3.4 Multiple factors govern the interaction of hotspot residues
To examine the predominant forces governing the interaction for the hotspot residues, it would be worthwhile to correlate ∆∆G values of hotspot residues with respect to changes in various types of interactions upon mutation. Multiple linear regression analysis was carried out to examine the correlation between ∆∆G values with energy contributions from Coulomb, lipophilic, hydrogen bonding, van der Waal’s interactions, packing desolvation, entropy and surface complementarity. Strong correlation exists (in the range of 0.9 -1, with p-value close to 0) between the binding affinity ∆∆G and its components (∆∆G of Coulomb, lipophilic, hydrogen bonding, van der Waal’s interactions and desolvation). Notably, these interactions contribute in equal proportion in the making of hotspot residues and no single factor predominantly governs over other in the hotspot characteristics across all three weak, moderate and strong types. As peptide has binding characteristic similar to protein-ligand interactions, some degree of correlation (~0.1) was also observed between ∆∆G and its component ∆∆G (surface complementarity) in PPepI.
CONCLUSION
Peptides and peptidomimetics are straightforward alternatives to protein-based biologics due to multiple advantages of larger shelf life, feasibility of oral delivery, flexibility of optimization, screening and synthesis. The knowledge of subtle differences between protein-protein and protein-peptide interactions should aid in the effective design of peptide-based biologics. In the present study, we have focussed on two important class of residues, namely, hotspot and anchor residues, to characterize differences between protein-protein and protein-peptide interactions.
Using implicit solvation-based free energy calculations, alanine scanning has been extensively performed on benchmarking datasets and hotspot and anchor residues were identified, which has revealed many interesting findings. The presence of sizable population (about 65%) of hotspot residues at the interface of the complex suggest that nature has remarkably optimized a great majority of the interface residues responsible for protein-protein interactions during evolution. It turned out that the differences in the two categories – PPI and PPepI are readily apparent, once we group the hotspot data into three distinct types, namely - weak hotspots (having binding free energy loss upon Ala mutation, ΔΔG in 2-10 kcal/mol range), moderate hotspots (ΔΔG, 10-20 kcal/mol) and strong hotspots (ΔΔG, 20 kcal/mol and higher). Correlation studies using MLR suggest that calculated free energy of binding of hotspot directly correlate with coulomb, lipophilic, hydrogen bonding, van der Waal interactions and desolvation penalty and no specific preference of any of the factor(s) over other was observed across all three types of hotspots. The analysis suggests that for PPI the preference is charged and polar followed by hydrophobic residues while for PPepI it is polar and hydrophobic followed by charged residues. In PPI, weak hotspots are predominantly populated by polar and hydrophobic residues. The distribution shifts towards charged and polar residues for moderate type, and charged residue (Arg) is overwhelmingly present in the strong type. In contrast, in the protein-peptide dataset, the distribution shifts from predominantly hydrophobic & polar (in the weak type) to more or less similar preference for polar, hydrophobic and charged residues and finally the charged residue (Arg) and Trp are mostly occupied in the strong type. Similar trend has been observed for anchor residues in both categories. The present work is an attempt to characterize and distinguish PPI and PPepI, focussing on two important class of residues. Further work is required to facilitate the discovery of new generations of peptide and peptidomimetic modulators. which can be utilized in the effective design of biologics.
Acknowledgement Kiran Kumar thanks UGC, India for PhD fellowship.