Geometric deep learning as a potential tool for antimicrobial peptide prediction - 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. (Frontiers in Bioinformatics, 2023)
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 2 This is a provisional file, not the final typeset article 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.