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Machine Learning Accelerates De Novo Design of Antimicrobial Peptides

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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.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This work was supported by the Natural Science Foundation of Henan Province (2323000421165); the Innovative Funds Plan of Henan University of Technology (2020ZKCJ23).

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Author notes

  1. Kedong Yin and Wen Xu have contributed equally to this work.

Authors and 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

    Kedong Yin, Wen Xu, Shiming Ren, Qingpeng Xu, Shaojie Zhang, Ruiling Zhang & Ruifang Li

  2. College of Biological Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People’s Republic of China

    Shiming Ren, Shaojie Zhang & Ruifang Li

  3. College of Information Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People’s Republic of China

    Kedong Yin & Degang Xu

  4. School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People’s Republic of China

    Qingpeng Xu, Mengwan Jiang & Yuhong Zhang

  5. Law College, Henan University of Technology, Zhengzhou, 450001, Henan, People’s Republic of China

    Wen Xu

  6. School of Economics and Trade, Henan University of Technology, Zhengzhou, 450001, Henan, People’s Republic of China

    Ruiling Zhang

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  1. Kedong Yin
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  2. Wen Xu
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Corresponding authors

Correspondence to Wen Xu, Degang Xu or Ruifang Li.

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Yin, K., Xu, W., Ren, S. et al. Machine Learning Accelerates De Novo Design of Antimicrobial Peptides. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00612-3

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