A structural biology community assessment of AlphaFold2 applications

• DOI: 10.1038/s41594-022-00849-w
• Publication Date: 2022-11-07T17:03:57Z
• Authors: Mehmet Akdel; Douglas E. V. Pires; Eduard Porta Pardo; Jürgen Jänes; Arthur O. Zalevsky; Bálint Mészáros; Patrick Bryant; Lydia L. Good; Roman A. Laskowski; Gabriele Pozzati; Aditi Shenoy; Wensi Zhu; Petras Kundrotas; Victoria Ruiz Serra; Carlos H. M. Rodr...; Alistair S. Dunham; David Burke; Neera Borkakoti; Sameer Velankar; Adam Frost; Jérôme Basquin; Kresten Lindorff-...; Alex Bateman; Andrey V. Kajava; Alfonso Valencia; Sergey Ovchinnikov; Janani Durairaj; David B. Ascher; Janet M. Thornton; Norman E. Davey; Amelie Stein; Arne Elofsson; Tristan I. Croll; Pedro Beltrao
• Keywords: 0301 basic medicine; 570; Biomedical and clinical sciences; PREDICTION; Protein Conformation; FEATURES; Bioinformatics and Computational Biology; Biophysics; 612; Medical and Health Sciences; SEQUENCE; Article; Databases; 03 medical and health sciences; Life Science; SPACE; TOOL; DOCKING; Furylfuramide; Databases, Protein; SERVER; 0303 health sciences; Binding Sites; IDENTIFICATION; MUTATIONS; Protein; Computational Biology; Proteins; Biological Sciences; Biological sciences; Networking and Information Technology R&D (NITRD); Chemical sciences; Chemical Sciences; Biochemistry and Cell Biology; Generic health relevance; PROTEIN STABILITY; Developmental Biology

AbstractMost proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.