Construction and training method and device of siRNA gene inhibition efficiency screening model and screening method

Abstract

The invention provides a siRNA gene inhibition efficiency screening model construction and training method, a siRNA gene inhibition efficiency screening device and a siRNA gene inhibition efficiency screening method. The construction and training method comprises the following steps: acquiring first sequence information of target mRNA and second sequence information of siRNA; obtaining first space image information of the target mRNA and second space image information of the siRNA; constructing a siRNA gene inhibition efficiency screening model, and training a first neural network model by using the first sequence information and the second sequence information to extract sequence features; training a second neural network model by using the first spatial image information and the second spatial image information to extract spatial structure features; and fusing the sequence features and the spatial structure features, and training a third neural network model by using the fused features. Therefore, the accuracy of screening the siRNA with the inhibition efficiency on the target gene can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of siRNA design, and specifically to the construction of a siRNA gene inhibition efficiency screening model and a training method, device and screening method. Background Art

[0002] siRNA can participate in the RNA interference (RNAi) phenomenon to regulate gene expression, and can be used to treat diseases such as hypercholesterolemia, hypertension, atherosclerosis, and viral infections, and has excellent prospects in drug development. In the field of siRNA design, it is currently mainly based on the target gene that researchers want to silence and their own experience to manually design siRNA. Due to the limitations of the researchers' own experience, it is easy to miss siRNAs with high efficiency in inhibiting the target gene when designing siRNA.

[0003] Moreover, currently, the screening of siRNA needs to be realized more through experimental verification methods, for example, the Western blotting method is used to determine the target gene inhibition efficiency of siRNA to screen siRNA, and the screening efficiency is low. Therefore, how to establish an efficient and accurate screening method from siRNA design to screening siRNA with inhibition efficiency on the target gene has become an urgent problem to be solved. Summary of the invention

[0004] In view of the above technical problems existing in the prior art, this application is proposed. This application aims to provide a construction and training method, device and screening method of a siRNA gene inhibition efficiency screening model, which can improve the screening efficiency of siRNA with targeted gene inhibition efficiency, comprehensively screen siRNA, and improve the screening accuracy while improving the screening efficiency, so as to ensure that the screened siRNA has a high targeted gene inhibition efficiency.

[0005] According to the first scheme of the present application, a method for constructing and training a siRNA gene inhibition efficiency screening model is provided, the method comprising: obtaining first sequence information of a target mRNA and second sequence information of a siRNA obtained based on the target mRNA; obtaining first spatial image information of the target mRNA and second spatial image information of the siRNA, the first spatial image information and the second spatial image information being used to characterize the spatial structures of the target mRNA and the siRNA, respectively; constructing a siRNA gene inhibition efficiency screening model, the siRNA gene inhibition efficiency screening model comprising a first neural network model, a second neural network model and a third neural network model; training the first neural network model using the first sequence information and the second sequence information to extract sequence features; training the second neural network model using the first spatial image information and the second spatial image information to extract spatial structure features; fusing the sequence features and the spatial structure features, and training the third neural network model using the fused features, so that the screening accuracy of the siRNA gene inhibition efficiency screening model is higher than a threshold.

[0006] According to the second scheme of the present application, a device for constructing and training a siRNA gene inhibition efficiency screening model is provided, comprising an interface, which is configured to: obtain first sequence information of a target mRNA and second sequence information of a siRNA obtained based on the target mRNA; obtain first spatial image information of the target mRNA and second spatial image information of the siRNA, wherein the first spatial image information and the second spatial image information are used to characterize the spatial structures of the target mRNA and the siRNA, respectively; and at least one processor, which is configured to implement the method for constructing and training a siRNA gene inhibition efficiency screening model as described in each embodiment of the present application.

[0007] According to the third scheme of the present application, a screening method for screening siRNA with targeted gene inhibition efficiency is provided, the screening method comprising: based on the principle of base complementarity, traversing the target mRNA with a preset walking interval length and a preset walking coverage interval length to obtain a siRNA sequence library combined with the target mRNA sequence; using the construction and training device of the siRNA gene inhibition efficiency screening model described in the embodiment of the present application to predict the targeted gene inhibition efficiency of the siRNA in the siRNA sequence library, so as to screen out siRNA whose targeted gene inhibition efficiency is greater than the set inhibition efficiency.

[0008] According to the fourth scheme of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the construction and training methods of the siRNA gene inhibition efficiency screening model as described in the various embodiments of the present application.

[0009] According to the fifth embodiment of the present application, a computer program product is provided, comprising computer instructions for causing a computer to execute the construction and training methods of the siRNA gene inhibition efficiency screening model as described in the various embodiments of the present application.

