CSM‐Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces

Abstract

AbstractProteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure–function relationship is highlighted by the exponential growth of experimental structures, which has been greatly expanded by recent breakthroughs in protein structure prediction, most notably RosettaFold, and AlphaFold2. These advances have prompted the development of several computational approaches that leverage these data sources to explore potential biological interactions. However, most methods are generally limited to analysis of single types of interactions, such as protein–protein or protein–ligand interactions, and their complexity limits the usability to expert users. Here we report CSM‐Potential2, a deep learning platform for the analysis of binding interfaces on protein structures. In addition to prediction of protein–protein interactions binding sites and classification of biological ligands, our new platform incorporates prediction of interactions with nucleic acids at the residue level and allows for ligand transplantation based on sequence and structure similarity to experimentally determined structures. We anticipate our platform to be a valuable resource that provides easy access to a range of state‐of‐the‐art methods to expert and non‐expert users for the study of biological interactions. Our tool is freely available as an easy‐to‐use web server and API available at https://biosig.lab.uq.edu.au/csm_potential.