

6040_v4_c15 c Tanzania 2005 V/VI Hetero Plasma submitted

6322_v4_c1 c Tanzania 2005 V/VI Hetero Plasma submitted

6471_v1_c16 c Tanzania 2003 V VI Hetero Plasma submitted

6631_v3_c10 c Tanzania 2004 V/VI Hetero Plasma submitted

6785_v5_c14 c Tanzania 2005 V/VI Hetero Plasma submitted

6838.v1.c35 c Tanzania 2003 l/ll Hetero Plasma submitted

6980.v1.c17. c Tanzania 2003 earty Hetro Plasma submitted

933_v4_c4 c Tanzania 2005 earty Hetro Plasma submitted

234_F1.16.57 c Tanzania V Hetero Plasma submitted

410_F2_1_30 c Tanzania VI Hetero Plasma submitted

541-F1.A7.2 c Tanzania ND Hetero Plasma submitted

569.F1_37.10 c Tanzania V/VI Hetero Plasma submitted

98_F4.H5.13 c Tanzania V Hetero Plasma submitted

PWJ.0513.39 c Thailand ccPBMC submitted

96Z 651_02 c Zambia VI AF286224

ZM55F_PB28a c Zambia 1998 ≤VI -F ucPBMC AY423971

Z 53M_PB12_ c Zambia 2000 ≤VI F-M ucPBMC AY423984 11313

ZM135 _PL10a_ c Zambia 1998 ≤VI F-M Plasma AY424079 11315

ZM109F_PB4_ c Zambia 2000 ≤VI M-F ucPBMC AY424138 11314

ZM106F_PB9 c Zambia 1998 ≤VI M-F ucPBMC AY424163

Z 249M_PL1_ c Zambia 2003 II F-M Plasma DQ388514 11319

ZM197 _PB7_ c Zambia 2002 ≤VI F-M ucPBMC DQ388515 11309

ZM214M.PL15. c Zambia 2003 ≤VI F-M Plasma DQ3B8516 11310

ZM233 .PB6. c Zambia 2002 ≤VI F-M ucPBMC DQ388517 11311

ZM215F.PB8 c Zambia 2002 ≤VI M-F ucPBMC DQ422948

246F.C1G c Zambia 2003 II M to F Plasma submitted

249 .B10 c Zambia 2003 IV F to M Plasma submitted

ZM247v1_Rev_ c Zambia 2003 II Hetero plasma submitted

3326_v4_c3 CD Tanzania 2005 V/VI Hetero Plasma submitted

6952_v1_c20 CD Tanzania 2003 early Hetro Plasma submitted

3337_v2_c6 CD Tanzania 2004 V VI Hetero Plasma submitted

3817_v2.c59 CD Tanzania 2004 early Hetro Plasma submitted

6480_v4_c25 CD Tanzania 2004 early Hetro Plasma submitted

6650_v1_c8 CD Tanzania 2003 V/VI Hetero Plasma submitted

6811_v7_c18 CD Tanzania 2006 earty Hetro Plasma submitted

401.F1.8_10 CD Tanzania VI Hetero Plasma submitted

252.7 C!ade G W. Afr. VI EU513190 CNE59 CRF.01 AE China/Yunna 2006 VI IDU PB C submitted n

AE03 CRF01.AE 2005 V/VI Sexual plasma EU363851

China/Shang

hai

BJOX005000_09_2 CRF01_AE C ina Beijin 2007 II homosex. plasma submitted

9

BJOX009000.02.4 CRFOLAE China/Beijin 2007 IV homosex. plasma submitted g

BJOX010000.06.2 CRF01_AE China/Beijin 2007 II homosex. plasma submitted

9

BJOX025000_01.1 CRFOLAE China/Beijin 2007 II homosex. plasma submitted

9

BJOX02BO00J0.3 CRFOLAE China/Beijin 2007 II homosex. plasma submitted

9

AE01 CRFOLAE China/Guan 1999 V/VI Blood plasma EU363849 gdong,

CNE5 CRFOLAE China/Hena 2006 VI hetero PBMC submitted n

AE02 CRF01.AE China/Yunna 2006 V VI IDU plasma EU363850 n

CNE28 CRF01_AE China/Yunna 2007 VI IDU PBMC submitted n

CNE3 CRFOLAE China Yunna 2006 VI IDU PBMC submitted n

