Introduction
Protein-protein interactions (PPI) and protein-peptide interactions (hereafter referred to as -PPepI) play a crucial role in many cell-signaling and metabolic processes. In recent years, they have become attractive targets for drug and biologics discovery [1-8]. To interfere or modulate PPI, protein-protein complex may be disrupted or interactions may be augmented by peptides or mimetics. Peptides have the advantage of larger shelf life, less prone to proteolysis, feasibility of their oral delivery and flexibility in optimization, screening and synthesis, similar to small molecule based drug. Thus, to augment or disrupt PPI by peptide-based therapeutic, it is worthwhile to understand the differences between PPI and PPepI.
To characterize differences between PPI and PPepI, in the present work we have focused on two important class of residues, namely, hotspot and anchor residues. At the interface of molecular complex, certain residues play a crucial role in governing such interactions. These residues contributing most to the binding of molecular complexes are termed as hotspot residues. As a rule of thumb, a hotspot residue is defined, if its energy contribution to the binding of complex is more than 2 kcal/mol [9-10]. The binding affinity and specificity of the PPI and PPepI are in essence governed by such energetically important hotspot residues. Mutations at hotspots have been shown to cause dissociation of complexes or disruption in the signaling processes [11]. Additionally, anchoring residue also play specific role in the initial stage of molecular recognition. They avoid kinetically costly structural rearrangements in the binding pathway allowing for a relatively smooth recognition process [12]. Anchor residues are hotspot residues that bury the highest amounts of solvent accessible surface area ( ≥ 100 Å2) upon binding [12-13].
To quantitatively probe the energetic contributions of residue to the overall binding of molecular complexes, earlier experimental alanine scanning mutagenesis were attempted [9]. This involves experimentally mutating each residue at the interface to alanine and then measuring the effect of the mutation on binding to the partner protein or peptide. Extending the previous definition of hotspot more specifically with the Ala-scanning method, a residue is considered a hotspot residue, if its mutation to alanine gives rise to loss in binding affinity ≥ 2 kcal/mol. Early on, limited data were generated using experiment mutagenesis for many diverse complexes [14-22], and few databases on experimentally determined hotspot residues in protein-protein complexes such as AB-bind [14], ASEdb [15], ATLAS [16], BID [17], DACUM [18], dbAMEPNI [19], dbMPIKT [20], PROXiMATE [21] and SKEMPI 2.0 [22] have also been developed. The identification of hotspot residues by experimental techniques is a costly and time consuming job, which has led to the development of a series of computational algorithms to predict hotspots at the protein-protein interfaces [1, 23-39]. Serrano’s group have developed FoldX based on an empirical potential for rapid evaluation of effect of mutations in proteins and nucleic acids [23,24]. Flex_ddG method in Rosetta uses a combination of sophisticated Monte Carlo sampling, minimization, and specialized force fields [25-27]. BeAtMuSiC is a tool, which employ statistical potentials adapted to a coarse-grained representation of protein structures [28] and mCSM (mutation Cutoff Scanning Matrix) is based on graph-based structural signatures, which encodes distance patterns between atoms to represent protein residue environments [29]. Various other methods and their applications have also been discussed in the literature [30-36, 44-45].
In the present work, we have used Bioluminate Residue scanning approach implemented in Schrödinger [37-38]. The method uses implicit solvation based MM-GBSA (Molecular Mechanics with Generalized Born and Surface Area) with the OPLS2005 force field, VSGB2.0 solvent model and rotamer search algorithms from Prime [37-38]. To understand the differences in the nature of hotspots in protein-protein and protein-peptide complexes using benchmarking data sets, a comprehensive Ala scanning, comprising of over 5500 mutation analyses were performed.
Materials and Methods