[0010] Compared with the prior art, the beneficial effects of the embodiments of the present application are: The construction and training method of the siRNA gene inhibition efficiency screening model provided in the embodiment of the present application is based on the first sequence information of the target mRNA and the second sequence information of the siRNA, the first neural network model is trained to extract sequence features, based on the first spatial image information of the target mRNA and the second spatial image information of the siRNA, the second neural network model is trained to extract spatial structure features, and the sequence features and the spatial structure features are fused to train the third neural network model to achieve the prediction of the inhibition efficiency of the targeted gene. In this way, it is possible to realize the automatic and efficient screening of siRNA with targeted inhibition efficiency using the neural network model, greatly improving the screening efficiency of siRNA, thereby reducing the time for siRNA drug development. In addition, in the process of intelligent screening of siRNA, the sequence information and spatial image information of RNA are feature fused, so as to comprehensively consider the influence of spatial structure on inhibition efficiency, thereby improving the accuracy of predicting the inhibition efficiency of the targeted gene, thereby ensuring the quality of siRNA drug development.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above description and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In the drawings, which are not necessarily drawn to scale, the same reference numerals may describe similar parts in different views. Similar reference numerals with letter suffixes or different letter suffixes may represent different examples of similar components. The accompanying drawings generally illustrate various embodiments by way of example and not by way of limitation, and together with the description and claims, are used to illustrate the disclosed embodiments. Such embodiments are illustrative and exemplary and are not intended to be exhaustive or exclusive embodiments of the present method or non-transitory computer-readable medium having instructions for implementing the method.

[0013] Figure 1 A schematic diagram of the process of constructing and training a siRNA gene inhibition efficiency screening model according to an embodiment of the present application is shown.

[0014] Figure 2 A schematic diagram showing relevant information of some target mRNAs and siRNAs according to an embodiment of the present application is shown.

[0015] Figure 3 A schematic diagram of a siRNA gene inhibition efficiency screening model according to an embodiment of the present application is shown.

[0016] Figure 4 A schematic diagram of the structure of a siRNA gene inhibition efficiency screening model and a training device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the technical solution of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific implementation examples. The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings and specific implementation examples, but are not intended to limit the present application.

[0018] The words "first", "second" and similar words used in this application do not indicate any order, quantity or importance, but are only used to distinguish. The words "include" or "comprises" and similar words used in this application mean that the elements before the word include the elements listed after the word, and do not exclude the possibility of covering other elements. In this application, the arrows shown in the figures of each step are only examples of the execution order, not limitations. The technical solution of this application is not limited to the execution order described in the embodiments. The various steps in the execution order can be combined, decomposed, or the order can be changed, as long as the logical relationship of the execution content is not affected.

[0019] All terms (including technical terms or scientific terms) used in this application have the same meaning as those understood by ordinary technicians in the field to which this application belongs, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined here. Techniques and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, the techniques and equipment should be considered as part of the specification.

[0020] Figure 1A flowchart of the construction and training method of the siRNA gene inhibition efficiency screening model according to the embodiment of the present application is shown. Specifically, a processor can be used to execute the method shown in steps S101 to S106. In this embodiment, the arrows shown in the figure for each step are only examples of the execution order, not limitations. The technical solution of the present application is not limited to the execution order described in the embodiment. The steps in the execution order can be combined, decomposed, or the order can be changed, as long as the logical relationship of the execution content is not affected.

[0021] The targeted gene inhibition efficiency of siRNA refers to the efficiency of inhibiting the transcription or translation of the gene during the expression of the targeted gene. Specifically, the inhibition efficiency reflects the degree of inhibition of the target gene mRNA or protein level by siRNA. If siRNA can effectively degrade the mRNA of the target gene, then the inhibition efficiency is relatively high. The inhibition efficiency is usually expressed as a percentage. The higher the value, the stronger the targeting effect of siRNA, and the more effectively it can reduce the expression of the target gene.

