EvoDesign: Designing Protein-Protein Binding Interactions Using Evolutionary Interface Profiles in Conjunction with an Optimized Physical Energy Function

Abstract

EvoDesign (https://zhanglab.ccmb.med.umich.edu/EvoDesign) is an online server system for protein design. The method uses evolutionary profiles to guide the sequence search simulation and demonstrated significant advantages over physics-based approaches in terms of more accurately designing proteins that adopt desired target folds. Despite the success, the previous EvoDesign program focused only on monomer protein design, which limited its ability and usefulness in terms of designing functional proteins. In this work, we propose a new EvoDesign server, which extends the principles of evolution-based design to design protein-protein interactions. Starting from a two-chain complex structure, structurally similar interfaces are identified from known protein-protein interaction databases. An interface evolutionary profile is then constructed from a multiple sequence alignment of the interface analogies, which is combined with a newly developed, atomic-level physical energy function to guide the replica-exchange Monte Carlo simulation search. The purpose of the server is to redesign the specified complex chain to increase its stability and binding affinity for the other chain in the complex. With the improved scope and accuracy of the methodology, the new EvoDesign pipeline should become a useful online tool for functional protein design and drug discovery studies.

Keywords: interface profile; physical force field; protein design; protein structure prediction; protein–protein interaction.

Figure 1.

Flowchart of the EvoDesign pipeline for PPI design. Starting from a given protein complex, similar monomer and interface structures are identified from monomer and dimer structure libraries for the scaffold and protein complex, respectively. Alignments of the structural analogies are used to create evolutionary profiles. These profiles are used as energy terms in conjunction with a physical energy function, EvoEF, to guide the replica-exchange Monte Carlo simulation. After clustering the sequence decoys generated during the simulation, the final designs are selected from the lowest free energy sequences in the largest clusters.

Figure 2.

Correlation between predicted and experimental values for mutation-induced folding stability and binding affinity changes. (A, B) Folding stability changes upon mutation, $\Delta \Delta G_{stability}^{WT\to mut},$ for monomer proteins predicted by EvoEF (A) and FoldX (B) versus the experimental data for 1,994 test proteins. (C, D) Binding affinity changes upon mutation in the interface of protein-protein complexes, $\Delta \Delta G_{binding}^{WT\to mut}$, predicted by EvoEF (C) and FoldX (D) versus the experimental data for 1,102 test proteins.