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

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.