Best templates outperform homology models in predicting the impact of mutations on protein stability.

TitleBest templates outperform homology models in predicting the impact of mutations on protein stability.
Publication TypeJournal Article
Year of Publication2022
AuthorsPak MA, Ivankov DN
JournalBioinformatics
Volume38
Issue18
Pagination4312-4320
Date Published2022 Sep 15
ISSN1367-4811
KeywordsAlgorithms, Mutation, Protein Folding, Protein Stability, Proteins
Abstract

MOTIVATION: Prediction of protein stability change upon mutation (ΔΔG) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. In case, protein 3D structure is not available, one can predict the structure from protein sequence; however, the perspectives of ΔΔG predictions for predicted protein structures are unknown. The accuracy of using 3D structures of the best templates for the ΔΔG prediction is also unclear.RESULTS: To investigate these questions, we used a representative set of seven diverse and accurate publicly available tools (FoldX, Eris, Rosetta, DDGun, ACDC-NN, ThermoNet and DynaMut) for stability change prediction combined with AlphaFold or I-Tasser for protein 3D structure prediction. We found that best templates perform consistently better than (or similar to) homology models for all ΔΔG predictors. Our findings imply using the best template structure for the prediction of protein stability change upon mutation if the protein 3D structure is not available.AVAILABILITY AND IMPLEMENTATION: The data are available at https://github.com/ivankovlab/template-vs-model.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI10.1093/bioinformatics/btac515
Alternate JournalBioinformatics
PubMed ID35894930