Reliable Prediction of the Protein–Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase

Protein
mutations that directly impair drug binding are related
to therapeutic resistance, and accurate prediction of their impact
on drug binding would benefit drug design and clinical practice. Here,
we have developed a scoring strategy that predicts the effect of the
mutations on the protein–ligand binding affinity. In view of
the critical importance of electrostatics in protein–ligand
interactions, the charge penetration corrected AMOEBA force field
(AMOEBA_CP model) was employed to improve the accuracy of the calculated
electrostatic energy. We calculated the electrostatic energy using
an energy decomposition analysis scheme based on the generalized Kohn–Sham
(GKS-EDA). The AMOEBA_CP model was validated by a protein-fragment–ligand
complex data set (Abl236) constructed from the co-crystal structures
of the cancer target Abl kinase with six inhibitors. To predict ligand
binding affinity changes upon protein mutation of Abl kinase, we used
sampling protocol with multistep simulated annealing to search conformations
of mutant proteins. The scoring strategy based on AMOEBA_CP model
has achieved considerable performance in predicting resistance for
8 kinase inhibitors across 144 clinically identified point mutations.
Overall, this study illustrates that the AMOEBA_CP model, which accurately
treats electrostatics through penetration correction, enables the
accurate prediction of the mutation-induced variation of protein–ligand
binding affinity.