Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method

A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).Author Summary: Developing methods for accurate prediction of effects of amino acid substitutions on protein-protein affinity is important for both understanding disease-causing mechanism of missense mutations and guiding protein engineering. For both purposes, there is a need for accurate methods primarily based on first principle calculations, while being fast enough to handle large number of cases. Here we report a new method, the Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) method. The core of the SAAMBE method is a modified molecular mechanics Poisson-Boltzmann Surface Area (MM/PBSA) method with residue specific dielectric constant. Adopting residue specific dielectric constant allows for mimicking the effects of plausible conformational changes induced by the binding on the solvation energy without performing computationally expensive explicit modeling. This makes the SAAMBE algorithm fast, while still capable of capturing many of the explicit effects associated with the binding. The performance of the SAAMBE protocol was tested against experimentally determined binding free energy changes over 1300 mutations in 43 proteins and very good correlation coefficient was obtained. Due to its computational efficiency, the SAAMBE method will be soon implemented into webserver and made available to the computational community.