Accurate Estimation of Ligand Binding Affinity Changes upon Protein Mutation

The
design of proteins with novel ligand-binding functions holds
great potential for application in biomedicine and biotechnology.
However, our ability to engineer ligand-binding proteins is still
limited, and current approaches rely primarily on experimentation.
Computation could reduce the cost of the development process and would
allow rigorous testing of our understanding of the principles governing
molecular recognition. While computational methods have proven successful
in the early stages of the discovery process, optimization approaches
that can quantitatively predict ligand affinity changes upon protein
mutation are still lacking. Here, we assess the ability of free energy
calculations based on first-principles statistical mechanics, as well
as the latest Rosetta protocols, to quantitatively predict such affinity
changes on a challenging set of 134 mutations. After evaluating different
protocols with computational efficiency in mind, we investigate the
performance of different force fields. We show that both the free
energy calculations and Rosetta are able to quantitatively predict
changes in ligand binding affinity upon protein mutations, yet the
best predictions are the result of combining the estimates of both
methods. These closely match the experimentally determined ΔΔG values, with a root-mean-square error of 1.2 kcal/mol
for the full benchmark set and of 0.8 kcal/mol for a subset of protein
systems providing the most reproducible results. The currently achievable
accuracy offers the prospect of being able to employ computation for
the optimization of ligand-binding proteins as well as the prediction
of drug resistance.