Computational methods and tools in antimicrobial peptide research
Computational methods and tools in antimicrobial peptide research
The evolution of antibiotic-resistant bacteria is an ongoing and troubling development that has increased the number of diseases and infections that risk going untreated. There is an urgent need to develop alternative strategies and treatments to address this issue. One class of molecules that is attracting significant interest is that of antimicrobial peptides (AMPs). Their design and development has been aided considerably by the applications of molecular models, and we review these here. These methods include the use of tools to explore the relationships between their structures, dynamics, and functions and the increasing application of machine learning and molecular dynamics simulations. This review compiles resources such as AMP databases, AMP-related web servers, and commonly used techniques, together aimed at aiding researchers in the area toward complementing experimental studies with computational approaches.
aggregation, antibiotic resistance, antimicrobial peptides, artificial Intelligence, computational chemistry, machine learning, membranes, molecular dynamics, peptide engineering, peptides
3172-3196
Aronica, Pietro G.A.
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Reid, Lauren, Marie
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Desai, Nirali
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Li, Jianguo
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Fox, Stephen J
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Yadahalli, Shilpa
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Essex, Jonathan W.
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Verma, Chandra
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26 July 2021
Aronica, Pietro G.A.
83b02818-6d65-4a88-a006-446214b66511
Reid, Lauren, Marie
c546d1c1-8eb7-4a4b-8b65-57457207e034
Desai, Nirali
87e184f2-c335-41a0-8645-7d7a5c1c1f85
Li, Jianguo
06b0e84d-0c7f-48a5-ad4e-a1c52a836f4f
Fox, Stephen J
a8957e8a-3086-4917-8575-eb0f9e8604cf
Yadahalli, Shilpa
18a081f5-abf4-462a-859a-f7b8939ed7ff
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Verma, Chandra
06a08004-58f0-4248-997c-0947fe0d310d
Aronica, Pietro G.A., Reid, Lauren, Marie, Desai, Nirali, Li, Jianguo, Fox, Stephen J, Yadahalli, Shilpa, Essex, Jonathan W. and Verma, Chandra
(2021)
Computational methods and tools in antimicrobial peptide research.
Journal of Chemical Information and Modeling, 61 (7), .
(doi:10.1021/acs.jcim.1c00175).
Abstract
The evolution of antibiotic-resistant bacteria is an ongoing and troubling development that has increased the number of diseases and infections that risk going untreated. There is an urgent need to develop alternative strategies and treatments to address this issue. One class of molecules that is attracting significant interest is that of antimicrobial peptides (AMPs). Their design and development has been aided considerably by the applications of molecular models, and we review these here. These methods include the use of tools to explore the relationships between their structures, dynamics, and functions and the increasing application of machine learning and molecular dynamics simulations. This review compiles resources such as AMP databases, AMP-related web servers, and commonly used techniques, together aimed at aiding researchers in the area toward complementing experimental studies with computational approaches.
Text
AMP Review
- Accepted Manuscript
More information
Accepted/In Press date: 24 June 2021
e-pub ahead of print date: 24 June 2021
Published date: 26 July 2021
Additional Information:
Funding Information:
We thank Raghav Kannan for help in proofreading the manuscript. We thank A*STAR for funding (grant IDs H17/01/a0/010, IAF111213C) and the A*STAR Research Attachment Programme (ARAP).
Publisher Copyright:
© 2021 American Chemical Society
Keywords:
aggregation, antibiotic resistance, antimicrobial peptides, artificial Intelligence, computational chemistry, machine learning, membranes, molecular dynamics, peptide engineering, peptides
Identifiers
Local EPrints ID: 450480
URI: http://eprints.soton.ac.uk/id/eprint/450480
ISSN: 1549-9596
PURE UUID: a68da6b0-c142-4b7c-9233-d0d816b4a69d
Catalogue record
Date deposited: 29 Jul 2021 16:31
Last modified: 17 Mar 2024 06:43
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Contributors
Author:
Pietro G.A. Aronica
Author:
Lauren, Marie Reid
Author:
Nirali Desai
Author:
Jianguo Li
Author:
Stephen J Fox
Author:
Shilpa Yadahalli
Author:
Chandra Verma
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