A REVIEW OF METHODS DEVELOPED FOR PREDICTION OF PROTEIN-PROTEIN INTERACION SITES

Biological function of gene products such as proteins mediate through interactions they make with one another. In humans, a larger numbers of proteins are expected to engage in hundreds of thousands of interactions, many of which involve large assemblies and play key roles in cellular function and disease. Such assemblies are, however, still rather poorly represented in the Protein Data Bank (PDB). Computational procedures capable of reliably generating structural models of multi-protein assemblies starting from the atomic coordinates of individual components, the so-called “protein-protein docking” methods, will help in generating models of protein complexes. The 3D structures of protein complexes are pivotal for a full understanding of the mechanism of interactions because they provide specific interaction details at the atomic level. Such details are important for rational design of drug molecules to modulate protein interactions. In order to predict the interface regions one needs to know what distinguishes an interface region from the rest of the protein. Several attempts have been made in the literature using physicochemical properties like surface residue energy distributions, residue conservation and propensity information and salvation energies etc and also using numerical and probabilistic methods some of which uses these properties. However none of these are significantly successful in predicting or modeling the structures. This review paper gives an overview of the various methods developed in the literature for predicting the interaction sites on the protein surfaces over the last fifteen years.