Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity

Abstract

The crucial prerequisite for proper biological function is the protein’s ability to establish highly selective interactions with macromolecular partners. A missense mutation that alters the protein binding affinity may cause significant perturbations or complete abolishment of the function, potentially leading to diseases. The availability of computational methods to evaluate the impact of mutations on protein–protein binding is critical for a wide range of biomedical applications. Here, we report an efficient computational approach for predicting the effect of single and multiple missense mutations on protein–protein binding affinity. It is based on a well-tested simulation protocol for structure minimization, modified MM-PBSA and statistical scoring energy functions with parameters optimized on experimental sets of several thousands of mutations. Our simulation protocol yields very good agreement between predicted and experimental values with Pearson correlation coefficients of 0.69 and 0.63 and root-mean-square errors of 1.20 and 1.90 kcal mol–1 for single and multiple mutations, respectively. Compared with other available methods, our approach achieves high speed and prediction accuracy and can be applied to large datasets generated by modern genomics initiatives. In addition, we report a crucial role of water model and the polar solvation energy in estimating the changes in binding affinity. Our analysis also reveals that prediction accuracy and effect of mutations on binding strongly depends on the type of mutation and its location in a protein complex.

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

“…We iSEE competes with state-of-the-art ∆∆G predictors. We evaluated the performance of our iSEE ∆∆G predictor on the blind Benedix et al NM dataset [8] (see Methods) and compared it to several other state-of-the-art ∆∆G predictors based on empirical potentials or machine learning methods, which have been tested by Li et al [9] on the same data set. We only selected data from the NM data set for mutations that were not represented in the training data, which left 19 single point mutations for one complex (PDB ID: 1IAR).…”

“…• CC/PBSA [8], pred1 [9] and pred2 [9], which generate an ensemble of structures and apply a Molecular Mechanics -Poisson-Boltzmann Surface Area (MM-PBSA) approach to calculate the binding free energy.…”

“…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.…”

Balancing Protein Stability and Activity in Cancer: A New Approach for Identifying Driver Mutations Affecting CBL Ubiquitin Ligase Activation

“…We applied our recently developed optimization protocol for minimizing wild-type and mutant structures (20). Heavy side chain atoms without known coordinates and hydrogen atoms were added to the crystal structures using the VMD (version 1.9.1) program (21) with models immersed into rectangular boxes of water molecules extending up to 10 Å from the protein in each direction.…”

Computational protein design for given backbone: recent progresses in general method-related aspects

“…For proteinprotein binding affinity, Li et al proposed a molecular simulation protocol that uses molecular mechanics energies combined with Poisson-Boltzmann and solvent accessibility-based effective free energies (i.e. MM-PBSA) to predict the effects of single and double mutations [41]. Such molecular simulation-derived energies, retrained using quantitatively measured experimental data if possible, may be used as an effective metric for assessing sequences proposed through CPD.…”