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J Chem Theory Comput. 2016 Dec 13;12(12):6201-6212. Epub 2016 Nov 7.

Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules.

Author information

1
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center , 1100 Fairview Avenue N., Seattle, Washington 98019, United States.

Abstract

Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking have been parametrized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties.

PMID:
27766851
PMCID:
PMC5515585
DOI:
10.1021/acs.jctc.6b00819
[Indexed for MEDLINE]
Free PMC Article

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