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Cited by 295

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

Open access
Barlow, Kyle A., Ó Conchúir, Shane, Thompson, Samuel, Suresh, Pooja, Lucas, James E., Heinonen, Markus, Kortemme, Tanja·2018·The Journal of Physical Chemistry B
DOI 10.1021/acs.jpcb.7b11367

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Abstract

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 generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ 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 ΔΔ G values but also highlighted the necessity of future energy function improvements.

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Cited by

Metadata

Authors
Barlow, K. A., Ó Conchúir, S., Thompson, S., Suresh, P., Lucas, J. E., Heinonen, M., & Kortemme, T.
Venue
The Journal of Physical Chemistry B
Publisher
American Chemical Society (ACS)
Published
2018-02-05
Volume
122
Issue
21
Pages
5389-5399
ISSN
  • 1520-5207 (ISSN-L)
  • 1520-6106
DOI
10.1021/acs.jpcb.7b11367
OpenAlex
W2780523853
Cited by
295
References
12
FWCI
6.36
Type
journal article
Language
en
Open access
green
Funders
  • Suomen Akatemia (299915)
  • National Institute of General Medical Sciences (R01 GM110089, R01 GM117189)
  • National Science Foundation

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