Polypeptide sequence transformation method, device and equipment based on artificial intelligence and medium

Abstract

The invention relates to the technical field of polypeptide design, and discloses a polypeptide sequence transformation method, device and equipment based on artificial intelligence and a medium, and the method comprises the following steps: obtaining a reference polypeptide sequence; covering part of lexical elements in the reference polypeptide sequence by using preset covering lexical elements to obtain a covering polypeptide sequence; inputting the covering polypeptide sequence into a pre-trained polypeptide sequence modification model; on the basis of a prediction result output by the polypeptide sequence modification model, determining a prediction lexical element of a covered part in the covered polypeptide sequence; replacing the covered part of lexical elements in the reference polypeptide sequence with the predicted lexical elements to obtain a predicted polypeptide sequence; and if the predicted polypeptide sequence is different from the existing polypeptide sequence, taking the predicted polypeptide sequence as a modified polypeptide sequence. A new polypeptide sequence is obtained through modification based on an existing polypeptide sequence, and the quality and speed of polypeptide design can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of polypeptide design, and in particular to a polypeptide sequence modification method, device, equipment and medium based on artificial intelligence. Background Art

[0002] Peptide drugs are short-chain molecules composed of amino acids, which are between small molecule drugs and large molecule biological drugs, and show great potential in treating a variety of diseases, such as infectious diseases, cancer, and diabetes. However, the current peptide drug design is inefficient and of low quality. Summary of the invention

[0003] In view of this, the present invention provides an artificial intelligence-based polypeptide sequence modification method, device, equipment and medium to solve the problems of low efficiency and low quality in polypeptide drug design.

[0004] In a first aspect, the present invention provides a method for polypeptide sequence modification based on artificial intelligence, the method comprising: Obtaining a reference peptide sequence; Using a preset masking word to mask some words in the reference polypeptide sequence to obtain a masked polypeptide sequence; Inputting the masked polypeptide sequence into a pre-trained polypeptide sequence transformation model; Determining predicted words of the masked portion of the masked polypeptide sequence based on the prediction results output by the polypeptide sequence modification model; Replacing the masked part of the word in the reference polypeptide sequence with the predicted word to obtain a predicted polypeptide sequence; If the predicted polypeptide sequence is a polypeptide sequence that is different from any existing polypeptide sequence, the predicted polypeptide sequence is used as the modified polypeptide sequence.

[0005] In an optional embodiment, the word element of the reference polypeptide sequence is an amino acid residue word element; and / or, The masked word is a set non-amino acid residue word, or a designated amino acid residue word.

[0006] In an optional embodiment, the using of a preset masking word to mask some words in the reference polypeptide sequence to obtain a masked polypeptide sequence comprises: Acquire a set word unit covering parameter, wherein the word unit covering parameter includes: at least one of a word unit covering ratio, a word unit covering number, and a word unit covering position; According to the set word-memory masking parameters, some words in the reference polypeptide sequence are masked to obtain the masked polypeptide sequence.

[0007] In an optional embodiment, the reference polypeptide sequence is multiple, and the corresponding covering polypeptide sequence is multiple; the polypeptide sequence modification model is based on the multiple covering polypeptide sequences, and the predicted polypeptide sequence is multiple; and / or, The polypeptide sequence modification model is based on one masked polypeptide sequence, and the predicted polypeptide sequences obtained are multiple.

[0008] In an optional embodiment, if the predicted polypeptide sequence is a polypeptide sequence different from any existing polypeptide sequence, after using the predicted polypeptide sequence as the modified polypeptide sequence, the method further comprises: Inputting the sequence information of the modified polypeptide sequence and the sequence information of the target receptor, as well as the structural image of the modified polypeptide sequence and the structural image of the target receptor into a first multimodal deep learning model to predict the activity information of the modified polypeptide sequence; and / or, The sequence information of the modified polypeptide sequence and the sequence information of the target receptor, as well as the structural image of the modified polypeptide sequence and the structural image of the target receptor are input into the second multimodal deep learning model to predict the toxicity information of the modified polypeptide sequence.

[0009] In an optional embodiment, the first multimodal deep learning model and the second multimodal deep learning model both include a Star-Transformer encoder, a convolutional neural network, a feature fusion layer, and a fully connected layer; The Star-Transformer encoder is used to output first feature information based on the sequence information of the transformed polypeptide sequence and the sequence information of the target receptor; The convolutional neural network is used to output second feature information based on the structural image of the modified polypeptide sequence and the structural image of the target receptor; The feature fusion layer is used to fuse the first feature information and the second feature information to obtain fused feature information; The fully connected layer of the first multimodal deep learning model predicts the activity information of the modified polypeptide sequence based on the fused feature information, and the fully connected layer of the second multimodal deep learning model predicts the toxicity information of the modified polypeptide sequence based on the fused feature information.

