CN115565607A - Method, device, readable medium and electronic equipment for determining protein information - Google Patents
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Abstract
The present disclosure relates to a method, an apparatus, a readable medium and an electronic device for determining protein information, comprising: acquiring a target protein sequence of protein information to be determined; inputting the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model; determining protein information of the target protein sequence according to the target protein expression, wherein the protein information comprises at least one of protein structure information, protein function information, protein stability information and protein interaction information; the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, each sample sequence information comprises a sample protein representation, a sample gene ontology feature and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology feature.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a readable medium, and an electronic device for determining protein information.
Background
Proteins are the basic substances of all life, are the most basic and important components of body cells, and the prediction of protein structure is very important for biology, medicine and pharmacy, which is helpful for understanding the role of proteins. In the related art, a protein pre-training model is generated according to a traditional Transformer model, fine tuning is performed on the protein pre-training model, and the protein structure is predicted, so that the accuracy of the protein pre-training model directly influences the accuracy of the predicted protein structure. Therefore, how to improve the accuracy of the pre-training model becomes an urgent problem to be solved.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present disclosure provides a method of determining protein information, the method comprising:
acquiring a target protein sequence of protein information to be determined;
inputting the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model;
determining protein information for the target protein sequence based on the target protein representation, the protein information including at least one of protein structure information, protein function information, protein stability information, and protein interaction information;
the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, the sample sequence information comprises a sample protein representation, sample gene ontology features and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology features.
In a second aspect, the present disclosure provides an apparatus for determining protein information, the apparatus comprising:
the first acquisition module is used for acquiring a target protein sequence of the protein information to be determined;
a second obtaining module, configured to input the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model;
a determining module for determining protein information of the target protein sequence based on the target protein representation, the protein information including at least one of protein structure information, protein function information, protein stability information, and protein interaction information;
the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, each sample sequence information comprises a sample protein representation, a sample gene ontology feature and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology feature.
In a third aspect, the present disclosure provides a computer readable medium having stored thereon a computer program which, when executed by a processing apparatus, performs the steps of the method of the first aspect of the present disclosure.
In a fourth aspect, the present disclosure provides an electronic device comprising:
a storage device having at least one computer program stored thereon;
at least one processing device for executing the at least one computer program in the storage device to implement the steps of the method of the first aspect of the disclosure.
Through the technical scheme, a target protein sequence of the protein information to be determined is obtained; inputting the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model; determining protein information for the target protein sequence based on the target protein representation, the protein information including at least one of protein structure information, protein function information, protein stability information, and protein interaction information; the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, each sample sequence information comprises a sample protein representation, a sample gene ontology feature and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology feature. That is to say, the training set for training the target protein representation model according to the present disclosure includes, in addition to the sample protein sequence and the sample protein representation corresponding to the sample protein sequence, the sample gene ontology feature corresponding to the sample protein sequence and the sample relationship information corresponding to the sample protein sequence, and the protein representation capability of the target protein representation model can be improved by the sample gene ontology feature and the sample relationship information, so that the accuracy of the target protein representation model is higher, and thus, the target protein representation determined according to the target protein representation model is also more accurate, thereby improving the accuracy of the target protein information determined according to the target protein representation.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale. In the drawings:
FIG. 1 is a flow chart illustrating a method of determining protein information according to an exemplary embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating a method of model generation according to an exemplary embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a protein representation model shown in accordance with an exemplary embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating model training in accordance with an exemplary embodiment of the present disclosure;
FIG. 5 is a block diagram illustrating an apparatus for determining protein information according to an exemplary embodiment of the present disclosure;
FIG. 6 is a block diagram illustrating another apparatus for determining protein information according to an exemplary embodiment of the present disclosure;
fig. 7 is a block diagram illustrating an electronic device according to an exemplary embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein is intended to be open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure is described below with reference to specific examples.
