Skip to main content

Research Repository

Advanced Search

Krein support vector machine classification of antimicrobial peptides

Redshaw, Joseph; Ting, Darren S.J.; Brown, Alex; Hirst, Jonathan D.; Gärtner, Thomas

Krein support vector machine classification of antimicrobial peptides Thumbnail


Joseph Redshaw

Darren S.J. Ting

Alex Brown

Thomas Gärtner


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.


Redshaw, J., Ting, D. S., Brown, A., Hirst, J. D., & Gärtner, T. (2023). Krein support vector machine classification of antimicrobial peptides. Digital Discovery, 2(2), 502-511.

Journal Article Type Article
Acceptance Date Feb 22, 2023
Online Publication Date Feb 27, 2023
Publication Date Apr 1, 2023
Deposit Date Mar 9, 2023
Publicly Available Date Mar 9, 2023
Journal Digital Discovery
Electronic ISSN 2635-098X
Publisher Royal Society of Chemistry (RSC)
Peer Reviewed Peer Reviewed
Volume 2
Issue 2
Pages 502-511
Public URL
Publisher URL


You might also like

Downloadable Citations