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Jun, 2024;16 (2): 392-403.

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.

Kedong Yin, Wen Xu, Shiming Ren, Qingpeng Xu, Shaojie Zhang, Ruiling Zhang, Mengwan Jiang, Yuhong Zhang, Degang Xu, Ruifang Li
Author Information
  1. Kedong Yin: Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
  2. Wen Xu: Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China. xw@haut.edu.cn.
  3. Shiming Ren: Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
  4. Qingpeng Xu: Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
  5. Shaojie Zhang: Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
  6. Ruiling Zhang: Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
  7. Mengwan Jiang: School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China.
  8. Yuhong Zhang: School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China.
  9. Degang Xu: College of Information Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China. xudegang@haut.edu.cn.
  10. Ruifang Li: Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China. lrf@haut.edu.cn. ORCID
PMID: 38416364 DOI: 10.1007/s12539-024-00612-3

Abstract

Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr.2, iAMPpred and AMPlify) and being effective in identifying AMPs. In addition, a peptide sequence generator, AP_Gen, was devised based on the concept of recombining dominant amino acids and dipeptide compositions. After inputting the parameters of the 71 tridecapeptides from antimicrobial peptides database (APD3) into AP_Gen, a tridecapeptide bank consisting of de novo designed 17,496 tridecapeptide sequences were randomly generated, from which 2675 candidate AMP sequences were identified by AP_Sin. Chemical synthesis was performed on 180 randomly selected candidate AMP sequences, of which 18 showed high antimicrobial activities against a wide range of the tested pathogenic microorganisms, and 16 of which had a minimal inhibitory concentration of less than 10 μg/mL against at least one of the tested pathogenic microorganisms. The method established in this research accelerates the discovery of valuable candidate AMPs and provides a novel approach for de novo design of antimicrobial peptides.

Keywords

Antimicrobial activity Antimicrobial peptide Bioinformatics De novo design Machine learning

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Grants

  1. 2323000421165/Natural Science Foundation of Henan Province
  2. 2020ZKCJ23/Innovative Funds Plan of Henan University of Technology

MeSH Term

Machine Learning
Antimicrobial Peptides
Microbial Sensitivity Tests
Drug Design
Amino Acid Sequence

Chemicals

Antimicrobial Peptides
Journal Article

Word Cloud

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