Fig. 1 is a flowchart illustrating a method of determining protein information according to an exemplary embodiment of the present disclosure, which may include, as shown in fig. 1:
s101, obtaining a target protein sequence of the protein information to be determined.
The target protein sequence may be a protein sequence of any length, which is not limited in the present disclosure.
S102, inputting the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model.
Wherein the target protein representation may comprise the sequence length and hidden dimension (hidden dim) of the target protein sequence, which may be, for example, P e R L×D L is the sequence length and D is the concealment dimension. The target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, each sample sequence information comprises a sample protein representation, a sample gene ontology feature and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology feature.
In this step, the target protein sequence may be input into the target protein model, and the sequence length and hidden dimension of the target protein sequence may be determined for the target protein sequence by the target protein model, so as to obtain a target protein representation corresponding to the target protein sequence.
S103, determining protein information of the target protein sequence according to the target protein representation.
Wherein the protein information may include at least one of protein structure information, protein function information, protein stability information, and protein interaction information.
In this step, after obtaining a representation of the target protein corresponding to the target protein sequence, the protein information of the target protein sequence can be specified by a previously generated information specifying model. For example, the information determination model may be a structure prediction model, and the target protein representation is input into the structure prediction model, so that the structure information of the target protein sequence can be obtained.
By adopting the method, the training set used for training the target protein representation model comprises the sample protein sequence and the sample protein representation corresponding to the sample protein sequence, and also comprises the sample gene ontology characteristic corresponding to the sample protein sequence and the sample relation information corresponding to the sample protein sequence, and the protein representation capability of the target protein representation model can be improved through the sample gene ontology characteristic and the sample relation information, so that the accuracy of the target protein representation model is higher, and thus, the target protein representation determined according to the target protein representation model is more accurate, and the accuracy of the target protein information determined according to the target protein representation is improved.
Fig. 2 is a flowchart illustrating a model generation method according to an exemplary embodiment of the present disclosure, which may include, as shown in fig. 2:
and S21, acquiring a plurality of sample sets.
The sample set may be a protein knowledge map disclosed in the prior art, for example, the sample set may be selected from proteins in a Gene Ontology (GO) database, where a protein triplet is stored in the GO database, the triplet includes a protein sequence, a Gene Ontology feature corresponding to the protein sequence, and an association relationship corresponding to the protein sequence, and the Gene Ontology feature and the association relationship are both described in words. Illustratively, the triplet may be "MNPRRKKRLLVIVAVLFGIGASLV.", "part of", "cytolic large ribosomal basic library" ", which is a protein-localized in The cytosol, wherein" MNPRRKKRLLVIVAVLFGIGASLV. "is The protein sequence," part of "is a gene-localized characteristic corresponding to The protein sequence," cytolic large ribosomal basic library: the large sublibrary of a protein-localized in The cytosol.
In this step, a plurality of protein triples may be obtained from the GO database, for each protein triplet, the protein sequence in the protein triplet is used as the sample protein sequence, the gene ontology feature and the association relationship in the protein triplet are used as the sample gene ontology feature and the sample association relationship corresponding to the sample protein sequence, and the sequence length and the hidden dimension of the sample protein sequence are determined to obtain the sample protein representation corresponding to the sample protein sequence.
S22, determining a current sample set from the plurality of sample sets, taking a preset protein representation model as a current protein representation model, and circularly executing the model training step according to the current sample set until the trained current protein representation model meets a preset iteration stopping condition, and taking the trained current protein representation model as the target protein representation model.
Wherein, the model training step comprises: determining a first loss value corresponding to the current sample set through the current protein representation model; determining a model through a relationship generated in advance, and determining a second loss value corresponding to the current sample set; determining a target loss value according to the first loss value and the second loss value; and under the condition that the current protein representation model does not meet the preset iteration stopping condition according to the target loss value, updating the parameters of the current protein representation model according to the target loss value to obtain a trained current protein representation model, taking the trained current protein representation model as a new current protein representation model, and determining a new current sample set from a plurality of sample sets. The relationship determination model may be an MLP (multi layer Perceptron) model.
