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
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