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].