In this step, any sample set in the multiple sample sets may be used as the current sample set, after the current sample set is determined, the preset protein representation model is obtained, the preset protein representation model is used as the current protein representation model, a first loss value corresponding to the current sample set is determined through the current protein representation model, and a second loss value corresponding to the current sample set is determined through the relationship determination model.
In a possible implementation manner, the sample protein sequence corresponding to the current sample set may be input into the current protein representation model to obtain a predicted protein representation output by the current protein representation model; and determining a first loss value corresponding to the current sample set according to the predicted protein representation and the sample protein representation corresponding to the current sample set. Illustratively, the first loss value may be determined by an MLM (Masked Language Model) loss function.
The current protein representation model includes a fourier transform layer and a protein representation determination layer, the fourier transform layer may perform a two-dimensional fourier transform process, and for example, the sample protein sequence corresponding to the current sample set may be input into the current protein representation model, sample dimension information of the sample protein sequence corresponding to the current sample set may be determined by the fourier transform layer, and the predicted protein representation may be determined by the protein representation determination layer according to the sample dimension information.
FIG. 3 is a protein representation model schematic diagram illustrating a current protein representation model further including, as shown in FIG. 3, an embedding layer, the protein representation determining layer including Add & Norm, a feed-forward network, and Add & Norm, an output of the embedding layer coupled to an input of the Fourier transform layer, an output of the Fourier transform layer coupled to an output of the protein representation determining layer, according to an exemplary embodiment of the present disclosure. After the sample protein sequence is input into the current protein representation model, a sequence embedding vector corresponding to the sample protein sequence is determined through the embedding layer, the sequence embedding vector is input into the Fourier transform layer, one-dimensional Fourier transform is performed on the sequence dimension of the sample protein sequence through the Fourier transform layer, one-dimensional Fourier transform is performed on the hidden dimension of the sample protein sequence, and sample dimension information containing the sample sequence dimension and the sample hidden dimension is obtained. The sample dimensional information may then be input into the protein representation determination layer from which the predicted protein representation is derived.
It should be noted that, since the time complexity of the fourier transform processing is relatively low, when the protein sequence is relatively long, the calculation time can be greatly reduced by the processing of the fourier transform layer, thereby improving the efficiency of the model training.
After the predicted protein representation corresponding to the current sample set is obtained, the gene ontology features of the sample corresponding to the current sample set can be input into a preset feature extraction model to obtain the gene feature information output by the preset feature extraction model; representing and inputting the gene characteristic information and the predicted protein corresponding to the current sample set into the relation determination model to obtain predicted relation information output by the relation determination model; and determining a second loss value corresponding to the current sample set according to the prediction relation information and the sample relation information corresponding to the current sample set. The preset feature extraction model may be a feature extraction model mature in the prior art, for example, the preset feature extraction model may be a BioBERT model.
For example, the gene ontology feature of the sample corresponding to the current sample set may be input into the preset feature extraction model to obtain the gene feature information corresponding to the current sample set, and the gene feature information and the predicted protein representation corresponding to the current sample set may be input into the relationship determination model to obtain the predicted relationship information corresponding to the current sample set. And then, determining a second loss value corresponding to the current sample set according to the prediction relation information and the sample relation information corresponding to the current sample set through a cross entropy loss function.
After determining the first loss value and the second loss value, the target loss value may be determined based on the first loss value and the second loss value. For example, the sum of the first loss value and the second loss value may be used as the target loss value, or different weights may be set for the first loss value and the second loss value in advance, for example, a first weight corresponding to the first loss value is 0.6, a second weight corresponding to the second loss value is 0.4, a product of the first loss value and the first weight is calculated to obtain a first target loss value, a product of the second loss value and the second weight is calculated to obtain a second target loss value, and a sum of the first target loss value and the second target loss value is used as the target loss value.
