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Abstract
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.
Highlights
The formation of protein-protein complexes plays an essential role in the regulation of various biological processes
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
Recognizing Mutations on Protein Binding Interactions among people or by disease causing mutations such as those that occur in cancer or in genetic disorders
Summary
The formation of protein-protein complexes plays an essential role in the regulation of various biological processes. Mutations play fundamental roles in evolution by introducing diversity into genomes that can either be selectively advantageous or cause a change in protein affinity that can result in malfunction of the protein interaction network [1, 2]. Knowledge of how individual subpopulations respond to drugs remains a major bottleneck within the drug discovery process. Understanding how this natural variation within the human genome impacts the protein interaction network is expected to yield insight into this process, provided that the impact of a mutation on the formation of a protein complex can be reliably predicted. The rational design or modification of the affinity and specificity of protein-protein interactions is another challenging issue that has stimulated considerable efforts, as it presents many promising applications, notably for both industrial and therapeutic purposes [3]
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