Multi-label learning based activity prediction method for antibacterial peptide

Abstract

The invention provides a multi-label learning based activity prediction method for antibacterial peptide. The method is that corresponding amino acid components corresponding peptide sequences are extracted, and then corresponding matrix features are acquired according to physical and chemical attribute encoding, so as to generate feature vectors of the peptide sequences; the feature vector of each peptide sequence comprises two parts, namely, the amino acid components, and the matrix features extracted on the basis of the physical and chemical attribute encoding; the least-squares multi-label learning algorithm is performed to calculating to obtain a minimum transformation matrix W, then each label output of a sample to be tested can be obtained through the transformation matrix W, and a predication label vector set can be acquired according to each label output, thus the activity of antibacterial peptide sequences can be fast and accurately predicated according to the similar label vector set, the shape feature of each angle of each peptide sequence can be acquired, and as a result, the antibacterial peptide activity can be fast, accurately and automatically marked.

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 ...

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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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