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

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Kyle Barlow
  • Shane Conchuir
  • Samuel Thompson
  • Pooja Suresh
  • James Lucas
  • Markus Heinonen

  • Tanja Kortemme

Research units

  • University of California at San Francisco

Abstract

Computationally modeling changes in binding free energies upon mutation (interface ΔΔG) 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 ΔΔG 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 ΔΔG values but also highlighted the necessity of future energy function improvements.

Details

Original languageEnglish
Pages (from-to)5389-5399
Number of pages11
JournalJournal of Physical Chemistry B
Volume122
Issue number21
Publication statusPublished - 31 May 2018
MoE publication typeA1 Journal article-refereed

ID: 18164600