Binding free energy prediction
The binding free energy of the wild-type protein complex and the change in binding free energy upon mutation was predicted using different predictive tools, such as BeAtMuSiC [43], mCSM-PPI2 [44], mmCSM-PPI [45], MutaBind2 [46] and HawkDock [47]. BeAtMuSiC is a coarse-grained prediction tool for the binding free energy changes as a result of point mutations. The algorithm depends on a set of statistical potentials extracted from proteins with known structures and combines the mutation effect on the overall complex stability and on the strength of the protein-protein interactions at the interface [43]. mCSM-PPI2 is a novel machine learning tool developed for the precise prediction of missense mutation effects on the binding affinity of protein-protein interactions. The tool utilizes graph-based structural signatures for the modeling of variation effects on energetic terms, complex network metrics, evolutionary information and inter-residue interaction network for the generation of an optimized prediction tool [44]. mmCSM-PPI is an effective and scalable machine learning tool for the accurate assessment of protein-protein interaction binding affinity changes resulting from multiple and single missense mutations. The tool utilizes a well-established graph-based signature in capturing geometrical and physiochemical properties of various wild-type residues and integrates them with both normal mode analysis dynamics terms and substitution scores [45]. MutaBind2 estimates protein-protein interaction binding affinity changes as a result of single- and multiple-site mutations in corresponding sequences. The tool makes predictions based on the protein-protein complex structure. MutaBind2 uses rapid side chain optimization algorithms built through random forest method, mechanics force fields and statistical potentials. The training set used for the development of multiple and single models of mutation consist of 1707 multiple mutations from 120 protein complexes and 4191 single mutations from 265 protein complexes respectively [46]. The development of HawkDock was targeted at the prediction and analysis of protein-protein interactions through the integration of the MM/GBSA free energy decomposition analysis, ATTRACT docking algorithm and the HawkRank scoring function. The integration of MM/GBSA into HawkDock is to serve the purpose of analyzing important residues in the binding interface of protein-protein interactions and also for the purpose of model re-ranking [47].