[0010] In an optional embodiment, the training process of the polypeptide sequence modification model is: Obtaining sample peptide sequences; Using the masked word to mask some words in the sample polypeptide sequence to obtain a masked sample polypeptide sequence; The masked sample polypeptide sequence is used as the input of the polypeptide sequence transformation model to be trained, and the masked part of the word in the sample polypeptide sequence is used as the training label to train the polypeptide sequence transformation model.

[0011] In an optional embodiment, the word-meta coverage parameters of at least part of the sample polypeptide sequence are different from the word-meta coverage parameters of the reference polypeptide sequence; wherein the word-meta coverage parameters include: at least one of a word-meta coverage ratio, a word-meta coverage number, and a word-meta coverage position; and / or, The length of at least a portion of the sample polypeptide sequence is different from the length of the reference polypeptide sequence.

[0012] In a second aspect, the present invention provides a polypeptide sequence modification device based on artificial intelligence, the device comprising: A reference polypeptide sequence acquisition module, used to acquire a reference polypeptide sequence; A masking module, used to mask some words in the reference polypeptide sequence using preset masking words to obtain a masked polypeptide sequence; A prediction module, used for inputting the masked polypeptide sequence into a pre-trained polypeptide sequence transformation model; A predicted word unit determination module, used to determine the predicted word unit of the masked part in the masked polypeptide sequence based on the prediction result output by the polypeptide sequence transformation model; A predicted polypeptide sequence acquisition module, used to replace the masked part of the word element in the reference polypeptide sequence with the predicted word element to obtain a predicted polypeptide sequence; The modified polypeptide sequence determination module is used to use the predicted polypeptide sequence as the modified polypeptide sequence if the predicted polypeptide sequence is a polypeptide sequence different from any existing polypeptide sequence.

[0013] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the artificial intelligence-based polypeptide sequence modification method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the artificial intelligence-based polypeptide sequence modification method of the first aspect or any corresponding embodiment thereof.

[0015] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which are used to enable a computer to execute the artificial intelligence-based polypeptide sequence modification method of the above-mentioned first aspect or any corresponding embodiment thereof.

[0016] The artificial intelligence-based polypeptide sequence modification method, device, equipment and medium provided in the embodiments of the present invention utilize polypeptide backbone modification based on an existing polypeptide sequence (i.e., a reference polypeptide sequence) to generate a new polypeptide sequence (i.e., a modified polypeptide sequence) and explore the possibility of replacing different amino acid residues, thereby improving the quality and speed of polypeptide design. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is a schematic diagram of a process of a polypeptide sequence modification method based on artificial intelligence according to an embodiment of the present invention; Figure 2 is a schematic diagram of a word unit table (including masked word units) according to an embodiment of the present invention; Figure 3 is a schematic diagram of a polypeptide sequence modification model and a polypeptide sequence modification prediction process according to an embodiment of the present invention; Figure 4 is a schematic diagram of the architecture of the first multimodal deep learning model and the second multimodal deep learning model according to an embodiment of the present invention; Figure 5 is a structural block diagram of a polypeptide sequence modification device based on artificial intelligence according to an embodiment of the present invention; Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] Artificial Intelligence (AI) refers to the intelligent behavior exhibited by computer systems. It is a discipline that uses interdisciplinary technologies such as computer science, mathematics, and neuroscience to create systems that can perform tasks that usually require human intelligence.

[0021] Peptide design is an important field in biochemistry and molecular biology, which involves designing amino acid sequences according to specific functional requirements to form peptides with predetermined structures and functions. Peptides can be used as drugs, enzyme inhibitors, vaccine components or for other biomedical applications. With the development of artificial intelligence (AI) technology, AI has played an increasingly important role in peptide design. The advantage of using AI for peptide design is that it can handle complex data sets, quickly generate a large number of hypotheses, and continuously improve design strategies through iterative learning.

[0022] According to an embodiment of the present invention, an embodiment of a polypeptide sequence modification method based on artificial intelligence is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of executable computer instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0023] In this embodiment, a method for modifying a polypeptide sequence based on artificial intelligence is provided, which can be used in various computer devices. Figure 1 is a flow chart of a polypeptide sequence modification method based on artificial intelligence according to an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps: Step S101, obtaining a reference polypeptide sequence.

[0024] In the embodiments of the present invention, the reference polypeptide sequence may be a polypeptide sequence with high activity and low toxicity, so as to increase the possibility that the modified polypeptide sequence obtained by modification has high activity and low toxicity.