[0022] like Figure 1As shown, in step S101, first sequence information of a target mRNA and second sequence information of a siRNA obtained based on the target mRNA are obtained. Wherein, the target mRNA and siRNA can both come from a data set comprising mRNA and siRNA. For example, the data set source can be a plurality of references, for example, Huesken D, Lange J, Mickanin C, et al. Design of a genome-wide siRNA library using an artificial neural network[J];Nature biotechnology, 2005, 23(8): 995-1001;Reynolds A, Leake D,Boese Q, et al. Rational siRNA design for RNA interference[J];Nature biotechnology, 2004, 22(3): 326-330;Vickers TA, Koo S, Bennett CF, et al.Efficient reduction of target RNAs by small interfering RNA and RNase H-dependent antisense agents: a comparative analysis[J];Journal of Biological Chemistry, 2003, 278(9): 7108-7118;Harborth J, Elbashir SM, Vandenburgh K, et al. Sequence, chemical, and structural variation of small interfering RNAsand short hairpin RNAs and the effect on mammalian gene silencing[J]; Antisense and Nucleic Acid Drug Development, 2003, 13(2): 83-105; Ui‐Tei K,Naito Y, Takahashi F, et al.Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference[J]; Nucleic acids research, 2004, 32(3): 936-948; Ichihara M, Murakumo Y, Masuda A, et al. Thermodynamic instability of siRNA duplex is a prerequisite for dependableprediction of siRNA activities[J]; Nucleic acids research, 2007, 35(18): e123. like. Figure 2 As shown in the figure, the data set contains detailed information such as siRNA ID, siRNA sequence, mRNA sequence and inhibition rate (efficacy). Figure 2 The data set shown in FIG. 1 is only a part of the data set for exemplary description, and does not constitute the entire data set. Figure 2 The siRNA and mRNA shown in the figure are merely exemplary and do not constitute a specific limitation on the target mRNA and siRNA.

[0023] In this embodiment, the first sequence information of the target mRNA refers to the gene mRNA sequence that you want to interfere with or inhibit. In other embodiments, it can be obtained through gene databases such as NCBI, Ensembl, etc. The first sequence information is used to characterize the target mRNA sequence, and the specific representation method of the first sequence information is not limited. For example, it can be digital information obtained by word unit conversion of the base, or other representation methods, as long as it can be used to characterize the target mRNA sequence. Among them, the second sequence information is used to characterize the siRNA sequence designed based on the target mRNA. The specific representation method of the second sequence information is not limited. For example, it can be digital information obtained by word unit conversion of the base, or other representation methods, as long as it can be used to characterize the siRNA sequence.

[0024] That is, in some other embodiments of the present application, the sequence information includes digital information obtained by performing word-unit conversion based on bases in RNA, and the word-unit conversion includes: mapping different bases to different digital information, or combining different bases in a preset manner to obtain different word-unit combinations, and mapping different word-unit combinations to different digital information. In this embodiment, RNA includes target mRNA and siRNA.

[0025] "Term" refers to the smallest unit of a gene sequence. In this embodiment, term segmentation refers to breaking down siRNA and mRNA sequences into small fragments or units, which are usually single bases (such as A, U, G, C) or base pairs of RNA. For mRNA sequences, they may be segmented into base pairs (for example, "AU", "GC", etc.) according to the characteristics of RNA. For siRNA sequences, they may also be segmented into similar base pairs or short sequence fragments. There is no specific limitation on this.

[0026] Specifically, as an implementation method, the four bases in RNA, such as A, G, C, and U, can be directly used as four word units, and then the bases A, G, C, and U can be mapped to four numbers respectively. For example, base A can be mapped to number 1, base U can be mapped to number 2, base T (DNA base) can also be mapped to number 2, base C can be mapped to number 3, and base G can be mapped to number 4, indicating special characters for filling. <pad>Mapped to number 0, ie {' <pad>':0,'A':1,'U':2,'T':2,'C':3,'G':4}.

[0027] In addition, byte pair encoding can be used to merge words and map the merged words to numbers. Byte Pair Encoding (BPE) is a commonly used algorithm in natural language processing. Its core idea is to compress words or sequences into a more compact form by repeatedly merging the most frequently occurring character pairs.

[0028] For example, first, the siRNA and mRNA sequences are segmented into tokens according to the base bases (or base pairs). For example, the mRNA sequence "AUCG" can be segmented into ['A', 'U', 'C', 'G']. The frequencies of occurrence of these tokens are counted. For example, a certain base pair (such as "AU") appears very frequently in the entire sequence. The two base pairs with the highest frequency are merged into a new token. For example, if "AU" and "CG" are the most common, then they are merged into a new token "AUCG". Other tokens are merged according to frequency until the preset number of mergers is reached or the number of tokens obtained is sufficient. Through BPE, RNA sequences are gradually transformed from fragments composed of single bases into some higher-level synthetic tokens, which represent common patterns or structures of RNA sequences. After converting RNA sequences into "tokens" through methods such as BPE, these tokens need to be further mapped to numbers for use in machine learning or deep learning models.

[0029] In this way, the first sequence information and the second sequence information are obtained respectively by processing and converting the target mRNA and siRNA sequences, so that the first sequence information and the second sequence information can be used in the neural network model in a digitally represented manner.

