Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

By Jeffrey R. Brender, Yang Zhang. 2015

147 citations recorded by the source corpus.

The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.

Subfields: Molecular Biology, Physical and Theoretical Chemistry

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Citations in the corpus

Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XII (2017, background): “32 Prediction of the correct quaternary structure of a protein is therefore an essential aspect of protein structure modelling in order to provide models useful for biological applications.”
DisruPPI: structure-based computational redesign algorithm for protein binding disruption (2018, background): “…including all-atom molecular dynamics (Deng and Roux, 2009; Moretti et al. , 2013; Weis et al. , 2006), empirically derived physical potentials (Brender and Zhang, 2015; Guerois et al. , 2002; Kortemme and Baker, 2004), statistical contact potentials (Pons et al. , 2011; Tharakaraman et al. ,…”
EvoDesign: Designing protein-protein binding interactions using evolutionary interface profiles in conjunction with an optimized physical energy function (2019, unknown): “Finally, the evolution-based binding affinity change for each mutation at the interface is determined by the logarithm of the relative probability of each mutant amino acid compared to the wild type amino acid in the interface profile [13, 16].”
STRUM: structure-based prediction of protein stability changes upon single-point mutation (2016, methodology): “FoldX was recently exploited by BindProf (Brender and Zhang, 2015), which shows that it can improve the prediction accuracy of mutation effects on protein binding interactions when combined with structure-based interface profiles.”
Changing the Apoptosis Pathway through Evolutionary Protein Design (2019, background): “The binding potentials can be either physical [20] or evolutionary based [21,22].”