Please use this identifier to cite or link to this item:
https://doi.org/10.1186/s12859-015-0471-x
Title: | PCalign: A method to quantify physicochemical similarity of protein-protein interfaces | Authors: | Cheng, S Zhang, Y Brooks, C.L |
Keywords: | Bioinformatics Biology Biological relationships Chemical characteristic Convergent evolution Macromolecular complexes Protein-protein interactions Protein-protein interface Robustness against noise Structural bioinformatics Proteins protein protein binding algorithm chemical structure chemistry computer program human metabolism physical chemistry Algorithms Humans Models, Molecular Physicochemical Phenomena Protein Binding Proteins Software |
Issue Date: | 2015 | Citation: | Cheng, S, Zhang, Y, Brooks, C.L (2015). PCalign: A method to quantify physicochemical similarity of protein-protein interfaces. BMC Bioinformatics 16 (1) : 33. ScholarBank@NUS Repository. https://doi.org/10.1186/s12859-015-0471-x | Rights: | Attribution 4.0 International | Abstract: | Background: Structural comparison of protein-protein interfaces provides valuable insights into the functional relationship between proteins, which may not solely arise from shared evolutionary origin. A few methods that exist for such comparative studies have focused on structural models determined at atomic resolution, and may miss out interesting patterns present in large macromolecular complexes that are typically solved by low-resolution techniques. Results: We developed a coarse-grained method, PCalign, to quantitatively evaluate physicochemical similarities between a given pair of protein-protein interfaces. This method uses an order-independent algorithm, geometric hashing, to superimpose the backbone atoms of a given pair of interfaces, and provides a normalized scoring function, PC-score, to account for the extent of overlap in terms of both geometric and chemical characteristics. We demonstrate that PCalign outperforms existing methods, and additionally facilitates comparative studies across models of different resolutions, which are not accommodated by existing methods. Furthermore, we illustrate potential application of our method to recognize interesting biological relationships masked by apparent lack of structural similarity. Conclusions: PCalign is a useful method in recognizing shared chemical and spatial patterns among proteinprotein interfaces. It outperforms existing methods for high-quality data, and additionally facilitates comparison across structural models with different levels of details with proven robustness against noise. © Cheng et al. | Source Title: | BMC Bioinformatics | URI: | https://scholarbank.nus.edu.sg/handle/10635/181459 | ISSN: | 14712105 | DOI: | 10.1186/s12859-015-0471-x | Rights: | Attribution 4.0 International |
Appears in Collections: | Elements Staff Publications |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
10_1186_s12859-015-0471-x.pdf | 3.95 MB | Adobe PDF | OPEN | None | View/Download |
This item is licensed under a Creative Commons License