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dc.contributor.authorLu, Chih-Haoen_US
dc.contributor.authorHuang, Shao-Weien_US
dc.contributor.authorLai, Yan-Longen_US
dc.contributor.authorLin, Chih-Pengen_US
dc.contributor.authorShih, Chien-Huaen_US
dc.contributor.authorHuang, Cuen-Chaoen_US
dc.contributor.authorHsu, Wei-Lunen_US
dc.contributor.authorHwang, Jenn-Kangen_US
dc.date.accessioned2014-12-08T15:11:04Z-
dc.date.available2014-12-08T15:11:04Z-
dc.date.issued2008-08-01en_US
dc.identifier.issn0887-3585en_US
dc.identifier.urihttp://dx.doi.org/10.1002/prot.21954en_US
dc.identifier.urihttp://hdl.handle.net/11536/8488-
dc.description.abstractRecently, we have developed a method (Shih et al., Proteins: Structure, Function, and Bioinformatics 2007,68: 34-38) to compute correlation of fluctuations of proteins. This method, referred to as the protein fixed-point (PFP) model, is based on the positional vectors of atoms issuing from the fixed point, which is the point of the least fluctuations in proteins. One corollary from this model is that atoms lying on the same shell centered at the fixed point will have the same thermal fluctuations. In practice, this model provides a convenient way to compute the average dynamical properties of proteins directly from the geometrical shapes of proteins without the need of any mechanical models, and hence no trajectory integration or sophisticated matrix operations are needed. As a result, it is more efficient than molecular dynamics simulation or normal mode analysis. Though in the previous study the PFP model has been successfully applied to a number of proteins of various folds, it is not clear to what extent this model will be applied. In this article, we have carried out the comprehensive analysis of the PFP model for a dataset comprising 972 high-resolution X-ray structures with pairwise sequence identity <= 25%. We found that in most cases the PFP model works well. However, in case of proteins comprising multiple domains, each domain should be treated separately as an independent dynamical module with its own fixed point, and in case of the protein complex comprising it number of subunits, if functioning as a biological unit, the whole complex should be considered as one single dynamical module with one fixed point. Under such considerations, the resultant correlation coefficient between the computed and the X-ray structural B-factors for the data set is 0.59 and 75% (727/972) of proteins with a correlation coefficient >= 0.5. Our result shows that the fixed-point model is indeed quite general and will be a useful tool for high throughput analysis of dynamical properties of proteins.en_US
dc.language.isoen_USen_US
dc.subjectprotein dynamicsen_US
dc.subjectthermal fluctuationsen_US
dc.subjectmolecular dynamicsen_US
dc.subjectnormal mode analysisen_US
dc.subjectB-factorsen_US
dc.titleOn the relationship between the protein structure and protein dynamicsen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/prot.21954en_US
dc.identifier.journalPROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICSen_US
dc.citation.volume72en_US
dc.citation.issue2en_US
dc.citation.spage625en_US
dc.citation.epage634en_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000257156500008-
dc.citation.woscount13-
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