CNE55 CRF01.AE China/Yunna 2007 VI IDU PBMC submitted n

CNE56 CRFOLAE China/Yunna 2007 VI IDU PBMC submitted n

CNE8 CRF01_AE China/Yunna 2006 VI IDU PBMC submitted n

C1080rsga_c3 CRFOLAE Thailand 1999 >VI Hetero Plasma submitted

R1166rsga_c1 CRFOLAE Thailand 1998 >VI Hetero Plasma submitted

R2184rsga_c4 CRFOLAE Thailand 2001 >VI Hetero Plasma submitted

R3265rsga_c6 CRFOLAE Thailand 1999 >VI Hetro Plasma submitted

KSS_0514_13 CRFOLAE Thailand ccPBMC submitted

PSR_0508_2 CRFOLAE Thailand ccPBMC submitted

SPK_0525_13 CRFOLAE Thailand ccPBMC submitted

T266.60 CRF02_AG Cameroon Chronic ccPBMC EU513193

T278_50_ CRF02.AG Cameroon Chronic ccPBMC EU513198

DJ263J CRF02_AG Djibouti VI Sexual ccPBMC AF063223

263_8_ CRF02_AG' Cameroon Chronic ccPBMC EU513182

T255_34_ CRF02_AG' Cameroon Chronic ccPBMC EU513184

T257JL CRF02_AG' Cameroon Acute/early ccPBMC EU513185

T33_7_ CRF02_AG' Cameroon Chronic ccPBMC EU513186

211_9_ CRF02_AG' Cameroon Chronic ccPBMC EU513187

242_14_ CRF02_AG' Cameroon Chronic ccPBMC EU513188

T250_4_ CRF02.AG' Cameroon Chronic ccPBMC EU513189

T253J 1 CRF02_AG' Cameroon Chronic ccPBMC EU513191

269.12 CRF02_AG' Cameroon Chronic ccPBMC EU513194

235_47_ CRF02_AG' Cameroon Chronic ccPBMC EU513195

T251_18_ CRF02_AG' Cameroon Chronic ccPBMC EU513196

271 J 1_ CRF02_AG' Cameroon Acute/early ccPBMC EU513197 928_28_ CRF02_AG' Cote d'lvoire Acute/early ccPBMC EU513199

1656_p21 CRF06_cpx Senegal 1999 VI Plasma submitted

CH038J2 CRF07.BC China 2004 VI IVDU ccPBMC EF042692

CH064.20 CRF07.BC China 2004 VI IVDU ccPBMC EF117254

CH070J CRF07.BC China 2004 VI IVDU ccPBMC EF117255

CH091.9 CRF07.BC China 2003 VI IVDU ccPBMC EF117256

CH110J CRF07.BC China 2004 VI IVDU ccPBMC EF117257

CH111J CRF07.BC China 2004 VI IVDU ccPBMC EF117258

CH181.12 CRF07.BC China 2004 VI IVDU ccPBMC EF117259

CH120_6 CRF07_BC China 2004 VI IVDU ccPBMC EF117260

CH119_10 CRF07_BC China 2004 VI IVDU ccPBMC EF117261

CH117J CRF07_BC China 2004 VI IVDU ccPBMC EF117262

CH115_12 CRF07_BC China 2004 VI IVDU ccPBMC EF117263

CH114J CRF07.BC China 2004 VI IVDU ccPBMC EF117264

BC14 CRF07_BC China/Beijin 2003 V/VI Sexual plasma EU363844

9

BC15 CRF07.BC China Beijin 2003 V/VI Sexual plasma EU363845

9

BJOX002000.03.2 CRF07.BC China Beijin 2007 I /II homosex. plasma submitted g

BC04 CRF07_BC China/Sichu 2007 V/VI IDU plasma EU363834 an

BC05 CRF07_BC China/Sichu 2007 V/VI IDU plasma EU363835 an

BC03 CRF07_BC China/Xinjia 2003 V/VI IDU plasma EU363833 ng

BC18 CRF07_BC China/Xinjia 2005 V/VI IDU plasma EU363848 ng

CNE20 CRF07_BC China Xinjia 2007 VI hetero PBMC submitted ng

CNE19 CRF07_BC China/Xinjia 2007 VI hetero PBMC submitted ng

CNE21 CRF07.BC China/Xinjia 2007 VI hetero PBMC submitted ng

BC07 CRF07.BC China/Yunna 2005 V/VI IDU plasma EU363837 n