[0025] Specifically, the process of obtaining the reference polypeptide sequence may be: Collect and organize peptide data sets, including peptide sequences and corresponding activity and toxicity information, and select peptide sequences with high binding activity and low toxicity to the target as reference sequences. For example, RSV (Respiratory Syncytial Virus) peptide data collected from databases such as the DRAVP database (http: / / dravp.cpu-bioinfor.org / ) and the AVPdb database (http: / / crdd.osdd.net / servers / avpdb / ), and RSV peptide data collected from other channels. Then, active and non-toxic peptide sequences are screened out according to the set threshold. IC50 / EC50 30uM is used as the toxicity threshold, CC50>30uM is marked as 0, that is, non-toxic, and CC50 ⩽ 30uM is marked as 1, that is, toxic. IC50 / EC50 1uM was used as the activity threshold, IC50 / EC50>1uM was marked as 0, i.e. inactive, and IC50 / EC50 ⩽ 1uM was marked as 1, i.e. active. Finally, 119 active and non-toxic RSV peptide reference sequences were screened out, all of which were derived from the HR2 sequence of RSV (references: Sun, Z.; Pan, Y.; Jiang, S.; Lu, L. Respiratorysyncytial virus entry inhibitors targeting the F protein. Viruses 2013, 5,211–225.). The RSV receptor sequence was derived from the sequence included in the NCBI database (identification number FJ614815).

[0026] Step S102, using a preset masking word unit to mask some words in the reference polypeptide sequence to obtain a masked polypeptide sequence.

[0027] Specifically, the word of the reference polypeptide sequence can be an amino acid residue word. The mask word can be a set non-amino acid residue word or a specified amino acid residue word. For example, each amino acid residue is taken as a separate word and the mask word is taken as a special word. Figure 2 The word table shown, where <mask>To cover the word, <pad>To complete the word (used to complete the polypeptide sequence to the set maximum length of the model input), Y, A, T, V, L, D, E, G, R, H, I, W, Q, K, M, F, N, S, P, C are 20 amino acid residue words.

[0028] A word in the reference polypeptide sequence can be masked by a masking word, or multiple words in the reference polypeptide sequence can be masked by multiple masking words respectively. Multiple masked words in the reference polypeptide sequence can be non-adjacent, partially adjacent, or completely adjacent (i.e., multiple continuous words).

[0029] In an embodiment of the present invention, using designated amino acid residue words as masked words can improve the accuracy of predicting masked words using a subsequent polypeptide sequence transformation model. When the masked polypeptide sequence is subsequently input into a pre-trained polypeptide sequence transformation model, the position information of the masked words in the masked polypeptide sequence is simultaneously input into the polypeptide sequence transformation model. In the case where multiple words in a masked polypeptide sequence are replaced with masked words, that is, in the case where there are multiple masked words in a masked polypeptide sequence, these masked words may be the same masked word or different masked words, for example, some may be <mask>, some are specified amino acid residue words.

[0030] In some optional specific embodiments, step S102, i.e., using a preset masking word to mask some words in the reference polypeptide sequence to obtain a masked polypeptide sequence, includes: Step S1021, obtaining the set word-meta masking parameters, wherein the word-meta masking parameters include: at least one of the word-meta masking ratio, the number of word-meta masking, and the word-meta masking position. Among them, the word-meta masking ratio and the number of word-meta masking are generally selected from one of the two, that is, the word-meta masking parameter only includes one of them. Setting the word-meta masking ratio or the number of word-meta masking can control the number of masked word-meta. In addition, when the word-meta masking position parameter can uniquely determine the number of word-meta masking, the word-meta masking parameter can only include the word-meta masking position.

[0031] In an embodiment of the present invention, the word-blocking position of the reference polypeptide sequence may be specified. In other embodiments, the word-blocking position of the reference polypeptide sequence may be random.

[0032] Step S1022, according to the set word-meta masking parameters, mask some words in the reference polypeptide sequence to obtain the masked polypeptide sequence.

[0033] For example, Figure 3 As shown, the HR2 sequence of RSV (i.e., NFYDPLVFPSDEFDASISQVNEKINQSLAFIRKSDELLHNVNAGKSTTN) is divided into multiple single amino acid residue tokens, in which the token Y at the third position and the token L at the sixth position are masked (the masked tokens are marked with <mask>indicates, … indicates the omission of other tokens).

[0034] In addition, the set word-meta masking parameter may be a range of the number of word-meta masking, such as 1 to 5. Then, when masking some words in the reference polypeptide sequence according to the set word-meta masking parameter, no more than 5 words may be randomly selected for masking, that is, when masking words in the reference polypeptide sequence, the number of masked words is a random number between 1 and 5.

[0035] Step S103, inputting the masked polypeptide sequence into a pre-trained polypeptide sequence transformation model.

