Unknown

Dataset Information

0

Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation.


ABSTRACT: Computationally modeling changes in binding free energies upon mutation (interface ?? G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ?? G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ?? G values but also highlighted the necessity of future energy function improvements.

SUBMITTER: Barlow KA 

PROVIDER: S-EPMC5980710 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

altmetric image

Publications

Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation.

Barlow Kyle A KA   Ó Conchúir Shane S   Thompson Samuel S   Suresh Pooja P   Lucas James E JE   Heinonen Markus M   Kortemme Tanja T  

The journal of physical chemistry. B 20180215 21


Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to genera  ...[more]

Similar Datasets

| S-EPMC6311686 | biostudies-literature
| S-EPMC10311296 | biostudies-literature
| S-EPMC4170975 | biostudies-literature
| S-EPMC2711448 | biostudies-literature
| S-EPMC10777193 | biostudies-literature
| S-EPMC3113940 | biostudies-literature
| S-EPMC7223817 | biostudies-literature
| S-EPMC6361233 | biostudies-literature
| S-EPMC3692068 | biostudies-literature
| S-EPMC4987957 | biostudies-literature