Systematic analysis of the relationship between fold-dependent flexibility and artificial intelligence protein structure prediction. PLoS One 2024;19(11):e0313308
Date
11/26/2024Pubmed ID
39591473Pubmed Central ID
PMC11594405DOI
10.1371/journal.pone.0313308Scopus ID
2-s2.0-85210375728 (requires institutional sign-in at Scopus site) 1 CitationAbstract
Artificial Intelligence (AI)-based deep learning methods for predicting protein structures are reshaping knowledge development and scientific discovery. Recent large-scale application of AI models for protein structure prediction has changed perceptions about complicated biological problems and empowered a new generation of structure-based hypothesis testing. It is well-recognized that proteins have a modular organization according to archetypal folds. However, it is yet to be determined if predicted structures are tuned to one conformation of flexible proteins or if they represent average conformations. Further, whether or not the answer is protein fold-dependent. Therefore, in this study, we analyzed 2878 proteins with at least ten distinct experimental structures available, from which we can estimate protein topological rigidity verses heterogeneity from experimental measurements. We found that AlphaFold v2 (AF2) predictions consistently return one specific form to high accuracy, with 99.68% of distinct folds (n = 623 out of 628) having an experimental structure within 2.5Å RMSD from a predicted structure. Yet, 27.70% and 10.82% of folds (174 and 68 out of 628 folds) have at least one experimental structure over 2.5Å and 5Å RMSD, respectively, from their AI-predicted structure. This information is important for how researchers apply and interpret the output of AF2 and similar tools. Additionally, it enabled us to score fold types according to how homogeneous versus heterogeneous their conformations are. Importantly, folds with high heterogeneity are enriched among proteins which regulate vital biological processes including immune cell differentiation, immune activation, and metabolism. This result demonstrates that a large amount of protein fold flexibility has already been experimentally measured, is vital for critical cellular processes, and is currently unaccounted for in structure prediction databases. Therefore, the structure-prediction revolution begets the protein dynamics revolution!
Author List
Haque N, Wagenknecht JB, Ratnasinghe BD, Zimmermann MTAuthors
Jessica B. Wagenknecht Bioinformatics Analyst II in the Mellowes Center for Genomic Sciences and Precision Medicine department at Medical College of WisconsinMichael T. Zimmermann PhD Director, Associate Professor in the Data Science Institute department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
Artificial IntelligenceComputational Biology
Databases, Protein
Models, Molecular
Protein Conformation
Protein Folding
Proteins