BC09 CRF07.BC China/Yunna 2003 V/VI Sexual plasma EU363839 n

BC10 CRF07.BC China Yunna 2005 V/VI IDU plasma EU363840 n

BC01 CRF08_BC China Yunna 2006 V/VI IDU plasma EU363831 n

BC06 CRF08_BC China/Yunna 2005 V/VI IDU plasma EU363836 n

BC08 CRF08.BC China/Yunna 2006 V/VI IDU plasma EU363838 n

BC11 CRF08_BC China Yunna, 2005 V/VI IDU plasma EU363841 n

BC17 CRF08_BC China Yunna 2006 V/VI IDU plasma EU363847 n

X2252_c7 CRF14_BG Portugal 2007 Chronic Hetero Plasma EU885766

X1100_c7 CRF14_BG Switzerland 2002 NA IDU Plasma EU885760

3016_v5_c45 D Tanzania 2005 I or II Hetro Plasma submitted

6405_v4_c34 D Tanzania 2004 early Hetro Plasma submitted A07412M1_vrc12 D Uganda 1999 n a ccPBMC submitted

X20B8 G Ghana Chronic Hetero Plasma EU885764

P0 02_c2_11 G Portugal 2002 Chronic Hetero Plasma EU885759

X1193 G,_B Spain 2002 Chronic IDU Plasma EU885761

Spain 2003 Chronic IDU Plasma EU885762

X185 _10 Spain 2005 IDU Plasma EU885763 _c25 Spain 2007 Chronic IDU Plasma EU885765

UNC6316J 1 H USA Chronic submitted

* 6540 and

6545 are a

likely

epidemiological!

y linked pair,

with very

.42629333Γ

Table 9. Sets of sites used for deeper combinatorial analyses of signatures.

'bl2 epitope, the region of gpl20 that is bound by mAb bl2; CD4bs, the CD4 binding site;

CoRbs, the CCR5 coreceptor binding site; MPER, membrane proximal external region. The entire protein was scanned for simple signatures, but for more complex signatures (multiple amino acids per position and multiple positions in combination) to make the analyses computationally feasible, only regions of known biological relevance were scanned. An examination was made of the preservation or loss of glycosylation sequons (potential N-linked glycosylation sites PNLGs) in conjunction with neutralization susceptibility, testing for the acquisition or loss of the amino acid pattern Nx[ST], where N is an Asp, x is any amino acid, and [ST] is either a Ser or Thr.

Table 10. Summary of charged residues in the gpl20 core structure. Qualitative evaluation of all acidic residues in the recent X-ray structure of bl2-bound to the JRFL gpl20 [35] that was used in the electrostatic potential calculations.

Table 11 : A list of all sites that co-vary with bl2 signature sites. All sites are found to co-vary in a contingency table analysis with a q-value < 0.2. Co-variation sets among signature sites are highlighted in bold or underlined.

¹ All site positions are based on HXB2 numbering.

Table 12. HTV-l -positive serum samples used for signature analysis. Single SGA Env clones were sequenced from each sample. All samples were taken during chronic infection, at the same time the sample was tested for cross-reactive neutralizing antibodies. All sequences have been submitted to GenBank (in progress).

