Leveraging AI Advances and Online Tools for Structure‐Based Variant Analysis

Abstract

AbstractUnderstanding how a gene variant affects protein function is important in life science, as it helps explain traits or dysfunctions in organisms. In a clinical setting, this understanding makes it possible to improve and personalize patient care. Bioinformatic tools often only assign a pathogenicity score, rather than providing information about the molecular basis for phenotypes. Experimental testing can furnish this information, but this is slow and costly and requires expertise and equipment not available in a clinical setting. Conversely, mapping a gene variant onto the three‐dimensional (3D) protein structure provides a fast molecular assessment free of charge. Before 2021, this type of analysis was severely limited by the availability of experimentally determined 3D protein structures. Advances in artificial intelligence algorithms now allow confident prediction of protein structural features from sequence alone. The aim of the protocols presented here is to enable non‐experts to use databases and online tools to investigate the molecular effect of a genetic variant. The Basic Protocol relies only on the online resources AlphaFold, Protein Structure Database, and UniProt. Alternate Protocols document the usage of the Protein Data Bank, SWISS‐MODEL, ColabFold, and PyMOL for structure‐based variant analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol: 3D Mapping based on UniProt and AlphaFoldAlternate Protocol 1: Using experimental models from the PDBAlternate Protocol 2: Using information from homology modeling with SWISS‐MODELAlternate Protocol 3: Predicting 3D structures with ColabFoldAlternate Protocol 4: Structure visualization and analysis with PyMOL