[0036] Specifically, the masked polypeptide sequence may not be directly input into the polypeptide sequence modification model, but may be firstly subjected to word segmentation processing, and the word units obtained by the word segmentation are replaced with corresponding numerical values ​​to obtain a digitized masked polypeptide sequence, and then the digitized masked polypeptide sequence is input into the polypeptide sequence modification model.

[0037] The peptide sequence modification model may be a deep learning model. For example, the peptide sequence modification model may be a model built based on a Transformer encoder architecture. The Transformer encoder architecture may be an architecture built from scratch. For example, Figure 3 As shown, the HR2 sequence of RSV with some words masked is input into the Transformer encoder to predict the masked words.

[0038] Step S104, based on the prediction result output by the polypeptide sequence modification model, determining the predicted word element of the masked part in the masked polypeptide sequence.

[0039] When there is only one masked word in the masked polypeptide sequence, the output of the polypeptide sequence modification model is N probability values, where N is the total number of amino acid residue words (the total number does not include the number of masked words and other special words (such as completion words, etc.)). For example, Figure 2 The word-gram table shown is all the words, and the total number of words other than the masked words and the completed words is 20. The N probability values ​​output by the polypeptide sequence transformation model correspond to N words, and each probability value indicates that the masked word in the reference polypeptide sequence is the probability value of the corresponding word. The word corresponding to the maximum probability value can be used as the predicted word of the polypeptide sequence transformation model, that is, the word corresponding to the maximum probability value is the masked word predicted by the polypeptide sequence transformation model. It is also possible not to use the word corresponding to the maximum probability value as the predicted word, but to use the word corresponding to other probability values ​​as the predicted word, for example, to use the word corresponding to the second largest probability value as the predicted word.

[0040] When there are multiple masked words in the masked polypeptide word sequence, the number of probability values ​​output by the polypeptide sequence transformation model is M×N, where M is the number of masked words in the masked polypeptide word sequence.

[0041] Step S105, replacing the masked part of the word in the reference polypeptide sequence with the predicted word to obtain a predicted polypeptide sequence.

[0042] In the embodiment of the present invention, the predicted word may be the same as the original word that is masked in the reference polypeptide sequence, or may be different.

[0043] Step S106, if the predicted polypeptide sequence is a polypeptide sequence that is different from the existing polypeptide sequence, the predicted polypeptide sequence is used as the modified polypeptide sequence. Specifically, the existing polypeptide sequences can be collected in advance, and then the predicted polypeptide sequence is compared with the collected existing polypeptide sequences one by one to determine whether the predicted polypeptide sequence is the same as the existing polypeptide sequence. The predicted polypeptide sequence that is the same as the existing polypeptide sequence is discarded to ensure the novelty of the modified polypeptide sequence.

[0044] If the predicted word predicted by the polypeptide sequence transformation model is the original word at the masked position in the reference polypeptide sequence, then the predicted polypeptide sequence obtained is the existing polypeptide sequence. Only when the predicted word predicted by the polypeptide sequence transformation model is not the original word at the masked position in the reference polypeptide sequence can the modified polypeptide sequence be obtained. Of course, if there are multiple words in the reference polypeptide sequence that are masked, then even if there are multiple predicted words that are the same as the corresponding original words, as long as there is one predicted word that is different from the corresponding original word, the predicted polypeptide sequence obtained is different from the reference polypeptide sequence, and it is possible that it is not an existing polypeptide sequence.

[0045] In addition, if the word corresponding to the maximum probability value in the probability value output by the polypeptide sequence modification model is the same as the original word at the masked position in the reference polypeptide sequence, then the word corresponding to the second largest probability value can be used as the predicted word, and then the masked part of the word in the reference polypeptide sequence is replaced with the predicted word to obtain the predicted polypeptide sequence.

[0046] In some optional specific embodiments, there are multiple reference polypeptide sequences, and there are multiple corresponding covering polypeptide sequences; the polypeptide sequence modification model is based on the multiple covering polypeptide sequences, and the predicted polypeptide sequences are multiple.

[0047] That is, multiple predicted polypeptide sequences can be obtained based on multiple reference polypeptide sequences. If multiple predicted polypeptide sequences are all different from the existing polypeptide sequences, multiple modified polypeptide sequences can be obtained.

[0048] When there are multiple reference polypeptide sequences, one or more tokens in the reference polypeptide sequences are first masked using masking tokens to obtain multiple masked polypeptide sequences (one masked polypeptide sequence is obtained from one reference polypeptide sequence). Then, the multiple masked polypeptide sequences are sequentially input into the polypeptide sequence modification model to predict the masked tokens. Finally, the predicted tokens (i.e., predicted tokens) are used to replace the masking tokens in the corresponding masked polypeptide sequences, and multiple predicted polypeptide sequences can be obtained.

