Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12859-015-0471-x
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dc.titlePCalign: A method to quantify physicochemical similarity of protein-protein interfaces
dc.contributor.authorCheng, S
dc.contributor.authorZhang, Y
dc.contributor.authorBrooks, C.L
dc.date.accessioned2020-10-27T10:59:30Z
dc.date.available2020-10-27T10:59:30Z
dc.date.issued2015
dc.identifier.citationCheng, 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.issn14712105
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/181459
dc.description.abstractBackground: 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.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectBioinformatics
dc.subjectBiology
dc.subjectBiological relationships
dc.subjectChemical characteristic
dc.subjectConvergent evolution
dc.subjectMacromolecular complexes
dc.subjectProtein-protein interactions
dc.subjectProtein-protein interface
dc.subjectRobustness against noise
dc.subjectStructural bioinformatics
dc.subjectProteins
dc.subjectprotein
dc.subjectprotein binding
dc.subjectalgorithm
dc.subjectchemical structure
dc.subjectchemistry
dc.subjectcomputer program
dc.subjecthuman
dc.subjectmetabolism
dc.subjectphysical chemistry
dc.subjectAlgorithms
dc.subjectHumans
dc.subjectModels, Molecular
dc.subjectPhysicochemical Phenomena
dc.subjectProtein Binding
dc.subjectProteins
dc.subjectSoftware
dc.typeArticle
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.description.doi10.1186/s12859-015-0471-x
dc.description.sourcetitleBMC Bioinformatics
dc.description.volume16
dc.description.issue1
dc.description.page33
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