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Accurate and Rigorous Prediction of the Changes in Protein Free Energies in a Large‐Scale Mutation Scan
Author(s) -
Gapsys Vytautas,
Michielssens Servaas,
Seeliger Daniel,
de Groot Bert L.
Publication year - 2016
Publication title -
angewandte chemie
Language(s) - English
Resource type - Journals
eISSN - 1521-3757
pISSN - 0044-8249
DOI - 10.1002/ange.201510054
Subject(s) - thermostability , force field (fiction) , biological system , energy (signal processing) , field (mathematics) , computer science , computational biology , statistical physics , chemistry , physics , biology , mathematics , statistics , artificial intelligence , biochemistry , enzyme , pure mathematics
The prediction of mutation‐induced free‐energy changes in protein thermostability or protein–protein binding is of particular interest in the fields of protein design, biotechnology, and bioengineering. Herein, we achieve remarkable accuracy in a scan of 762 mutations estimating changes in protein thermostability based on the first principles of statistical mechanics. The remaining error in the free‐energy estimates appears to be due to three sources in approximately equal parts, namely sampling, force‐field inaccuracies, and experimental uncertainty. We propose a consensus force‐field approach, which, together with an increased sampling time, leads to a free‐energy prediction accuracy that matches those reached in experiments. This versatile approach enables accurate free‐energy estimates for diverse proteins, including the prediction of changes in the melting temperature of the membrane protein neurotensin receptor 1.