[0049] In addition, based on one of the masked polypeptide sequences, the polypeptide sequence modification model predicts multiple predicted polypeptide sequences. That is, the polypeptide sequence modification model can predict multiple predicted polypeptide sequences based on one of the masked polypeptide sequences.

[0050] For example, if the polypeptide sequence modification model outputs N probability values corresponding to all tokens, then the N probability values can be sorted in descending order, and then the first M (M < N) probability values are taken. The M tokens corresponding to these M probability values are all used as predicted tokens. Finally, these M tokens are respectively used to replace the masked tokens in the reference polypeptide sequence to obtain M predicted polypeptide sequences. The predicted polypeptide sequences that are different from the existing polypeptide sequences among these M predicted polypeptide sequences are the modified polypeptide sequences. That is, multiple modified polypeptide sequences can be obtained based on one reference polypeptide sequence.

[0051] In the embodiments of the present invention, the relationship between the input of the polypeptide sequence modification model and the modified polypeptide sequence can be one-to-one, that is, one input sequence (i.e., the reference polypeptide sequence) can be modified to obtain one polypeptide sequence. The relationship between the input of the polypeptide sequence modification model and the modified polypeptide sequence can also be one-to-many, that is, one input sequence (i.e., the reference polypeptide sequence) can be modified to obtain multiple polypeptide sequences. The relationship between the input of the polypeptide sequence modification model and the modified polypeptide sequence can also be many-to-many, that is, multiple input sequences (i.e., the reference polypeptide sequences) can be modified to obtain multiple polypeptide sequences.

[0052] In some optional specific embodiments, after the predicted polypeptide sequence is used as the modified polypeptide sequence if it is a polypeptide sequence that is different from all existing polypeptide sequences, the following is further included: Inputting the sequence information of the modified polypeptide sequence, the sequence information of the target receptor, as well as the structural image of the modified polypeptide sequence and the structural image of the target receptor into the first multi-modal deep learning model to predict the activity information of the modified polypeptide sequence; and / or The sequence information of the modified polypeptide sequence and the sequence information of the target receptor, as well as the structural image of the modified polypeptide sequence and the structural image of the target receptor are input into the second multimodal deep learning model to predict the toxicity information of the modified polypeptide sequence.

[0053] In some specific implementations, such as Figure 4 As shown, both the first multimodal deep learning model and the second multimodal deep learning model include a Star-Transformer encoder, a convolutional neural network, a feature fusion layer, and a fully connected layer; The Star-Transformer encoder is used to output first feature information based on the sequence information of the transformed polypeptide sequence and the sequence information of the target receptor; The convolutional neural network is used to output second feature information based on the structural image of the modified polypeptide sequence and the structural image of the target receptor; The feature fusion layer is used to fuse the first feature information and the second feature information to obtain fused feature information; The fully connected layer of the first multimodal deep learning model predicts the activity information of the modified polypeptide sequence based on the fused feature information, and the fully connected layer of the second multimodal deep learning model predicts the toxicity information of the modified polypeptide sequence based on the fused feature information.

[0054] In an embodiment of the present invention, the architecture of the first multimodal deep learning model and the second multimodal deep learning model can be the same, both including a Star-Transformer encoder and a convolutional neural network (CNN). The first multimodal deep learning model is used to predict the activity information of the modified polypeptide sequence, and the second multimodal deep learning model is used to predict the toxicity information of the modified polypeptide sequence.

[0055] Specifically, when predicting the activity information of the modified polypeptide sequence, the sequence information of the modified polypeptide sequence and the sequence information of the target receptor are input into the Star-Transformer encoder of the first multimodal deep learning model, and the structural image of the modified polypeptide sequence and the structural image of the target receptor are input into the convolutional neural network of the first multimodal deep learning model. The output of the Star-Transformer encoder and the output of the convolutional neural network are merged and input into the fully connected layer to predict the activity information of the modified polypeptide sequence.

[0056] When predicting the toxicity information of the modified polypeptide sequence, the sequence information of the modified polypeptide sequence and the sequence information of the target receptor are input into the Star-Transformer encoder of the second multimodal deep learning model, and the structural image of the modified polypeptide sequence and the structural image of the target receptor are input into the convolutional neural network of the second multimodal deep learning model. The output of the Star-Transformer encoder and the output of the convolutional neural network are merged and input into the fully connected layer to predict the toxicity information of the modified polypeptide sequence.

[0057] The structural image of the modified polypeptide sequence can be predicted using relevant techniques.

[0058] Although the architecture of the first multimodal deep learning model and the second multimodal deep learning model can be the same, they need to be trained and saved separately because the predicted information is different. During the training process, for the structural image samples of peptides and receptors of the convolutional neural network, multiple data enhancement methods can be used, including but not limited to image rotation, random cropping of receptor images, changing image colors, etc., to improve the generalization of the model.