[0030] Returning to the embodiment of the present application, in step S102, first spatial image information of the target mRNA and second spatial image information of the siRNA are obtained, and the first spatial image information and the second spatial image information are used to characterize the spatial structures of the target mRNA and siRNA, respectively.

[0031] Specifically, the spatial image information of siRNA and target mRNA can be obtained based on an artificial intelligence model or experiment. In a specific embodiment, the artificial intelligence tool HelixFold 3 can be used to predict the spatial structure of siRNA and target mRNA. For specific implementation methods, please refer to the document Liu L, Zhang S, Xue Y, et al. Technical Report of HelixFold3 for Biomolecular Structure Prediction[J].arXiv preprint arXiv:2408.16975, 2024.

[0032] This is only an exemplary description and does not constitute a limitation on the specific scheme.

[0033] For example, the spatial structure of the target mRNA can also be analyzed by experimental methods, or other prediction tools such as RNAfold, RNAstructure, etc. can be used to predict the spatial structure. Of course, the spatial structure of siRNA can also be obtained from RNA databases (such as RNAstrand, PDB), or its spatial structure can be predicted using tools (such as 3D RNA).

[0034] Exemplarily, PyMOL Open-Source 2.5.0 can be used to convert the spatial structure of RNA into spatial image information, or the geometric information of the spatial structure of RNA (such as curvature, angle, hydrogen bond, etc.) can be extracted into a numerical feature vector, or its spatial image information can be presented in PDB format, structure vector, etc., without specific limitation, as long as the first spatial image information and the second spatial image information can respectively characterize the spatial structure of the target mRNA and siRNA, and can be analyzed and processed by the neural network model.

[0035] In the embodiment of the present application, in step S103, a siRNA gene inhibition efficiency screening model is constructed, and the siRNA gene inhibition efficiency screening model includes a first neural network model, a second neural network model and a third neural network model. In step S104, the first neural network model is trained using the first sequence information and the second sequence information to extract sequence features; in step S105, the second neural network model is trained using the first spatial image information and the second spatial image information to extract spatial structure features.

[0036] That is, in this embodiment, the siRNA gene inhibition efficiency screening model includes using a first neural network model to extract sequence features based on first sequence information and second sequence information, and using a second neural network model to extract spatial structure features based on first spatial image information and second spatial image information.

[0037] When the first sequence information and the second sequence information are input into the first neural network model, the input order of the first sequence information and the second sequence information can be limited. For example, the first sequence information can be in front and the second sequence information can be in the back. After the front and back splicing operations are performed, they are input into the first neural network model, which is conducive to the first neural network model to quickly extract sequence features and improve the screening efficiency of siRNA by the siRNA gene inhibition efficiency screening model.

[0038] In some other embodiments of the present application, the second neural network model is one or two. Specifically, Figure 3 , the first neural network model is Star Transformer 301. RNA sequences usually have a long sequence length, and the dependencies between sequences are relatively complex. Traditional neural networks or RNNs (such as LSTM) may have gradient vanishing or gradient explosion problems when processing long sequences, making it difficult to capture long-range dependencies. Star Transformer301, as a variant of the Transformer network, can process long sequence data more effectively and avoid the above problems. Star Transformer 301 can better model the relationship between different row positions in RNA sequences through the self-attention mechanism, including potential secondary structure dependencies, which is crucial for tasks such as RNA function prediction, RNA-protein interactions, and siRNA design. Through training, Star Transformer 301 can extract meaningful sequence features from RNA sequences and effectively capture complex local and global information in RNA sequences.

[0039] In some other embodiments, when there is only one second neural network model, the second neural network model is trained based on the first spatial image information and the second spatial image information. That is, when there is only one second neural network model, the first spatial image information and the second spatial image information are spliced ​​and input into the second neural network model to extract the spatial structural features of RNA.

[0040] In the case where there are two second neural network models, one of the second neural network models is trained based on the first spatial image information, and the other of the second neural network models is trained based on the second spatial image information. Figure 3 As shown, the second neural network model includes Swin Transformer 1 302 and Swin Transformer 2 303, wherein Swin Transformer 1 302 is trained based on the first spatial image information to extract the spatial structural features of mRNA, and Swin Transformer 2 303 is trained based on the second spatial image information to extract the spatial structural features of siRNA. Swin Transformer 1 302 and Swin Transformer 2 303, whose inputs are the target mRNA and siRNA spatial images, respectively, cut the target mRNA and siRNA spatial images into different windows, and move the windows to interact and calculate the attention of the windows, and there is overlap between the windows. By cutting the image into multiple small windows, Swin Transformer only calculates self-attention within the local window without performing global calculations. The size of each window can be adjusted according to the task requirements, and the windows are overlapped to promote information exchange, which means that the features within and between windows can be transmitted and interacted through the attention mechanism. By translating the window at different levels, the Swin Transformer model can capture the connections between different positions, enabling the Swin Transformer model to retain local details while extracting global contextual information when processing large images.

