Please use this identifier to cite or link to this item:
https://doi.org/10.1186/s12859-015-0471-x
DC Field | Value | |
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dc.title | PCalign: A method to quantify physicochemical similarity of protein-protein interfaces | |
dc.contributor.author | Cheng, S | |
dc.contributor.author | Zhang, Y | |
dc.contributor.author | Brooks, C.L | |
dc.date.accessioned | 2020-10-27T10:59:30Z | |
dc.date.available | 2020-10-27T10:59:30Z | |
dc.date.issued | 2015 | |
dc.identifier.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 | |
dc.identifier.issn | 14712105 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/181459 | |
dc.description.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. | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Unpaywall 20201031 | |
dc.subject | Bioinformatics | |
dc.subject | Biology | |
dc.subject | Biological relationships | |
dc.subject | Chemical characteristic | |
dc.subject | Convergent evolution | |
dc.subject | Macromolecular complexes | |
dc.subject | Protein-protein interactions | |
dc.subject | Protein-protein interface | |
dc.subject | Robustness against noise | |
dc.subject | Structural bioinformatics | |
dc.subject | Proteins | |
dc.subject | protein | |
dc.subject | protein binding | |
dc.subject | algorithm | |
dc.subject | chemical structure | |
dc.subject | chemistry | |
dc.subject | computer program | |
dc.subject | human | |
dc.subject | metabolism | |
dc.subject | physical chemistry | |
dc.subject | Algorithms | |
dc.subject | Humans | |
dc.subject | Models, Molecular | |
dc.subject | Physicochemical Phenomena | |
dc.subject | Protein Binding | |
dc.subject | Proteins | |
dc.subject | Software | |
dc.type | Article | |
dc.contributor.department | DUKE-NUS MEDICAL SCHOOL | |
dc.description.doi | 10.1186/s12859-015-0471-x | |
dc.description.sourcetitle | BMC Bioinformatics | |
dc.description.volume | 16 | |
dc.description.issue | 1 | |
dc.description.page | 33 | |
Appears in Collections: | Elements Staff Publications |
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