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Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme
Author(s) -
Hayes Ryan L.,
Vilseck Jonah Z.,
Brooks Charles L.
Publication year - 2018
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.3500
Subject(s) - scalability , protein design , folding (dsp implementation) , molecular dynamics , protein folding , energy landscape , computer science , protein engineering , computational biology , protein dynamics , energy (signal processing) , protein structure , physics , biological system , chemistry , biology , computational chemistry , enzyme , thermodynamics , biochemistry , quantum mechanics , database , electrical engineering , engineering
The estimation of changes in free energy upon mutation is central to the problem of protein design. Modern protein design methods have had remarkable success over a wide range of design targets, but are reaching their limits in ligand binding and enzyme design due to insufficient accuracy in mutational free energies. Alchemical free energy calculations have the potential to supplement modern design methods through more accurate molecular dynamics based prediction of free energy changes, but suffer from high computational cost. Multisite λ dynamics (MS λ D) is a particularly efficient and scalable free energy method with potential to explore combinatorially large sequence spaces inaccessible with other free energy methods. This work aims to quantify the accuracy of MS λ D and demonstrate its scalability. We apply MS λ D to the classic problem of calculating folding free energies in T4 lysozyme, a system with a wealth of experimental measurements. Single site mutants considering 32 mutations show remarkable agreement with experiment with a Pearson correlation of 0.914 and mean unsigned error of 1.19 kcal/mol. Multisite mutants in systems with up to five concurrent mutations spanning 240 different sequences show comparable agreement with experiment. These results demonstrate the promise of MS λ D in exploring large sequence spaces for protein design.