Antibacterial peptide prediction method and device based on protein pre-training representation learning

Abstract

The invention discloses an antibacterial peptide prediction method and device based on protein pre-training representation learning; the method comprises the following steps: S1, employing a pre-training strategy to carry out the word segmentation and covering of a label-free protein sequence from a protein database, and obtaining a pre-training representation learning model, carrying out pre-training of two tasks of covering a language model and sentence continuity prediction, capturing expressions of a word level and a sentence level, and helping the model to learn general structural features of a protein sequence; S2, for the antibacterial peptide pre-recognition and prediction task, changing an output layer of a pre-training model, and performing fine adjustment on the model by using an antibacterial peptide data set with a label to generate an antibacterial peptide prediction model; and S3, according to the antibacterial peptide pre-identification and prediction task, adopting an antibacterial peptide prediction model for identification, and outputting a prediction result. Pre-training is applied to the field of antibacterial peptide recognition and prediction, and an efficient antibacterial peptide prediction model is established based on a known antibacterial peptide sequence with small data volume and unbalanced distribution.

Description

technical field [0001] The invention relates to the technical field of computer recognition of antimicrobial peptide components, in particular to a method and device for antimicrobial peptide prediction based on protein pre-training representation learning. Background technique [0002] Due to the abuse of antibiotics, the problem of drug resistance of pathogenic bacteria is becoming more and more serious, which has become a huge threat to human health. Finding new raw materials for antibiotics is an effective way to protect human health. As an important part of the natural immune system of organisms, antimicrobial peptides (AMPs) are a class of small molecule polypeptides that widely exist in natural organisms. They kill target bacteria by destroying cell membranes and interfering with DNA replication and transcription processes. , viruses, fungi, parasites, tumor cells have a certain inhibitory effect. Antimicrobial peptides are regarded as the best substitutes for antib...

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Problems solved by technology

[0011] In view of the above-mentioned defects (deficiencies) of the prior art, the object of the present invention is to provide a method for predicting antimicrobial peptides based on natural language processing, to further improve the accuracy and speed of antimicrobial peptide identification and prediction, and to solve the problem that the built model cannot Common questions across datasets

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