[0041] The overlapping design of windows is crucial for the model to extract image features, especially when processing long sequence data. Overlapping windows can strengthen information transmission and ensure information sharing across windows. When each window calculates self-attention, it participates in the calculation together with the overlapping parts of adjacent windows. In this way, the local window not only pays attention to the features of its own area, but also obtains supplementary global information from adjacent windows.

[0042] By using masks inside or between windows, the computational efficiency of windows can be improved through masks, and relative position encoding can be used instead of absolute position encoding to improve model performance.

[0043] In this way, the efficiency of analyzing and processing spatial image information can be greatly improved, and the accuracy of extracting spatial structural features can be improved, which is conducive to improving the accuracy of the siRNA gene inhibition efficiency screening model in screening siRNA with targeted gene inhibition efficiency.

[0044] Swin Transformer uses a local window mechanism, which enables it to effectively capture local spatial structural features when processing image or structural data, while integrating information globally. Swin Transformer can better capture the folding, interaction and spatial structural features of RNA in space. Moreover, the second neural network model Swin Transformer is used to process spatial image information, which has higher computational efficiency, can capture the multi-scale spatial structural features of RNA, and improve the overall prediction accuracy. That is to say, in some other embodiments of the present application, the first neural network model is a Star Transformer model, the second neural network model is a Swin Transformer model, and the third neural network model is a DPCNN (Deep Pyramid Convolutional Neural Network) model.

[0045] like Figure 3 , the output result of Star Transformer 301 is input to MLP Head (Multi-layer Perceptron Head), and the output results of Swin Transformer 1 302 and Swin Transformer 2 303 are merged and also input to MLP Head, and MLP Head is used to further extract and integrate the data processed by Transformer, and input the integrated output result to DPCNN 304 for feature fusion, so as to predict the siRNA gene inhibition efficiency based on the fused fusion features. That is, in step S106, the sequence feature and the spatial structure feature are fused, and the fused fusion features are used to train the third neural network model, so that the screening accuracy of the siRNA gene inhibition efficiency screening model is higher than the threshold.

[0046] The construction and training method of the siRNA gene inhibition efficiency screening model described in the embodiment of the present application fuses sequence features and spatial structure features for predicting siRNA gene inhibition efficiency, which can improve prediction accuracy while improving prediction efficiency and ensure the quality of the screened siRNA. siRNA (small interfering RNA) guides the RISC complex to cut mRNA by base pairing with the target mRNA, thereby achieving gene silencing. The spatial structure of siRNA and mRNA has an important influence on the efficiency of targeted gene inhibition. For example, the spatial structure of the target mRNA (such as a stem-loop structure) may mask the target sequence, making it difficult for siRNA to bind to it. In the process of training the siRNA gene inhibition efficiency screening model, the present application fully considers the spatial structure information of RNA, fuses the sequence features and spatial structure features to train the siRNA gene inhibition efficiency screening model, which can improve the comprehensiveness of the prediction factors and thus improve the accuracy of the prediction of the targeted gene inhibition efficiency.

[0047] In this embodiment, the siRNA gene inhibition efficiency screening model is trained, and the siRNA gene inhibition efficiency screening model with the best predicted effect is saved, so that the siRNA gene inhibition efficiency screening model with the best predicted effect can be used to screen siRNA. Not only is the screening efficiency high, but the accuracy is also high, thereby avoiding the omission of siRNA with targeted gene inhibition efficiency.

[0048] In some other embodiments of the present application, based on the principle of base complementarity, the target mRNA is traversed with a preset walking interval length and a preset walking coverage interval length to obtain a siRNA sequence library combined with the target mRNA sequence. The trained siRNA gene inhibition efficiency screening model is used to predict the targeted gene inhibition efficiency of siRNA in the siRNA sequence library, so that efficient screening of siRNA with excellent targeted gene inhibition efficiency in the siRNA sequence library can be achieved, and the screening accuracy is high, and the problem of siRNA with excellent targeted gene inhibition efficiency being missed will not occur.

[0049] Among them, the preset walking interval length can represent the number of bases skipped each time when sliding on the target mRNA. For example, if the walking interval length is set to 10, each time it will start from the first base in the mRNA sequence, slide 10 bases to the right, and then select the next position to start a new slide.

