构象异构
构象集合
计算机科学
集合(抽象数据类型)
蛋白质超家族
蛋白质数据库
序列(生物学)
内在无序蛋白质
灵活性(工程)
计算生物学
多样性(政治)
蛋白质结构
人工智能
化学
作者
Tadeo Saldaño,Nahuel Escobedo,Julia Marchetti,Diego Javier Zea,Juan Mac Donagh,Ana Julia Velez Rueda,Eduardo Gonik,Agustina García Melani,Julieta Novomisky Nechcoff,Martín N Salas,Tomás Peters,Nicolás Demitroff,Sebastian Fernandez Alberti,Nicolas Palopoli,Maria Silvina Fornasari,Gustavo Parisi
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2022-04-05
卷期号:38 (10): 2742-2748
标识
DOI:10.1093/bioinformatics/btac202
摘要
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.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.Data and code used in this manuscript are publicly available at https://gitlab.com/sbgunq/publications/af2confdiv-oct2021.Supplementary data is available at the journal's web site.
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