{"id":"doi:10.1093/bioinformatics/btac202","kind":"article","title":"Impact of protein conformational diversity on AlphaFold predictions","authors":["Tadeo Enrique Saldaño","Nahuel Escobedo","Julia Marchetti","Diego Javier Zea","Juan Mac Donagh","Ana Julia Velez Rueda","Eduardo Gonik","Agustina García Melani","Julieta Novomisky Nechcoff","Martin Salas","Tomás Peters","Nicolás Demitroff","Sebastian Fernández-Alberti","Nicolás Palópoli","Marı́a Silvina Fornasari","Gustavo D. Parisi"],"author_count":16,"year":2022,"published":"2022-04-05","venue":"Bioinformatics","cited_by":196,"doi":"10.1093/bioinformatics/btac202","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/bioinformatics/btac202","archive_url":"https://space-kitty.com/papers/doi:10.1093/bioinformatics/btac202","updated":null,"pmid":"35561203","pmcid":null,"openalex_id":"W4225552973","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2021/10/28/2021.10.27.466189.full.pdf","abstract":"MOTIVATION: After the outstanding breakthrough of AlphaFold in predicting protein 3D models, new questions appeared and remain unanswered. The ensemble nature of proteins, for example, challenges the structural prediction methods because the models should represent a set of conformers instead of single structures. The evolutionary and structural features captured by effective deep learning techniques may unveil the information to generate several diverse conformations from a single sequence. Here, we address the performance of AlphaFold2 predictions obtained through ColabFold under this ensemble paradigm. RESULTS: Using a curated collection of apo-holo pairs of conformers, we found that AlphaFold2 predicts the holo form of a protein in ∼70% of the cases, being unable to reproduce the observed conformational diversity with the same error for both conformers. More importantly, we found that AlphaFold2's performance worsens with the increasing conformational diversity of the studied protein. This impairment is related to the heterogeneity in the degree of conformational diversity found between different members of the homologous family of the protein under study. Finally, we found that main-chain flexibility associated with apo-holo pairs of conformers negatively correlates with the predicted local model quality score plDDT, indicating that plDDT values in a single 3D model could be used to infer local conformational changes linked to ligand binding transitions. AVAILABILITY AND IMPLEMENTATION: Data and code used in this manuscript are publicly available at https://gitlab.com/sbgunq/publications/af2confdiv-oct2021. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Impact of protein conformational diversity on AlphaFold predictions\" by Tadeo Enrique Saldaño, Nahuel Escobedo, Julia Marchetti et al., https://doi.org/10.1093/bioinformatics/btac202, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"protein-design","name":"AI Protein Design & Structure Prediction","domain_slug":"biotech"}],"cited_in_dossiers":[]}