Geometric deep learning as a potential tool for antimicrobial peptide prediction.
Fabiano C Fernandes, Marlon H Cardoso, Abel Gil-Ley, L��via V Luchi, Maria G L da Silva, Maria L R Macedo, Cesar de la Fuente-Nunez, Octavio L Franco
Author Information
Fabiano C Fernandes: Centro de An��lises Prote��micas e Bioqu��micas, P��s-Gradua����o em Ci��ncias Gen��micas e Biotecnologia, Universidade Cat��lica de Bras��lia, Bras��lia, Brazil.
Marlon H Cardoso: Centro de An��lises Prote��micas e Bioqu��micas, P��s-Gradua����o em Ci��ncias Gen��micas e Biotecnologia, Universidade Cat��lica de Bras��lia, Bras��lia, Brazil.
Abel Gil-Ley: S-Inova Biotech, Programa de P��s-Gradua����o em Biotecnologia, Universidade Cat��lica Dom Bosco, Campo Grande, Brazil.
L��via V Luchi: S-Inova Biotech, Programa de P��s-Gradua����o em Biotecnologia, Universidade Cat��lica Dom Bosco, Campo Grande, Brazil.
Maria G L da Silva: Centro de An��lises Prote��micas e Bioqu��micas, P��s-Gradua����o em Ci��ncias Gen��micas e Biotecnologia, Universidade Cat��lica de Bras��lia, Bras��lia, Brazil.
Maria L R Macedo: Laborat��rio de Purifica����o de Prote��nas e suas Fun����es Biol��gicas, Universidade Federal de Mato Grosso do Sul, Cidade Universit��ria, Campo Grande, Mato Grosso do Sul, Brazil.
Cesar de la Fuente-Nunez: Machine Biology Group, Departments of Psychiatry and Microbiology, Perelman School of Medicine, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States.
Octavio L Franco: Centro de An��lises Prote��micas e Bioqu��micas, P��s-Gradua����o em Ci��ncias Gen��micas e Biotecnologia, Universidade Cat��lica de Bras��lia, Bras��lia, Brazil.
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.
