Issue 2, 2023

Krein support vector machine classification of antimicrobial peptides


Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating the discovery process. Kernel methods are a class of machine learning algorithms that utilise a kernel function to transform input data into a new representation. When appropriately normalised, the kernel function can be regarded as a notion of similarity between instances. However, many expressive notions of similarity are not valid kernel functions, meaning they cannot be used with standard kernel methods such as the support-vector machine (SVM). The Kreĭn-SVM represents generalisation of the standard SVM that admits a much larger class of similarity functions. In this study, we propose and develop Kreĭn-SVM models for AMP classification and prediction by employing the Levenshtein distance and local alignment score as sequence similarity functions. Utilising two datasets from the literature, each containing more than 3000 peptides, we train models to predict general antimicrobial activity. Our best models achieve an AUC of 0.967 and 0.863 on the test sets of each respective dataset, outperforming the in-house and literature baselines in both cases. We also curate a dataset of experimentally validated peptides, measured against Staphylococcus aureus and Pseudomonas aeruginosa, in order to evaluate the applicability of our methodology in predicting microbe-specific activity. In this case, our best models achieve an AUC of 0.982 and 0.891, respectively. Models to predict both general and microbe-specific activities are made available as web applications.

Graphical abstract: Krein support vector machine classification of antimicrobial peptides

Article information

Article type
19 Jan 2023
22 Feb 2023
First published
27 Feb 2023
This article is Open Access
Creative Commons BY license

Digital Discovery, 2023,2, 502-511

Krein support vector machine classification of antimicrobial peptides

J. Redshaw, D. S. J. Ting, A. Brown, J. D. Hirst and T. Gärtner, Digital Discovery, 2023, 2, 502 DOI: 10.1039/D3DD00004D

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