After the target loss value is determined, a preset loss value threshold value can be obtained, and under the condition that the target loss value is determined to be smaller than or equal to the preset loss value threshold value, the current protein representation model can be determined to meet the preset iteration stop condition and is taken as the target protein representation model; in a case that it is determined that the target loss value is greater than the preset loss value threshold, it may be determined that the current protein representation model does not satisfy the preset iteration stop condition, and according to the target loss value, parameters of the current protein representation model are updated to obtain a trained current protein representation model, and the trained current protein representation model is used as a new current protein representation model, and a new current sample set is determined from the plurality of sample sets, for example, the new current sample set may be randomly determined from the plurality of sample sets.
After obtaining a new current protein representation model and a new current sample set, the model training step may continue until it is determined that the target loss value is less than or equal to the preset loss value threshold, and the newly determined current protein representation model is taken as the target protein representation model.
Fig. 4 is a schematic diagram illustrating model training according to an exemplary embodiment of the disclosure, and as shown in fig. 4, a sample protein sequence is input into the current protein representation model to obtain a predicted protein representation corresponding to the sample protein sequence, and determine the first loss value, the sample gene ontology feature is input into the preset feature extraction model to obtain gene feature information corresponding to the sample protein sequence, the predicted protein representation and the gene feature information are input into the relationship determination model, and the second loss value is determined according to the relationship determination model and the sample relationship information corresponding to the sample protein sequence.
In a possible implementation manner, when it is determined that the current protein representation model does not satisfy the preset iteration stop condition according to the target loss value, parameters of the relationship determination model are updated according to the target loss value to obtain a trained relationship determination model, and the trained relationship determination model is used as a new relationship determination model. Illustratively, when the target loss value is greater than the preset loss value threshold, the parameters of the relationship determination model are synchronously updated according to the target loss value to obtain a new relationship determination model, and when the model training step is executed next time, the second loss value is determined by the new relationship determination model. Therefore, more accurate prediction relation information can be obtained, the second loss value determined according to the prediction relation information is more accurate, and the accuracy of the target protein representation model is further improved.
By adopting the model training method, the sample gene ontology features also participate in the training of the target protein representation model, so that the protein representation capability of the target protein representation model is stronger, and the accuracy of the target protein representation model is improved. In addition, in the process of model training, the dimension information of the protein sequence of the sample is determined through the Fourier transform layer, so that the calculated amount is greatly reduced, and the efficiency of model training is improved.
Fig. 5 is a block diagram illustrating an apparatus for determining protein information according to an exemplary embodiment of the present disclosure, which may include, as shown in fig. 5:
a first obtaining module 501, configured to obtain a target protein sequence of protein information to be determined;
a second obtaining module 502, configured to input the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model;
a determining module 503 for determining protein information of the target protein sequence according to the target protein representation, the protein information including at least one of protein structure information, protein function information, protein stability information, and protein interaction information;
the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, each sample sequence information comprises a sample protein representation, a sample gene ontology feature and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology feature.
Alternatively, fig. 6 is a block diagram illustrating another apparatus for determining protein information according to an exemplary embodiment of the disclosure, as shown in fig. 6, the apparatus further including:
a model training module 504, configured to obtain a plurality of sample sets; determining a current sample set from a plurality of sample sets, taking a preset protein representation model as a current protein representation model, circularly executing a model training step according to the current sample set until the current protein representation model after training meets a preset iteration stopping condition, and taking the current protein representation model after training as the target protein representation model; the model training step comprises: determining a first loss value corresponding to the current sample set through the current protein representation model; determining a model through a relationship generated in advance, and determining a second loss value corresponding to the current sample set; determining a target loss value according to the first loss value and the second loss value; and under the condition that the current protein representation model does not meet the preset iteration stopping condition according to the target loss value, updating the parameters of the current protein representation model according to the target loss value to obtain a trained current protein representation model, and taking the trained current protein representation model as a new current protein representation model.