[0050] The preset walking coverage interval length can represent the area length of the target mRNA covered when designing siRNA. For example, each siRNA is set to cover an area of ​​20 bases in length. By setting the walking interval length and the coverage interval length, you can slide on the target mRNA and select multiple target areas. For each coverage interval, an siRNA sequence complementary to the target area will be designed according to the principle of base complementarity. For example, the four bases of RNA, A, G, C, and U, are directly used as four words, and the complementary words of A, G, C, and U are U, C, G, and A, respectively, that is, A and U are complementary bases, C and G are complementary bases, G and C are complementary bases, and U and A are complementary bases.

[0051] In a specific embodiment, the PCSK9 gene mRNA sequence (NCBI database sequence number NM_174936.4) is used as the input sequence, and the base T (DNA base) in the mRNA sequence is replaced with the base U (RNA base) to generate a siRNA double-stranded sequence library binding to PCSK9.

[0052] By traversing the entire target mRNA sequence, a series of siRNA sequences complementary to different regions can be obtained to form an siRNA sequence library. Compared with artificially designed siRNA, it not only improves the efficiency from design to screening of siRNA, but also ensures the comprehensiveness of the siRNA sequence obtained based on the target mRNA, avoiding the omission of siRNA with targeted inhibition efficiency, thus helping to ensure the quality of the final screened siRNA.

[0053] In some other embodiments of the present application, the siRNA in the sequence library is preliminarily screened based on one or more of the number of consecutive bases, GC content and similarity to the reference siRNA sequence, so as to predict the targeted gene inhibition efficiency of the siRNA in the sequence library after the preliminary screening using the siRNA gene inhibition efficiency screening model. Wherein, the number of consecutive bases refers to the continuity of the base sequence in the siRNA sequence. Generally speaking, the design of siRNA will avoid the appearance of long continuous repeated bases (e.g., continuous A, G, C or U), because this situation may lead to non-specific binding or reduce the stability and efficiency of siRNA. These sequences with too long continuous identical bases can be excluded in the preliminary screening. GC content refers to the ratio of G (guanine) and C (cytosine) in the siRNA sequence. For example, the GC content can be controlled to be within the threshold GC content range. Controlling the GC content can help siRNA stabilize its binding with the target mRNA, while avoiding the adverse effects of too high or too low GC content. In the preliminary screening, the efficiency of siRNA can be improved by adjusting the range of GC content. Reference siRNA sequences are some siRNA sequences that have been proven to be effective (for example, siRNAs that have been experimentally verified). By comparing the siRNA to be screened with these reference sequences, its potential inhibitory effect can be evaluated based on the similarity. If a siRNA sequence has a high similarity to the reference sequence, it may also be able to better bind to the target gene in function and exert a gene inhibitory effect. Therefore, in the initial screening, siRNAs with a high similarity to the reference siRNA sequence can be given priority.

[0054] In this way, through preliminary screening, the amount of predicted data is further reduced, thereby further improving the screening efficiency of siRNAs with inhibitory efficiency on targeted genes.

[0055] In some other embodiments of the present application, when there are two second neural network models, the spatial image shape of the target mRNA determined based on the first spatial image information is consistent with the spatial image shape of the siRNA determined based on the second spatial image information, so that the spatial image shapes of the siRNA and the target mRNA input to the second neural network model are consistent. Specifically, the spatial image shape can be reflected from three dimensions: length, width, and channel. By controlling the spatial image shapes of the siRNA and the target mRNA input to two different second neural network models to be consistent, it is beneficial to align or map the spatial image shapes of the target mRNA and siRNA, so that they can be effectively compared and predicted in the neural network. This alignment may involve normalizing the three-dimensional structures of the two to ensure that they have the same spatial features (e.g., spatial position, folding pattern, etc.). In addition, the training efficiency can be improved, and the fusion of the spatial structural features extracted by the two different second neural network models is less difficult.

[0056] In some other embodiments of the present application, when the spatial structural features of the target mRNA and siRNA are extracted using the second neural network model, the size of the image block cut from the siRNA image is larger than the size of the image block cut from the target mRNA image, and the model window size of the siRNA is larger than the model window size of the target mRNA. Since the target mRNA image contains more spatial information than the siRNA image, the size of the image block cut from the siRNA image is larger than the size of the image block cut from the target mRNA image, and the window size of the second neural network model of the siRNA is also larger than the window size of the second neural network model of the target mRNA, so as to capture more details of the local spatial structure of the siRNA. Since the spatial structure of siRNA is relatively simple, the use of larger image blocks and windows can ensure the integrity of its spatial information and improve the predictive performance of the siRNA gene inhibition efficiency screening model. For the target mRNA, due to its complex structure and long length, smaller blocks and windows help reduce over-smoothing and information loss, which is conducive to capturing subtle differences and local features in the structure.