[0059] In an embodiment of the present invention, after the reference polypeptide sequence is modified using the polypeptide sequence modification model to obtain the modified polypeptide sequence, the first multimodal deep learning model and the second multimodal deep learning model can also be used to screen the activity and / or toxicity of the modified polypeptide sequence to screen out modified polypeptide sequences with high activity and low toxicity.

[0060] In some optional specific embodiments, the training process of the polypeptide sequence modification model is: Obtaining sample peptide sequences; Using the masked word to mask some words in the sample polypeptide sequence to obtain a masked sample polypeptide sequence; The masked sample polypeptide sequence is used as the input of the polypeptide sequence transformation model to be trained, and the masked part of the word in the sample polypeptide sequence is used as the training label to train the polypeptide sequence transformation model.

[0061] In the embodiment of the present invention, the number of sample polypeptide sequences used to train the polypeptide sequence transformation model is relatively large, and the multiple sample polypeptide sequences can be divided into a training set, a test set, and a validation set. The multiple sample polypeptide sequences may include the above-mentioned reference polypeptide sequence, or may not include the above-mentioned reference polypeptide sequence.

[0062] For example, all of the 119 active and non-toxic RSV polypeptide reference sequences described above may be used as sample polypeptide sequences, some may be used as sample polypeptide sequences, or none of them may be used as sample polypeptide sequences.

[0063] In an embodiment of the present invention, the masked sample polypeptide sequence is used as the input of the polypeptide sequence transformation model to be trained. Of course, the masked sample polypeptide sequence can also be first divided into word units, and then the word units obtained by the division are replaced with corresponding numerical values ​​to obtain a numerical masked sample polypeptide sequence as the input of the polypeptide sequence transformation model to be trained. Then, based on the output of the model, the word units of the masked part predicted by the model, that is, the predicted word units, are obtained. Finally, the original word units at the masked position in the sample polypeptide sequence are used as labels to calculate the loss of the model. The training process such as the loss calculation method and the adjustment process of the model parameters can be implemented by selecting appropriate related technologies as needed, which will not be described in detail here.

[0064] In some optional specific embodiments, at least part of the word-meta coverage parameters of the sample polypeptide sequence are different from the word-meta coverage parameters of the reference polypeptide sequence; wherein the word-meta coverage parameters include: at least one of a word-meta coverage ratio, a word-meta coverage number, and a word-meta coverage position; and / or, The length of at least a portion of the sample polypeptide sequence is different from the length of the reference polypeptide sequence.

[0065] The polypeptide sequence modification model in the embodiment of the present invention has a strong generalization ability. Therefore, the masking method (including quantity, position, etc.) of the reference polypeptide sequence input during the polypeptide sequence modification process can be different from the masking method of the training sample during model training, which facilitates the adjustment of the masking method of the reference polypeptide sequence during the polypeptide sequence modification process.

[0066] The artificial intelligence-based polypeptide sequence modification method provided in this embodiment utilizes polypeptide backbone modification based on an existing polypeptide sequence (i.e., a reference polypeptide sequence) to generate a new polypeptide sequence (i.e., a modified polypeptide sequence) and explore the possibility of replacing different amino acid residues, thereby improving the quality and speed of polypeptide design.

[0067] The embodiment of the present invention also uses deep learning to screen the activity and toxicity of the modified polypeptide sequence, which can help to transform a highly active and low-toxic polypeptide sequence, and further improve the quality and speed of polypeptide drug design.

[0068] The embodiments of the present invention utilize existing high-activity and low-toxicity polypeptide sequences and perform sequence modification and upgrading based on a deep learning model, which can increase polypeptide activity and reduce toxicity, thereby improving the quality of polypeptide design.

[0069] In the embodiments of the present invention, a reference polypeptide sequence can be transformed into many new polypeptide sequences by changing the position and number of masked words, replacing them with different predicted words, etc., and various types of polypeptide sequences can be used as reference polypeptide sequences. Therefore, a large number of new polypeptide sequences can be transformed to establish a polypeptide transformation library. Subsequently, the desired new polypeptide sequences can be screened out from the polypeptide transformation library, greatly improving the efficiency of polypeptide drug design.

[0070] In this embodiment, a polypeptide sequence modification device based on artificial intelligence is also provided, and the device is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions thereof are omitted. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and contemplated.