Optionally, the model training module 504 is further configured to:
inputting the sample protein sequence corresponding to the current sample set into the current protein representation model to obtain a predicted protein representation output by the current protein representation model;
and determining a first loss value corresponding to the current sample set according to the predicted protein representation and the sample protein representation corresponding to the current sample set.
Optionally, the model training module 504 is further configured to:
inputting the sample gene ontology features corresponding to the current sample set into a preset feature extraction model to obtain gene feature information output by the preset feature extraction model;
representing and inputting the gene characteristic information and the predicted protein corresponding to the current sample set into the relation determination model to obtain predicted relation information output by the relation determination model;
and determining a second loss value corresponding to the current sample set according to the prediction relation information and the sample relation information corresponding to the current sample set.
Optionally, the current protein representation model comprises a fourier transform layer and a protein representation determination layer, the model training module 504 is further configured to:
inputting the sample protein sequence corresponding to the current sample set into the current protein representation model, determining the sample dimension information of the sample protein sequence corresponding to the current sample set through the Fourier transform layer, and determining the predicted protein representation through the protein representation determination layer according to the sample dimension information.
Optionally, the model training module 504 is further configured to:
and under the condition that the current protein representation model does not meet the preset iteration stopping condition according to the target loss value, updating the parameters of the relationship determination model according to the target loss value to obtain a trained relationship determination model, and taking the trained relationship determination model as a new relationship determination model.
By the device, the training set used for training the target protein representation model comprises the sample protein sequence and the sample protein representation corresponding to the sample protein sequence, and also comprises the sample gene ontology characteristic corresponding to the sample protein sequence and the sample relation information corresponding to the sample protein sequence, and the protein representation capability of the target protein representation model can be improved through the sample gene ontology characteristic and the sample relation information, so that the accuracy of the target protein representation model is higher, and thus, the target protein representation determined according to the target protein representation model is more accurate, and the accuracy of the target protein information determined according to the target protein representation is improved.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Referring now to FIG. 7, a block diagram of an electronic device 600 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 7, the electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 7 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a target protein sequence of protein information to be determined; inputting the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model; determining protein information for the target protein sequence based on the target protein representation, the protein information including at least one of protein structure information, protein function information, protein stability information, and protein interaction information; the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, each sample sequence information comprises a sample protein representation, a sample gene ontology feature and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology feature.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. The name of the module does not constitute a limitation to the module itself in some cases, and for example, the first acquisition module may also be described as a "module that acquires a target protein sequence of protein information to be determined".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Example 1 provides a method of determining protein information, according to one or more embodiments of the present disclosure, including: acquiring a target protein sequence of protein information to be determined; inputting the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model; determining protein information for the target protein sequence based on the target protein representation, the protein information including at least one of protein structure information, protein function information, protein stability information, and protein interaction information; the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, the sample sequence information comprises a sample protein representation, sample gene ontology features and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology features.
Example 2 provides the method of example 1, the target protein representation model being pre-generated by: obtaining a plurality of said sample sets; determining a current sample set from the plurality of sample sets, taking a preset protein representation model as a current protein representation model, and circularly executing a model training step according to the current sample set until the trained current protein representation model meets a preset iteration stopping condition, and taking the trained current protein representation model as the target protein representation model; the model training step comprises: determining a first loss value corresponding to the current sample set through the current protein representation model; determining a model through a relationship generated in advance, and determining a second loss value corresponding to the current sample set; determining a target loss value according to the first loss value and the second loss value; and under the condition that the current protein representation model does not meet the preset iteration stopping condition according to the target loss value, updating parameters of the current protein representation model according to the target loss value to obtain a trained current protein representation model, taking the trained current protein representation model as a new current protein representation model, and determining a new current sample set from the plurality of sample sets.
Example 3 provides the method of example 2, wherein determining, by the current protein representation model, the first loss value for the current sample set comprises: inputting the sample protein sequence corresponding to the current sample set into the current protein representation model to obtain a predicted protein representation output by the current protein representation model; and determining a first loss value corresponding to the current sample set according to the predicted protein representation and the sample protein representation corresponding to the current sample set.