[0057] Thus, this embodiment takes into account the differences in structural characteristics of siRNA and target mRNA. Larger image blocks and window sizes can better capture the global features of siRNA, while smaller image blocks and window sizes are suitable for processing the complex structure of target mRNA to extract more refined local features, thereby improving the prediction accuracy of the siRNA gene inhibition efficiency screening model.

[0058] Figure 4 The schematic diagram of the structure of the device for constructing and training the siRNA gene inhibition efficiency screening model according to the embodiment of the present application is shown. The device for constructing and training the siRNA gene inhibition efficiency screening model 400 includes an interface 401 and a processor 402, wherein the interface 401 is configured to obtain the first sequence information of the target mRNA and the second sequence information of the siRNA obtained based on the target mRNA; obtain the first spatial image information of the target mRNA and the second spatial image information of the siRNA, the first spatial image information and the second spatial image information are used to characterize the spatial structure of the target mRNA and the siRNA respectively; at least one processor 402 is configured to implement the method for constructing and training the siRNA gene inhibition efficiency screening model as described in each embodiment of the present application.

[0059] In this way, the neural network model can be used to automatically and efficiently screen siRNA with targeted inhibition efficiency, greatly improving the screening efficiency of siRNA, thereby reducing the time for siRNA drug development. In addition, in the process of intelligent screening of siRNA, the sequence information of RNA and the spatial image information are fused to comprehensively consider the impact of the spatial structure on the inhibition efficiency, thereby improving the accuracy of predicting the inhibition efficiency of the targeted gene, thereby ensuring the quality of siRNA drug development.

[0060] In some other embodiments of the present application, a screening method for screening siRNA with targeted gene inhibition efficiency is provided, the screening method comprising: based on the principle of base complementarity, traversing the target mRNA with a preset walking interval length and a preset walking coverage interval length to obtain a siRNA sequence library combined with the target mRNA sequence; using the construction and training device of the siRNA gene inhibition efficiency screening model described in each embodiment of the present application to predict the targeted gene inhibition efficiency of the siRNA in the siRNA sequence library, so as to screen out siRNA with a targeted gene inhibition efficiency greater than the set inhibition efficiency. The screening method provided by this embodiment not only has a high screening efficiency, but also has a high screening accuracy, which can ensure the quality of the screened siRNA.

[0061] The processor may be a processing device including one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor running other instruction sets, or a processor running a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a system on a chip (SoC), etc.

[0062] This application describes various operations or functions that may be implemented as software code or instructions or defined as software code or instructions. Such content may be source code or differential code ("increment" or "patch" code) that can be directly executed ("object" or "executable" form). Software codes or instructions may be stored in a computer-readable storage medium, and when executed, may cause a machine to perform the described functions or operations, and include any mechanism for storing information in a form accessible to a machine (e.g., a computing device, an electronic system, etc.), such as recordable or non-recordable media (e.g., read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.).

[0063] The exemplary methods described in this application can be implemented at least in part by a machine or computer. In some embodiments, a computer-readable storage medium stores computer program instructions, which, when executed by a processor, cause the processor to execute the construction and training methods of the siRNA gene inhibition efficiency screening model as described in each embodiment of this application.

[0064] The implementation of such a method may include software code, such as microcode, assembly language code, high-level language code, etc. Various software programming techniques may be used to create various programs or program modules. For example, a program portion or program module may be designed with or by means of Java, Python, C, C++, assembly language, or any known programming language. One or more of such software portions or modules may be integrated into a computer system and / or a computer-readable medium. Such software code may include computer-readable instructions for executing various methods. The software code may form a part of a computer program product or a computer program module. In addition, in an example, the software code may be tangibly stored on one or more volatile, non-temporary or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media may include, but are not limited to, hard disks, removable disks, removable optical disks (such as optical disks and digital video disks), cassettes, memory cards or memory sticks, random access memories (RAMs), read-only memories (ROMs), etc.

[0065] According to an embodiment of the present application, a computer program product is also provided, which includes computer instructions, and the computer instructions are used to enable a computer to execute the steps in the construction and training method of the siRNA gene inhibition efficiency screening model as described in each embodiment of the present application.

[0066] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present application with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. The elements in the claims will be interpreted broadly based on the language adopted in the claims, and are not limited to the examples described in this specification or during the execution of the application, and the examples will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, and the true scope and spirit are indicated by the claims and the full scope of their equivalents.

