Antimicrobial Peptide Activity Prediction Method Based on Multi-label Learning

Abstract

The antimicrobial peptide activity prediction method based on multi-label learning extracts the amino acid components corresponding to the peptide sequence, and then obtains the corresponding moment features according to the physical and chemical attribute encoding, which together constitute the feature vector of the peptide sequence. The feature vector of each peptide sequence is composed of two parts, one is the amino acid composition, and the other is the moment feature extracted based on the physical and chemical attribute encoding. Using the least squares multi-label learning algorithm to calculate the minimum transformation matrix W, the label output of the sample to be tested can be obtained through the transformation matrix W, and the predicted class label vector set can be obtained according to the label output. Rapid and accurate prediction of activity of antimicrobial peptide sequences from ensembles of class label vectors. Therefore, the shape specificity of each angle of the peptide sequence can be obtained, so that the antimicrobial peptide activity can be marked quickly, accurately and automatically.

Description

technical field [0001] The invention relates to biomedical engineering, in particular to a method for predicting antimicrobial peptide activity based on multi-label learning that can quickly, accurately and automatically mark antimicrobial peptide activity. Background technique [0002] Antimicrobial peptides are small molecular polypeptides involved in innate immunity, generally composed of 20 to 60 amino acid residues, and these active peptides have broad-spectrum and high-efficiency bactericidal activity against bacteria. With the deepening of people's research, it is found that these antibacterial peptides have a strong killing effect on some fungi, protozoa, viruses and cancer cells. The wide range of biological activities of antimicrobial peptides shows its good application prospects in medicine. [0003] Determining the activity of antimicrobial peptides by experimental means, whether based on in vivo or in vitro techniques, is not only very time-consuming, but also ...

AI Technical Summary

Problems solved by technology

[0003] Determining the activity of antimicrobial peptides by experimental means, whether based on in vivo or in vitro techniques, is not only very time-consuming, but also expensive

At present, researchers have proposed more than ten kinds of antimicrobial peptide predictors. However, these tools are basically used to judge whether the peptide molecule has antibacterial properties, or whether it belongs to the antimicrobial peptide family, and no further research has been made on the specific activity of antimicrobial peptides. predict

Most of them design binary classification models to judge whether peptide molecules belong to antimicrobial peptides; or the proposed method can realize the activity prediction of antimicrobial peptides, but it is limited to 5 kinds of activities, and the prediction accuracy needs to be further improved

Most of the existing methods are binary classification models, which can only be used for antimicrobial peptide identification

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