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

doi:10.1007/s12539-024-00612-3

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides

Interdiscip Sci. 2024 Feb 28. doi: 10.1007/s12539-024-00612-3. Online ahead of print.

Authors

Kedong Yin^#^{1

2}, Wen Xu^#^{3

4}, Shiming Ren^{1

5}, Qingpeng Xu^{1

6}, Shaojie Zhang^{1

5}, Ruiling Zhang^{1

7}, Mengwan Jiang⁶, Yuhong Zhang⁶, Degang Xu⁸, Ruifang Li^{9

10}

Affiliations

¹ Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
² College of Information Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
³ 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.
⁴ Law College, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China. xw@haut.edu.cn.
⁵ College of Biological Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
⁶ School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China.
⁷ School of Economics and Trade, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China.
⁸ 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.
⁹ 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.
¹⁰ College of Biological Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China. lrf@haut.edu.cn.

^# Contributed equally.

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.

Abstract

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