This is a PLOS Computational Biology Methods paper ![]() The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: The project is supported in part by the National Institute of General Medical Sciences (GM083107) granted to YZ. Received: MaAccepted: AugPublished: October 27, 2015Ĭopyright: © 2015 Brender, Zhang. Jernigan, Iowa State University, UNITED STATES PLoS Comput Biol 11(10):Įditor: Robert L. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.Ĭitation: Brender JR, Zhang Y (2015) Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles. 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. 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. 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. 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. 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. 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. ![]() Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell.
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