What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks.

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.

What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks.

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.

What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks.

Identifying residues critical to protein-protein binding and efficient design of stable and specific protein binders are challenging tasks. Extending beyond the direct contacts in a protein-protein binding interface, our study employs computational modeling to reveal the essential network of residue interactions and dihedral angle correlations critical in protein-protein recognition. We hypothesized that mutating residues exhibiting highly correlated dynamic motion within the interaction network could efficiently optimize protein-protein interactions to create tight and selective protein binders. We tested this hypothesis using the ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complex, since Ub is a central player in multiple cellular functions and PLpro is an antiviral drug target. Our designed ubiquitin variant (UbV) hosting three mutated residues displayed a ∼3,500-fold increase in functional inhibition relative to wild-type Ub. Further optimization of two C-terminal residues within the Ub network resulted in a KD of 1.5 nM and IC50 of 9.7 nM for the five-point Ub mutant, eliciting 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 interactions, and introduces an effective approach to design high-affinity protein binders for cell biology research and future therapeutics.

Robust Design of Effective Allosteric Activators for Rsp5 E3 Ligase Using the Machine Learning Tool ProteinMPNN.

We used the deep learning tool ProteinMPNN to redesign ubiquitin (Ub) as a specific and functionally stimulating/enhancing binder of the Rsp5 E3 ligase. We generated 20 extensively mutated─up to 37 of 76 residues─recombinant Ub variants (UbVs), named R1 to R20, displaying well-folded structures and high thermal stabilities. These UbVs can also form stable complexes with Rsp5, as predicted using AlphaFold2. Three of the UbVs bound to Rsp5 with low micromolar affinity, with R4 and R12 effectively enhancing the Rsp5 activity six folds. AlphaFold2 predicts that R4 and R12 bind to Rsp5's exosite in an identical manner to the Rsp5-Ub template, thereby allosterically activating Rsp5-Ub thioester formation. Thus, we present a virtual solution for rapidly and cost-effectively designing UbVs as functional modulators of Ub-related enzymes.

Direct reprogramming of hepatocytes into insulin-producing cells for anti-diabetic treatment by ultrasound-targeted microbubble destruction enhanced hydrodynamic gene delivery.

In animal models, hepatocytes can be reprogrammed into insulin-producing cells (IPCs) for a novel antidiabetic treatment. However, the potential for an immunologic reaction and issues with gene integration of the viral vehicle hamper system efficacy. Here, we adopted an Ultrasound Targeted Microbubble Destruction (UTMD) enhanced hydrodynamic gene delivery system in a streptozotocin induced mouse diabetic model to examine its treatment effect. After transfection by combining UTMD and hydrodynamic injection, accumulated luciferase signal was only found in the liver with optimal signal intensity. Liver function tests showed an increase in alanine aminotransferase level followed by a decrease to normal levels. Then this new gene delivery system was used to deliver Pdx1, Neurog3, and MafA plasmids into diabetic mice. We found that glucose levels gradually decreased, and insulin levels increased in transfected diabetic mice compared to controls. Glucose intolerance in transfected mice was alleviated. Gene expression assay confirmed the reprogramming of hepatocytes. We demonstrated the feasibility of repeated plasmid transfection in vivo by UTMD enhanced hydrodynamic gene delivery system.