Engineering Nanomolar Potent Protein-based Inhibitors for Papain-like Protease Guided by Residue Correlation Network
Identifying critical residues in protein-protein binding and efficiently designing stable and specific protein binders 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. 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 highlights residue correlation and interaction networks in protein-protein interaction, introduces an effective approach to design high affinity protein binders for cell biology and future therapeutics solutions.