[0067] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more schemes thereof) may be used in combination with each other. For example, a person of ordinary skill in the art may use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features may be grouped together to simplify the present application. This should not be interpreted as an intention that a disclosed feature that is not required to be protected is necessary for any claim. On the contrary, the subject matter of the present application may be less than all the features of a particular disclosed embodiment. Thus, the claims are incorporated herein as examples or embodiments in the specific embodiments, wherein each claim is independently a separate embodiment, and it is considered that these embodiments may be combined with each other in various combinations or arrangements. The scope of the present application should be determined with reference to the attached claims and the full scope of equivalent forms granted by these claims.

[0068] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.< / pad> < / pad>

Claims

1. A method for constructing and training a siRNA gene inhibition efficiency screening model, characterized in that: The construction and training method includes: Obtaining first sequence information of a target mRNA and second sequence information of a siRNA obtained based on the target mRNA; Acquire first spatial image information of the target mRNA and second spatial image information of the siRNA, wherein the first spatial image information and the second spatial image information are used to characterize the spatial structures of the target mRNA and the siRNA, respectively; Constructing a siRNA gene inhibition efficiency screening model, wherein the siRNA gene inhibition efficiency screening model comprises a first neural network model, a second neural network model and a third neural network model; Using the first sequence information and the second sequence information to train the first neural network model to extract sequence features; Using the first spatial image information and the second spatial image information to train the second neural network model to extract spatial structure features; The sequence features and the spatial structure features are fused, and the fused features are used to train the third neural network model, so that the screening accuracy of the siRNA gene inhibition efficiency screening model is higher than a threshold.

2. The construction and training method according to claim 1, characterized in that: The construction and training method also includes: based on the principle of base complementarity, traversing the target mRNA with a preset walking interval length and a preset walking coverage interval length to obtain a siRNA sequence library combined with the target mRNA sequence.

3. The construction and training method according to claim 2, characterized in that: The siRNAs in the sequence library are preliminarily screened based on one or more of the number of consecutive bases, GC content and similarity to the reference siRNA sequence, so as to predict the targeted gene inhibition efficiency of the siRNAs in the sequence library after the preliminarily screening using the siRNA gene inhibition efficiency screening model.

4. The construction and training method according to claim 1, characterized in that: Sequence information includes digital information obtained by converting words based on the bases in RNA; The word-unit conversion includes: mapping different bases to different digital information respectively, or combining different bases according to a preset combination to obtain different word-unit combinations, and mapping different word-unit combinations to different digital information.

5. The construction and training method according to claim 1, characterized in that: The second neural network model is one or two, In the case where there is only one second neural network model, the second neural network model is trained based on the first spatial image information and the second spatial image information; In the case that there are two second neural network models, one of the second neural network models is trained based on the first spatial image information, and the other of the second neural network models is trained based on the second spatial image information.

6. The construction and training method according to claim 5, characterized in that: When there are two second neural network models, the spatial image shape of the target mRNA determined based on the first spatial image information is consistent with the spatial image shape of the siRNA determined based on the second spatial image information.

7. The construction and training method according to claim 1, characterized in that: When the second neural network model is used to extract spatial structural features of the target mRNA and siRNA, the size of the image block of the cut siRNA image is larger than the size of the image block of the cut target mRNA image, and the model window size of the siRNA is larger than the model window size of the target mRNA.

8. The construction and training method according to claim 1, characterized in that: The first neural network model is a StarTransformer model, the second neural network model is a Swin Transformer model, and the third neural network model is a DPCNN model.

9. A construction and training device for a siRNA gene inhibition efficiency screening model, characterized in that: include: Interface, which is configured as: Obtaining first sequence information of a target mRNA and second sequence information of a siRNA obtained based on the target mRNA; Acquire first spatial image information of the target mRNA and second spatial image information of the siRNA, wherein the first spatial image information and the second spatial image information are used to characterize the spatial structures of the target mRNA and the siRNA, respectively; At least one processor is configured to implement the construction and training method of the siRNA gene inhibition efficiency screening model as described in any one of claims 1-8.

10. A method for screening siRNA with targeted gene inhibition efficiency, characterized in that: The screening method comprises: Based on the principle of base complementarity, the target mRNA is traversed with a preset walking interval length and a preset walking coverage interval length to obtain a siRNA sequence library combined with the target mRNA sequence; The construction and training device of the siRNA gene inhibition efficiency screening model described in claim 9 is used to predict the targeted gene inhibition efficiency of siRNA in the siRNA sequence library to screen out siRNAs whose targeted gene inhibition efficiency is greater than the set inhibition efficiency.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the construction and training method of the siRNA gene inhibition efficiency screening model as described in any one of claims 1-8.

12. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the construction and training method of the siRNA gene inhibition efficiency screening model as described in any one of claims 1 to 8.

Images