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Predicting changes in protein stability caused by mutation using sequence‐and structure‐based methods in a CAGI5 blind challenge
Author(s) -
Strokach Alexey,
CorbiVerge Carles,
Kim Philip M.
Publication year - 2019
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23852
Subject(s) - biology , stability (learning theory) , set (abstract data type) , computational biology , protocol (science) , mutation , computer science , interface (matter) , protein stability , data mining , bioinformatics , algorithm , genetics , machine learning , gene , medicine , pulmonary surfactant , biochemistry , gibbs isotherm , alternative medicine , pathology , programming language , microbiology and biotechnology
Predicting the impact of mutations on proteins remains an important problem. As part of the CAGI5 frataxin challenge, we evaluate the accuracy with which Provean, FoldX, and ELASPIC can predict changes in the Gibbs free energy of a protein using a limited data set of eight mutations. We find that different methods have distinct strengths and limitations, with no method being strictly superior to other methods on all metrics. ELASPIC achieves the highest accuracy while also providing a web interface which simplifies the evaluation and analysis of mutations. FoldX is slightly less accurate than ELASPIC but is easier to run locally, as it does not depend on external tools or datasets. Provean achieves reasonable results while being computational less expensive than the other methods and not requiring a structure of the protein. In addition to methods submitted to the CAGI5 community experiment, and with the aim to inform about other methods with high accuracy, we also evaluate predictions made by Rosetta's ddg_monomer protocol, Rosetta's cartesian_ddg protocol, and thermodynamic integration calculations using Amber package. ELASPIC still achieves the highest accuracy, while Rosetta's catesian_ddg protocol appears to perform best in capturing the overall trend in the data.