Geometric deep learning as a potential tool for antimicrobial peptide prediction

Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhi...

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Bibliographic Details
Published in:	Frontiers in bioinformatics Vol. 3; p. 1216362
Main Authors:	Fernandes, Fabiano C, Cardoso, Marlon H, Gil-Ley, Abel, Luchi, Lívia V, da Silva, Maria G L, Macedo, Maria L R, de la Fuente-Nunez, Cesar, Franco, Octavio L
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 13-07-2023
Subjects:	antimicrobial peptide classification antimicrobial peptide design antimicrobial peptide prediction Bioinformatics explainable artificial intelligence geometric deep learning explainable artificial intelligence antimicrobial peptide design antimicrobial peptide prediction geometric deep learning antimicrobial peptide classification
Online Access:	Get full text
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Summary:	Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 Edited by: Yovani Marrero-Ponce, University of Valencia, Spain Reviewed by: Akanksha Rajput, University of California, San Diego, United States Michael Fernandez, University of British Columbia, Canada
ISSN:	2673-7647 2673-7647
DOI:	10.3389/fbinf.2023.1216362