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Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides

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Abstract

The ubiquitous antimicrobial peptides (AMPs), with a broad range of antimicrobial activities, represent a great promise for combating the multi-drug resistant infections. In this study, using a large and diverse set of AMPs (2638) and non-AMPs (3700), we have explored a variety of machine learning classifiers to build in silico models for AMP prediction, including Random Forest (RF), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), and ensemble learning. Among the various models generated, the RF classifier-based model top-performed in both the internal [Accuracy: 91.40%, Precision: 89.37%, Sensitivity: 90.05%, and Specificity: 92.36%] and external validations [Accuracy: 89.43%, Precision: 88.92%, Sensitivity: 85.21%, and Specificity: 92.43%]. In addition, the RF classifier-based model correctly predicted the known AMPs and non-AMPs; those kept aside as an additional external validation set. The performance assessment revealed three features viz. ChargeD2001, PAAC12 (pseudo amino acid composition), and polarity T13 that are likely to play vital roles in the antimicrobial activity of AMPs. The developed RF-based classification model may further be useful in the design and prediction of the novel potential AMPs.

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Acknowledgements

The authors are thankful to NIPER Kolkata and NIPER Mohali for providing the resources and support. The author Mushtaq A. Wani is thankful to the Department of Pharmaceuticals and the Ministry of Chemicals and Fertilizes for providing a Ph.D. fellowship.

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  1. Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Kolkata, 700054, West Bengal, India

    Mushtaq Ahmad Wani & Kuldeep K. Roy

  2. Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Mohali, 160062, Punjab, India

    Prabha Garg

  3. Department of Pharmaceutical Sciences, School of Health Sciences, University of Petroleum and Energy Studies (UPES), P.O. Bidholi, Dehradun, 248007, Uttarakhand, India

    Kuldeep K. Roy

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  1. Mushtaq Ahmad Wani
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Wani, M.A., Garg, P. & Roy, K.K. Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides. Med Biol Eng Comput 59, 2397–2408 (2021). https://doi.org/10.1007/s11517-021-02443-6

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