Example 4 provides the method of example 3, and the determining, by a pre-generated relationship determination model, the second loss value corresponding to the current sample set includes: inputting the sample gene ontology features corresponding to the current sample set into a preset feature extraction model to obtain gene feature information output by the preset feature extraction model; inputting the gene characteristic information and the predicted protein representation corresponding to the current sample set into the relation determination model to obtain predicted relation information output by the relation determination model; and determining a second loss value corresponding to the current sample set according to the prediction relation information and the sample relation information corresponding to the current sample set.
Example 5 provides the method of example 3, wherein the current protein representation model includes a fourier transform layer and a protein representation determination layer, and the inputting the sample protein sequence corresponding to the current sample set into the current protein representation model to obtain the predicted protein representation output by the current protein representation model includes: inputting the sample protein sequence corresponding to the current sample set into the current protein representation model, determining sample dimension information of the sample protein sequence corresponding to the current sample set through the Fourier transform layer, and determining the predicted protein representation through the protein representation determination layer according to the sample dimension information.
Example 6 provides the method of example 2, further comprising, in accordance with one or more embodiments of the present disclosure: and under the condition that the current protein representation model does not meet the preset iteration stopping condition according to the target loss value, updating parameters of the relation determination model according to the target loss value to obtain a trained relation determination model, and taking the trained relation determination model as a new relation determination model.
Example 7 provides an apparatus to determine protein information, according to one or more embodiments of the present disclosure, comprising: the first acquisition module is used for acquiring a target protein sequence of the protein information to be determined; a second obtaining module, configured to input the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model; a determining module for determining protein information of the target protein sequence based on the target protein representation, the protein information including at least one of protein structure information, protein function information, protein stability information, and protein interaction information; the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, each sample sequence information comprises a sample protein representation, a sample gene ontology feature and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology feature.
Example 8 provides the apparatus of example 7, further comprising a model training module to obtain a plurality of the sample sets, in accordance with one or more embodiments of the present disclosure; determining a current sample set from the plurality of sample sets, taking a preset protein representation model as a current protein representation model, and circularly executing a model training step according to the current sample set until the trained current protein representation model meets a preset iteration stopping condition, and taking the trained current protein representation model as the target protein representation model; the model training step comprises: determining a first loss value corresponding to the current sample set through the current protein representation model; determining a model through a relationship generated in advance, and determining a second loss value corresponding to the current sample set; determining a target loss value according to the first loss value and the second loss value; and under the condition that the current protein representation model does not meet the preset iteration stopping condition according to the target loss value, updating the parameters of the current protein representation model according to the target loss value to obtain a trained current protein representation model, and taking the trained current protein representation model as a new current protein representation model.
Example 9 provides a computer readable medium having stored thereon a computer program that, when executed by a processing apparatus, performs the steps of the method of any of examples 1-6, in accordance with one or more embodiments of the present disclosure.
Example 10 provides, in accordance with one or more embodiments of the present disclosure, an electronic device comprising: a storage device having a computer program stored thereon; processing means for executing the computer program in the storage means to carry out the steps of the method of any of examples 1 to 6.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Claims (10)
1. A method of determining protein information, the method comprising:
acquiring a target protein sequence of protein information to be determined;
inputting the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model;
determining protein information for the target protein sequence based on the target protein representation, the protein information including at least one of protein structure information, protein function information, protein stability information, and protein interaction information;
the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, each sample sequence information comprises a sample protein representation, a sample gene ontology feature and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology feature.
