Yoshida, M. , Hinkley, T., Tsuda, S., Abul-Haija, Y. , Mcburney, R. T., Kulikov, V., Mathieson, J., Galinanes Reyes, S., Castro Spencer, M. D. and Cronin, L. (2018) Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides. Chem, 4(3), pp. 533-543. (doi: 10.1016/j.chempr.2018.01.005)
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Abstract
We present a proof-of-concept methodology for efficiently optimizing a chemical trait by using an artificial evolutionary workflow. We demonstrate this by optimizing the efficacy of antimicrobial peptides (AMPs). In particular, we used a closed-loop approach that combines a genetic algorithm, machine learning, and in vitro evaluation to improve the antimicrobial activity of peptides against Escherichia coli. Starting with a 13-mer natural AMP, we identified 44 highly potent peptides, achieving up to a ca. 160-fold increase in antimicrobial activity within just three rounds of experiments. During these experiments, the conformation of the peptides selected was changed from a random coil to an α-helical form. This strategy not only establishes the potential of in vitro molecule evolution using an algorithmic genetic system but also accelerates the discovery of antimicrobial peptides and other functional molecules within a relatively small number of experiments, allowing the exploration of broad sequence and structural space.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Yoshida, Mari and Abul-Haija, Yousef and Kulikov, Dr Vladislav and Hinkley, Dr Trevor and Castro Spencer, Mrs Maria Diana and Mathieson, Dr Jennifer and Galinanes Reyes, Miss Sabrina and Tsuda, Dr Soichiro and Cronin, Professor Lee and Mcburney, Dr Roy |
| Authors: | Yoshida, M., Hinkley, T., Tsuda, S., Abul-Haija, Y., Mcburney, R. T., Kulikov, V., Mathieson, J., Galinanes Reyes, S., Castro Spencer, M. D., and Cronin, L. |
| College/School: | College of Science and Engineering > School of Chemistry |
| Journal Name: | Chem |
| Publisher: | Elsevier (Cell Press) |
| ISSN: | 2451-9308 |
| ISSN (Online): | 2451-9294 |
| Published Online: | 08 February 2018 |
| Copyright Holders: | Copyright © 2018 Elsevier |
| First Published: | First published in Chem 4(3):533-543 |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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