Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides

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:2451-9294
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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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
646611Programmable 'Digital' Synthesis for Discovery and Scale-up of Molecules, Clusters and NanomaterialsLeroy CroninEngineering and Physical Sciences Research Council (EPSRC)EP/L023652/1CHEM - CHEMISTRY
616021Energy and the Physical Sciences: Hydrogen Production using a Proton Electron BufferLeroy CroninEngineering and Physical Sciences Research Council (EPSRC)EP/K023004/1CHEM - CHEMISTRY
503291Molecular-Metal-Oxide-nanoelectronicS (M-MOS): Achieving the Molecular LimitLeroy CroninEngineering and Physical Sciences Research Council (EPSRC)EP/H024107/1CHEM - CHEMISTRY
562821Innovative Manufacturing Research Centre for Continuous Manufacturing and Crystallisation (CMAC)Leroy CroninEngineering and Physical Sciences Research Council (EPSRC)EP/I033459/1CHEM - CHEMISTRY
577391Programmable Molecular Metal Oxides (PMMOs) - From Fundamentals to ApplicationLeroy CroninEngineering and Physical Sciences Research Council (EPSRC)EP/J015156/1CHEM - CHEMISTRY

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