2. The method of claim 1, wherein the target protein representation model is pre-generated by:
obtaining a plurality of said sample sets;
determining a current sample set from the plurality of sample sets, taking a preset protein representation model as a current protein representation model, and circularly executing a model training step according to the current sample set until the trained current protein representation model meets a preset iteration stopping condition, and taking the trained current protein representation model as the target protein representation model;
the model training step comprises:
determining a first loss value corresponding to the current sample set through the current protein representation model;
determining a model through a relationship generated in advance, and determining a second loss value corresponding to the current sample set;
determining a target loss value according to the first loss value and the second loss value;
and under the condition that the current protein representation model does not meet the preset iteration stopping condition according to the target loss value, updating parameters of the current protein representation model according to the target loss value to obtain a trained current protein representation model, taking the trained current protein representation model as a new current protein representation model, and determining a new current sample set from the plurality of sample sets.
3. The method of claim 2, wherein the determining, by the current protein representation model, a first loss value corresponding to the current sample set comprises:
inputting a sample protein sequence corresponding to the current sample set into the current protein representation model to obtain a predicted protein representation output by the current protein representation model;
and determining a first loss value corresponding to the current sample set according to the predicted protein representation and the sample protein representation corresponding to the current sample set.
4. The method of claim 3, wherein determining the second loss value corresponding to the current sample set according to a pre-generated relationship determination model comprises:
inputting the sample gene ontology features corresponding to the current sample set into a preset feature extraction model to obtain gene feature information output by the preset feature extraction model;
inputting the gene characteristic information and the predicted protein representation corresponding to the current sample set into the relation determination model to obtain predicted relation information output by the relation determination model;
and determining a second loss value corresponding to the current sample set according to the prediction relation information and the sample relation information corresponding to the current sample set.
5. The method of claim 3, wherein the current protein representation model comprises a Fourier transform layer and a protein representation determination layer, and wherein inputting the sample protein sequence corresponding to the current sample set into the current protein representation model to obtain the predicted protein representation output by the current protein representation model comprises:
inputting the sample protein sequence corresponding to the current sample set into the current protein representation model, determining sample dimension information of the sample protein sequence corresponding to the current sample set through the Fourier transform layer, and determining the predicted protein representation through the protein representation determination layer according to the sample dimension information.
6. The method of claim 2, further comprising:
and under the condition that the current protein representation model does not meet the preset iteration stopping condition according to the target loss value, updating parameters of the relation determination model according to the target loss value to obtain a trained relation determination model, and taking the trained relation determination model as a new relation determination model.
7. An apparatus for determining protein information, the apparatus comprising:
the first acquisition module is used for acquiring a target protein sequence of the protein information to be determined;
a second obtaining module, configured to input the target protein sequence into a target protein representation model to obtain a target protein representation output by the target protein representation model;
a determining module for determining protein information of the target protein sequence based on the target protein representation, the protein information including at least one of protein structure information, protein function information, protein stability information, and protein interaction information;
the target protein representation model is generated in advance through a plurality of sample sets, each sample set comprises a sample protein sequence and sample sequence information corresponding to the sample protein sequence, each sample sequence information comprises a sample protein representation, a sample gene ontology feature and sample relation information, and the sample relation information is used for representing the relation between the sample protein sequence and the sample gene ontology feature.
8. The apparatus of claim 7, further comprising a model training module configured to obtain a plurality of the sample sets; determining a current sample set from the plurality of sample sets, taking a preset protein representation model as a current protein representation model, and circularly executing a model training step according to the current sample set until the trained current protein representation model meets a preset iteration stopping condition, and taking the trained current protein representation model as the target protein representation model; the model training step comprises: determining a first loss value corresponding to the current sample set through the current protein representation model; determining a model through a relationship generated in advance, and determining a second loss value corresponding to the current sample set; determining a target loss value according to the first loss value and the second loss value; and under the condition that the current protein representation model does not meet the preset iteration stopping condition according to the target loss value, updating the parameters of the current protein representation model according to the target loss value to obtain a trained current protein representation model, and taking the trained current protein representation model as a new current protein representation model.
9. A computer-readable medium, on which a computer program is stored, characterized in that the program, when being executed by processing means, carries out the steps of the method of any one of claims 1 to 6.
10. An electronic device, comprising:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to carry out the steps of the method according to any one of claims 1 to 6.
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