Simultaneous optimization of biomolecular energy function on features from small molecules and macromolecules

By Hahnbeom Park, Philip Bradley, Per Greisen, Yuan Liu, Vikram Khipple Mulligan, David E. Kim, David Baker, Frank DiMaio. 2016

510 citations recorded by the source corpus.

Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking, have been parameterized 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.

Subfields: Materials Chemistry, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Improved protein structure refinement guided by deep learning based accuracy estimation (2020, positive): “With improvements in the Rosetta energy function in the last several years (23, 24), the bottleneck to improving refinement has largely become sampling close to the correct structure.”
Evolutionary context-integrated deep sequence modeling for protein engineering (2020, background): “Rational design is oftentimes assisted by quantitative approaches to evaluate sequences that fold into a specific structure, including biophysical modeling [3,4] , force field approximations [5–7] , structure prediction [8–10] , and molecular dynamics simulations [11] .”
Protein sequence design with a learned potential (2022, methodology): “The REF2015 Rosetta energy function was used for all experiments 16,58 .”
Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, methodology): “We tested five filtering strategies: CASP13 protocol, CASP12 protocol, 20 CASP13 protocol but using RWplus 53 or dDFIRE 54 instead of the Rosetta score, 24 and averaging all sampled conformation without any filtering.”
High-accuracy refinement using Rosetta in CASP13 (2019, background): “11 Next generation structures were selected from the entire pool (including both parents and newly generated structures) based on Rosetta all-atom energy 12 and distance from other members of the new pool.”
Efficient consideration of coordinated water molecules improves computational protein-protein and protein-ligand docking discrimination (2019, methodology): “Two docking data sets were used to test the ability of the new energy functions to discriminate near-native from decoy docking conformations, a subsets of those used by Park et al.[14], but selected for waterrich interfaces (and to exclude problematic cases such as PPIs with disulfides across the in”
Protein structure prediction using Rosetta in CASP12 (2017, methodology): “Local regions to rebuild are identified following Park et al. 16 which uses a well-known correlation between residue-level fluctuation and structural error.”