A multiscale approach to predicting affinity changes in protein-protein interfaces

Abstract

Substitution mutations in protein-protein interfaces can have a substantial effect on binding, which has consequences in basic and applied biomedical research. Experimental expression, purification, and affinity determination of protein complexes is an expensive and time-consuming means of evaluating the effect of mutations, making a fast and accurate in silico method highly desirable. When the structure of the wild-type complex is known, it is possible to economically evaluate the effect of point mutations with knowledge based potentials, which do not model backbone flexibility, but these have been validated only for single mutants. Substitution mutations tend to induce local conformational rearrangements only. Accordingly, ZEMu (Zone Equilibration of Mutants) flexibilizes only a small region around the site of mutation, then computes its dynamics under a physics-based force field. We validate with 1254 experimental mutants (with 1-15 simultaneous substitutions) in a wide variety of different protein environments (65 protein complexes), and obtain a significant improvement in the accuracy of predicted ΔΔG.

Phosphorylation by PINK1 Releases the UBL Domain and Initializes the Conformational Opening of the E3 Ubiquitin Ligase Parkin

“…In functional studies, Serine is often substituted with Alanine for a phospho-dead version (S65A), while mutations to the negatively charged Aspartic (S65D) or Glutamic (S65E) acid are used as phospho-mimic variants. For critical analysis of the intermolecular interactions induced by these mutations (Figure S3), we computed changes in binding energy (ΔΔG), using the Zone Equilibration of Mutants (ZEMu) [37] method, implemented in the MacroMoleculeBuilder (MMB) [38]. However, we could not detect any significant change among the Ser65 substitutions (Figure S3).…”

“…ZEMu is implemented in MMB [37], [96]. It consists of first, introducing the mutation and finding an energetically favorable local rearrangement around the mutation site, and second, computing the change in interaction energy (ΔΔG) using a knowledge-based (KB) potential.…”

“…Due to the lack of solvent or other rigorous treatment of viscosity, it is possible for small chemical groups such as methyl to spin unnaturally quickly, which results in the variable time step integrator taking very small time steps. To deal with this we artificially scale the inertia matrices of such groups by a factor of 11.0 – empirically found [37] to be sufficient to lengthen time steps without significantly affecting results. This method establishes a flexibility zone and a (generally larger) physics zone in the protein [97], [98].…”

iSEE: Interface Structure, Evolution and Energy-based machine learning predictor of binding affinity changes upon mutations

“…• ZeMu, which can model conformational changes upon mutation using molecular dynamics simulations but relies on FoldX to predict ∆∆G [7].…”

“…The performance of the iSEE ∆∆G predictor was compared with several state-of-the-art ∆∆G predictors on the independent NM and MDM2-p53 test datasets. For the NM dataset, the predicted ∆∆G values of pred1 [9], pred2 [9], CC/PBSA [8], BeAtMuSiC [10] and FoldX [6] were directly extracted from Li et al [9] and those of ZeMu from Dourado's paper [7]. Predictions of mCSM [11] and BindProfX [12] for the NM and MDM2-p53 test datasets were directly obtained from their respective webservers.…”