Identifying critical residues in protein-protein binding and efficiently designing stable and specific protein binders to target another protein is challenging. In addition to direct contacts in a protein-protein binding interface, our study employs computation modeling to reveal the essential network of residue interaction and dihedral angle correlation critical in protein-protein recognition. We propose that mutating residues regions exhibited highly correlated motions within the interaction network can efficiently optimize protein-protein interactions to create tight and selective protein binders. We validated our strategy using ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complexes, where Ub is one central player in many cellular functions and PLpro is an antiviral drug target. Molecular dynamics simulations and experimental assays were used to predict and verify our designed Ub variant (UbV) binders. Our designed UbV with 3 mutated residues resulted in a ~3,500-fold increase in functional inhibition, compared with the wild-type Ub. Further optimization by incorporating 2 more residues within the network, the 5-point mutant achieved a KD of 1.5 nM and IC50 of 9.7 nM. The modification led to a 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study illustrates the importance of residue correlation and interaction networks in protein-protein interaction and introduces a new approach that can effectively design high affinity protein binder for cell biology studies and future therapeutic solution.
Keywords: Dihedral correlation network; nanomolar binder; papain-like protease; protein-based inhibitors; ubiquitin variant.