Pairwise decomposition of an MMGBSA energy function for computational protein design

Gaillard, Thomas; Simonson, Thomas

doi:10.1002/jcc.23637

Cited by 42 publications

(79 citation statements)

References 120 publications

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“…However, rotamer elimination criteria have only been defined for pairwise-additive energy functions such as the OPLS-AA (18), AMBER (19), and CHARMM (20) families of fixed partial-charge force fields and pairwise decomposable continuum solvents (21)(22)(23). Explicit inclusion of manybody effects has been neglected such that the strength of the interaction between two residues must be independent of their mutual environment.…”

Section: Introductionmentioning

confidence: 99%

Dead-End Elimination with a Polarizable Force Field Repacks PCNA Structures

LuCore

Litman

Powers

et al. 2015

Biophysical Journal

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A balance of van der Waals, electrostatic, and hydrophobic forces drive the folding and packing of protein side chains. Although such interactions between residues are often approximated as being pairwise additive, in reality, higher-order many-body contributions that depend on environment drive hydrophobic collapse and cooperative electrostatics. Beginning from dead-end elimination, we derive the first algorithm, to our knowledge, capable of deterministic global repacking of side chains compatible with many-body energy functions. The approach is applied to seven PCNA x-ray crystallographic data sets with resolutions 2.5-3.8 Å (mean 3.0 Å) using an open-source software. While PDB_REDO models average an Rfree value of 29.5% and MOLPROBITY score of 2.71 Å (77th percentile), dead-end elimination with the polarizable AMOEBA force field lowered Rfree by 2.8-26.7% and improved mean MOLPROBITY score to atomic resolution at 1.25 Å (100th percentile). For structural biology applications that depend on side-chain repacking, including x-ray refinement, homology modeling, and protein design, the accuracy limitations of pairwise additivity can now be eliminated via polarizable or quantum mechanical potentials.

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Section: Introductionmentioning

confidence: 99%

Dead-End Elimination with a Polarizable Force Field Repacks PCNA Structures

LuCore

Litman

Powers

et al. 2015

Biophysical Journal

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“…Another weakness concerns the solvation model; the current formulation has weakness describing the screening effect of electrostatic interactions by water or ions, in contrast to the more computationally expensive Poisson-Boltzmann (PB) or generalized Born (GB) solvation models. Incorporating such properties in an efficient pairwise decomposable manner is a difficult but potentially valuable future research effort 44 . Lastly, a more profound challenge in an implicit solvation model will be describing effects from well-ordered waters that form an essential part of biomolecular structures and interfaces.…”

Section: Discussionmentioning

confidence: 99%

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

Park¹,

Bradley

Greisen³

et al. 2016

J. Chem. Theory Comput.

457

599

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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.

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“…Negative values indicate a preference to be solvent‐exposed. To avoid overcounting of surface burial, for residue pairs involving at least one buried side chain, the surface energy was scaled by 0.65 …”

Section: Methodsmentioning

confidence: 99%

“…Side chains explore a discrete set of preferred rotamers, so that conformational space is finite. The energy is approximated as a sum of pairwise terms that involve just one or two amino acids; that is, it is pairwise additive . With a discrete conformational space and a pairwise additive energy, interaction energies can be computed ahead of time and stored in a lookup table, called the energy matrix .…”

Section: Introductionmentioning

confidence: 99%

Comparing pairwise‐additive and many‐body generalized Born models for acid/base calculations and protein design

et al. 2017

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Generalized Born (GB) solvent models are common in acid/base calculations and protein design. With GB, the interaction between a pair of solute atoms depends on the shape of the protein/solvent boundary and, therefore, the positions of all solute atoms, so that GB is a many-body potential. For compute-intensive applications, the model is often simplified further, by introducing a mean, native-like protein/solvent boundary, which removes the many-body property. We investigate a method for both acid/base calculations and protein design that uses Monte Carlo simulations in which side chains can explore rotamers, bind/release protons, or mutate. The fluctuating protein/solvent dielectric boundary is treated in a way that is numerically exact (within the GB framework), in contrast to a mean boundary. Its originality is that it captures the many-body character while retaining the residue-pairwise complexity given by a fixed boundary. The method is implemented in the Proteus protein design software. It yields a slight but systematic improvement for acid/base constants in nine proteins and a significant improvement for the computational design of three PDZ domains. It eliminates a source of model uncertainty, which will facilitate the analysis of other model limitations. © 2017 Wiley Periodicals, Inc.

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Pairwise decomposition of an MMGBSA energy function for computational protein design

Cited by 42 publications

References 120 publications

Dead-End Elimination with a Polarizable Force Field Repacks PCNA Structures

Dead-End Elimination with a Polarizable Force Field Repacks PCNA Structures

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

Comparing pairwise‐additive and many‐body generalized Born models for acid/base calculations and protein design

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