Method for predicting antibacterial peptides of lactic acid bacteria based on graph neural network

Abstract

The invention discloses a method for predicting antibacterial peptides of lactic acid bacteria based on a graph neural network. The method comprises the following steps: establishing a positive sample by searching known antibacterial peptides of lactic acid bacteria, establishing a negative sample by collecting sequences with the length of 5 to 255 from a protein database, and removing redundant sequences and similarities; performing feature extraction according to the positive and negative samples to obtain a feature vector and an initial input graph, and establishing a graph neural network model on the basis; through training, evaluation and loop optimization of the graph neural network model, determining parameters such as the optimal layer number, the optimal training round number and the learning rate of the graph neural network; and finally, predicting data of strains suspected to have antibacterial activity according to the graph neural network model. By adopting the method for predicting the antibacterial peptides of the lactic acid bacteria, wet experiment screening in a laboratory is replaced by computer model prediction, the judgment time of the protein sequence of the antibacterial peptides of the lactic acid bacteria is shortened, accurate and efficient batch identification is realized, and an effective alternative method is provided for screening lactic acid bacteria strains with antibacterial characteristics.

Description

technical field [0001] The invention relates to the field of identification of biological antibacterial peptides, in particular to a method for predicting antibacterial peptides of lactic acid bacteria based on a graph neural network. Background technique [0002] In the existing identification technology of biological antimicrobial peptides, the following two technologies are mainly used: [0003] 1. The antibacterial experiment using the agar hole diffusion method takes a long time and cannot be identified at high throughput; 2. The identification is performed using machine learning technology or long-term short-term memory and convolutional neural network technology in deep learning, although multiple Amino acid sequence, but it can only capture the local semantic information of the antimicrobial peptide sequence, and it is not easy to grasp the characteristic information of the antimicrobial peptide from the perspective of the overall structure, so the recognition accura...

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Problems solved by technology

[0003] 1. The antibacterial experiment using the agar hole diffusion method takes a long time and cannot be identified at high throughput; 2. The identification is performed using machine learning technology or long-term short-term memory and convolutional neural network technology in deep learning, although multiple

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