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Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates

Contreas, Leonardo; Hook, Andrew L.; Winkler, David A.; Figueredo, Grazziela; Williams, Paul; Laughton, Charles A.; Alexander, Morgan R.; Williams, Philip M.

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Authors

Leonardo Contreas

ANDREW HOOK ANDREW.HOOK@NOTTINGHAM.AC.UK
Assistant Professor

David A. Winkler

PAUL WILLIAMS PAUL.WILLIAMS@NOTTINGHAM.AC.UK
Professor of Molecular Microbiology

CHARLES LAUGHTON CHARLES.LAUGHTON@NOTTINGHAM.AC.UK
Professor of Computational Pharmaceutical Science

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MORGAN ALEXANDER MORGAN.ALEXANDER@NOTTINGHAM.AC.UK
Professor of Biomedical Surfaces

PHIL WILLIAMS PHIL.WILLIAMS@NOTTINGHAM.AC.UK
Professor of Biophysics



Abstract

Bacterial infections are increasingly problematic due to the rise of antimicrobial resistance. Consequently, the rational design of materials naturally resistant to biofilm formation is an important strategy for preventing medical device-associated infections. Machine learning (ML) is a powerful method to find useful patterns in complex data from a wide range of fields. Recent reports showed how ML can reveal strong relationships between bacterial adhesion and the physicochemical properties of polyacrylate libraries. These studies used robust and predictive nonlinear regression methods that had better quantitative prediction power than linear models. However, as nonlinear models’ feature importance is a local rather than global property, these models were hard to interpret and provided limited insight into the molecular details of material-bacteria interactions. Here, we show that the use of interpretable mass spectral molecular ions and chemoinformatic descriptors and a linear binary classification model of attachment of three common nosocomial pathogens to a library of polyacrylates can provide improved guidance for the design of more effective pathogen-resistant coatings. Relevant features from each model were analyzed and correlated with easily interpretable chemoinformatic descriptors to derive a small set of rules that give model features tangible meaning that elucidate relationships between the structure and function. The results show that the attachment of Pseudomonas aeruginosa and Staphylococcus aureus can be robustly predicted by chemoinformatic descriptors, suggesting that the obtained models can predict the attachment response to polyacrylates to identify anti-attachment materials to synthesize and test in the future.

Citation

Contreas, L., Hook, A. L., Winkler, D. A., Figueredo, G., Williams, P., Laughton, C. A., …Williams, P. M. (2023). Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates. ACS Applied Materials and Interfaces, 15(11), 14155-14163. https://doi.org/10.1021/acsami.2c23182

Journal Article Type Article
Acceptance Date Feb 23, 2023
Online Publication Date Mar 7, 2023
Publication Date Mar 22, 2023
Deposit Date Mar 8, 2023
Publicly Available Date Mar 10, 2023
Journal ACS Applied Materials and Interfaces
Print ISSN 1944-8244
Electronic ISSN 1944-8252
Publisher American Chemical Society
Peer Reviewed Peer Reviewed
Volume 15
Issue 11
Pages 14155-14163
DOI https://doi.org/10.1021/acsami.2c23182
Keywords Bacteria, Infectious diseases, Ions, Molecular modeling, Polymers
Public URL https://nottingham-repository.worktribe.com/output/18229486
Publisher URL https://pubs.acs.org/doi/10.1021/acsami.2c23182

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