704010461 C South Africa 2007 CH010461.w12.p1 submitted

704010540 C South Africa 2008 CH010540.e.p1 submitted

704010581 C South Africa 2008 CH010581.e.p1 submitted

704010605 C South Africa 2008 CH010605.w12.p1 submitted

707010175 A Tanzania 2008 CH0175.e2 submitted

707010219 A1 Tanzania 2008 CH0219.e4 submitted

703010269 C Malawi 2007 CH0269.e3 submitted

707010457 C Tanzania 2008 CH0457.e1 submitted

705010534 C South Africa 2008 CH0534.e1 submitted

707010536 A1/C Tanzania 2008 CH0536.e2 submitted

713080024 B England 2008 CH080024.e.p1 submitted

713080038 B England 2008 CH080038.e.p2 submitted

713080046 B England 2008 CH080046.e.p1 submitted

713080052 B England 2008 CH080052.e.p1 submitted

713080060 B England 2008 CH080060.e.p1 submitted

713080071 B England 2008 CH080071.e.p2 submitted

713080087 B England 2008 CH080087.e.p1 submitted

713080095 B England 2008 CH080095.e.p1 submitted

713080100 CRF01_AE England 2008 CH080100.e.p1 submitted

7130801 17 A1 England 2008 CH080117.e.p1 submitted

713080128 B England 2008 CH080128.e.p1 submitted

713080134 B England 2008 CH080134.e.p1 submitted

713080142 B England 2008 CH080142.e.p1 submitted

713080156 B England 2008 CH080156.e.p1 submitted

713080169 B England 2008 CH080169.e.p1 submitted

713080175 B England 2008 CH080175.e.p2 submitted

713080183 B England 2008 CH080183.e.p1 submitted

713080191 B England 2008 CH080191.e.p1 submitted

713080203 B England 2008 CH080203.e.p1 submitted

713080219 B England 2008 CH080219.e.p2 submitted

713080225 B England 2008 CH080225.e.p2 submitted

713080258. B England 2008 CH080258.e.p2 submitted

713080510 A1 England 2008 CH080510.e.p2 submitted

Table 13. HIV-1 strains used for NAb assays to identify signatures in serum-derived Env sequences.

* * *

All documents and other information sources cited herein are hereby rporated in their entirety by reference.

Claims

WHAT IS CLAIMED IS:

1. An HIV envelope protein comprising the signature regions of CH0219.e4 or CHO80510.ep2.

2. The protein according to claim 1 wherein said protein comprises the signature regions of CH0219.e4.

3. The protein according to claim 1 wherein said protein is a gp 160 or gpl40 protein.

4. The protein according to claim 1 wherein said protein is a gp 120 or gp41 protein.

5. The protein according to claim 1 wherein said protein comprises the amino acid sequence of CH0219.e4 gpl60 or CH0219.e4 gpl40 shown in Fig. 13.

6. An isolated nucleic acid encoding the protein according to claim 1.

7. The nucleic acid according to claim 6 wherein said nucleic acid is present in a vector.

8. The nucleic acid according to claim 7 wherein said vector is a viral vector.

9. A composition comprising the protein according to claim 1 or the nucleic acid according to claim 6 and a carrier.

10. The composition according to claim 9 wherein said composition further comprises an adjuvant.

1 1. A method of inducing an immune response in a mammal comprising administering said protein according to claim 1 or said nucleic acid according to claim 6 to said mammal in an amount sufficient to induce said response.

12. The method according to claim 1 1 wherein said mammal is a human.

13. An isolated antibody specific for said protein according to claim 1, or antigen binding fragment thereof.

14. A method of inhibiting infection of a mammalian cell by HIV-1 comprising contacting said cell with said antibody according to claim 13, or said fragment thereof, under conditions so that said inhibition is effected.

15. The method according to claim 14 wherein said cell is a human cell.