Pak MA, Ivankov DN. Best templates outperform homology models in predicting the impact of mutations on protein stability.
Bioinformatics 2022;
38:4312-4320. [PMID:
35894930 DOI:
10.1093/bioinformatics/btac515]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 05/31/2022] [Indexed: 12/24/2022] Open
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.
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