[0071] This embodiment provides a polypeptide sequence modification device based on artificial intelligence, such as Figure 5 As shown, including: A reference polypeptide sequence acquisition module 501 is used to acquire a reference polypeptide sequence; A masking module 502 is used to mask some of the words in the reference polypeptide sequence using a preset masking word to obtain a masked polypeptide sequence; Prediction module 503, used for inputting the masked polypeptide sequence into a pre-trained polypeptide sequence transformation model; A predicted word unit determination module 504 is used to determine the predicted word unit of the masked part in the masked polypeptide sequence based on the prediction result output by the polypeptide sequence transformation model; A predicted polypeptide sequence acquisition module 505 is used to replace the masked word elements in the reference polypeptide sequence with the predicted word elements to obtain a predicted polypeptide sequence; The modified polypeptide sequence determination module 506 is used to use the predicted polypeptide sequence as a modified polypeptide sequence if the predicted polypeptide sequence is a polypeptide sequence different from any existing polypeptide sequence.

[0072] In some optional embodiments, the word element of the reference polypeptide sequence is an amino acid residue word element; and / or, The masked word is a set non-amino acid residue word, or a designated amino acid residue word.

[0073] In some optional implementations, the covering module 502 includes: A word unit covering setting parameter acquisition unit, used to acquire the set word unit covering parameters, wherein the word unit covering parameters include: at least one of a word unit covering ratio, a word unit covering number, and a word unit covering position; The word-meta masking unit is used to mask some words in the reference polypeptide sequence according to the set word-meta masking parameters to obtain the masked polypeptide sequence.

[0074] In some optional embodiments, the reference polypeptide sequence is multiple, and the corresponding covering polypeptide sequence is multiple; the polypeptide sequence modification model is based on the multiple covering polypeptide sequences, and the predicted polypeptide sequence is multiple; and / or, The polypeptide sequence modification model is based on one of the masked polypeptide sequences, and the predicted polypeptide sequences obtained are multiple.

[0075] In some optional embodiments, the artificial intelligence-based polypeptide sequence modification device further comprises: An activity prediction module, used for inputting the sequence information of the modified polypeptide sequence and the sequence information of the target receptor, as well as the structural image of the modified polypeptide sequence and the structural image of the target receptor into the first multimodal deep learning model to predict the activity information of the modified polypeptide sequence; and / or, The toxicity prediction module is used to input the sequence information of the modified polypeptide sequence and the sequence information of the target receptor, as well as the structural image of the modified polypeptide sequence and the structural image of the target receptor into the second multimodal deep learning model to predict the toxicity information of the modified polypeptide sequence.

[0076] In some optional embodiments, the first multimodal deep learning model and the second multimodal deep learning model both include a Star-Transformer encoder, a convolutional neural network, a feature fusion layer, and a fully connected layer; The Star-Transformer encoder is used to output first feature information based on the sequence information of the transformed polypeptide sequence and the sequence information of the target receptor; The convolutional neural network is used to output second feature information based on the structural image of the modified polypeptide sequence and the structural image of the target receptor; The feature fusion layer is used to fuse the first feature information and the second feature information to obtain fused feature information; The fully connected layer of the first multimodal deep learning model predicts the activity information of the modified polypeptide sequence based on the fused feature information, and the fully connected layer of the second multimodal deep learning model predicts the toxicity information of the modified polypeptide sequence based on the fused feature information.

[0077] In some optional embodiments, the above-mentioned artificial intelligence-based polypeptide sequence modification device further includes: A sample acquisition module, used to obtain a sample polypeptide sequence; A sample masking module, used to mask some of the words in the sample polypeptide sequence using the masking words to obtain a masked sample polypeptide sequence; The training module is used to use the masked sample polypeptide sequence as the input of the polypeptide sequence transformation model to be trained, and to train the polypeptide sequence transformation model using the masked part of the word in the sample polypeptide sequence as the training label.

[0078] In some optional embodiments, the word-meta coverage parameters of at least part of the sample polypeptide sequence are different from the word-meta coverage parameters of the reference polypeptide sequence; wherein the word-meta coverage parameters include: at least one of a word-meta coverage ratio, a word-meta coverage number, and a word-meta coverage position; and / or, The length of at least a portion of the sample polypeptide sequence is different from the length of the reference polypeptide sequence.

[0079] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0080] The artificial intelligence-based polypeptide sequence modification device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0081] The embodiment of the present invention also provides a computer device having the above Figure 5 The artificial intelligence-based polypeptide sequence modification device shown.

[0082] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0083] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0084] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0085] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0086] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0087] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0088] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device may be a touch screen.

[0089] The computer device also includes a communication interface, which is used for the computer device to communicate with other devices or a communication network.

[0090] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0091] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0092] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.< / mask> < / mask> < / pad> < / mask>

Claims

1. A method for polypeptide sequence modification based on artificial intelligence, characterized in that: The method comprises: Obtaining a reference peptide sequence; Using a preset masking word to mask some words in the reference polypeptide sequence to obtain a masked polypeptide sequence; Inputting the masked polypeptide sequence into a pre-trained polypeptide sequence transformation model; Determining predicted words of the masked portion of the masked polypeptide sequence based on the prediction results output by the polypeptide sequence modification model; Replacing the masked part of the word in the reference polypeptide sequence with the predicted word to obtain a predicted polypeptide sequence; If the predicted polypeptide sequence is a polypeptide sequence that is different from any existing polypeptide sequence, the predicted polypeptide sequence is used as the modified polypeptide sequence.

