AUTHOR=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. TITLE=Geometric deep learning as a potential tool for antimicrobial peptide prediction JOURNAL=Frontiers in Bioinformatics VOLUME=3 YEAR=2023 URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2023.1216362 DOI=10.3389/fbinf.2023.1216362 ISSN=2673-7647 ABSTRACT=

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.