2. The method according to claim 1, characterized in that The words of the reference polypeptide sequence are amino acid residue words; and / or, The masked word is a set non-amino acid residue word, or a designated amino acid residue word.

3. The method according to claim 1, characterized in that The method of using a preset masking word to mask some words in the reference polypeptide sequence to obtain a masked polypeptide sequence includes: Acquire a set word unit covering parameter, wherein the word unit covering parameter includes: at least one of a word unit covering ratio, a word unit covering number, and a word unit covering position; According to the set word-memory masking parameters, some words in the reference polypeptide sequence are masked to obtain the masked polypeptide sequence.

4. The method according to claim 1, characterized in that: There are multiple reference polypeptide sequences, and there are multiple corresponding covering polypeptide sequences; the polypeptide sequence modification model is based on the multiple covering polypeptide sequences, and the predicted polypeptide sequences are multiple; and / or, The polypeptide sequence modification model is based on one of the masked polypeptide sequences, and the predicted polypeptide sequences obtained are multiple.

5. The method according to claim 1, characterized in that If the predicted polypeptide sequence is a polypeptide sequence different from the existing polypeptide sequence, after taking the predicted polypeptide sequence as the modified polypeptide sequence, the method further comprises: Inputting the sequence information of the modified polypeptide sequence and the sequence information of the target receptor, as well as the structural image of the modified polypeptide sequence and the structural image of the target receptor into a first multimodal deep learning model to predict the activity information of the modified polypeptide sequence; and / or, The sequence information of the modified polypeptide sequence and the sequence information of the target receptor, as well as the structural image of the modified polypeptide sequence and the structural image of the target receptor are input into the second multimodal deep learning model to predict the toxicity information of the modified polypeptide sequence.

6. The method according to claim 5, characterized in that The first multimodal deep learning model and the second multimodal deep learning model both include a Star-Transformer encoder, a convolutional neural network, a feature fusion layer, and a fully connected layer; The Star-Transformer encoder is used to output first feature information based on the sequence information of the transformed polypeptide sequence and the sequence information of the target receptor; The convolutional neural network is used to output second feature information based on the structural image of the modified polypeptide sequence and the structural image of the target receptor; The feature fusion layer is used to fuse the first feature information and the second feature information to obtain fused feature information; The fully connected layer of the first multimodal deep learning model predicts the activity information of the modified polypeptide sequence based on the fused feature information, and the fully connected layer of the second multimodal deep learning model predicts the toxicity information of the modified polypeptide sequence based on the fused feature information.

7. The method according to claim 1, characterized in that The training process of the polypeptide sequence transformation model is as follows: Obtaining sample peptide sequences; Using the masked word to mask some words in the sample polypeptide sequence to obtain a masked sample polypeptide sequence; The masked sample polypeptide sequence is used as the input of the polypeptide sequence transformation model to be trained, and the masked part of the word in the sample polypeptide sequence is used as the training label to train the polypeptide sequence transformation model.

8. The method according to claim 7, characterized in that The word-meta coverage parameters of at least part of the sample polypeptide sequence are different from the word-meta coverage parameters of the reference polypeptide sequence; wherein the word-meta coverage parameters include: at least one of a word-meta coverage ratio, a word-meta coverage number, and a word-meta coverage position; and / or, The length of at least a portion of the sample polypeptide sequence is different from the length of the reference polypeptide sequence.

9. A polypeptide sequence modification device based on artificial intelligence, characterized in that: The device comprises: A reference polypeptide sequence acquisition module, used to acquire a reference polypeptide sequence; A masking module, used to mask some words in the reference polypeptide sequence using preset masking words to obtain a masked polypeptide sequence; A prediction module, used for inputting the masked polypeptide sequence into a pre-trained polypeptide sequence transformation model; A predicted word unit determination module, used to determine the predicted word unit of the masked part in the masked polypeptide sequence based on the prediction result output by the polypeptide sequence transformation model; A predicted polypeptide sequence acquisition module, used to replace the masked part of the word element in the reference polypeptide sequence with the predicted word element to obtain a predicted polypeptide sequence; The modified polypeptide sequence determination module is used to use the predicted polypeptide sequence as the modified polypeptide sequence if the predicted polypeptide sequence is a polypeptide sequence different from any existing polypeptide sequence.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the artificial intelligence-based polypeptide sequence modification method according to any one of claims 1 to 8 by executing the computer instructions.

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