File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Structure-oriented bioinformatic approach exploring histidine-rich clusters in proteins

TitleStructure-oriented bioinformatic approach exploring histidine-rich clusters in proteins
Authors
Issue Date2013
PublisherRoyal Society of Chemistry. The Journal's web site is located at http://www.rsc.org/Publishing/Journals/MT/About.asp
Citation
Metallomics, 2013, v. 5 n. 7, p. 904-912 How to Cite?
AbstractSpatially clustered histidines are commonly found in protein structures. The versatility of histidine coordination favors transition metal bindings, suggesting that spatially clustered histidines are potentially involved in metal binding and thereby play an important role in protein functions. We have applied a bioinformatic approach to identify and characterize histidine-rich clusters (HrCs) protein candidates with a focus on metal coordination. The computational analysis revealed over a thousand non-homologous HrC proteins with a large portion exhibiting interaction with transition metals, particularly zinc, copper and nickel. The results reflect that multiple histidines are apparently clustered together for the corroboration of both static and dynamical metal binding. The identified HrC proteins are correlated with microbial pathogenesis, offering useful information for drug design. This approach can be readily extended to other types of biological studies, where the relationship between single amino acid-rich clusters and their structural-functional relationships can be scrutinized. © 2013 The Royal Society of Chemistry.
Persistent Identifierhttp://hdl.handle.net/10722/185738
ISSN
2021 Impact Factor: 4.636
2020 SCImago Journal Rankings: 1.012
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCun, S-
dc.contributor.authorLAI, TP-
dc.contributor.authorCHANG, YY-
dc.contributor.authorSun, H-
dc.date.accessioned2013-08-20T11:37:47Z-
dc.date.available2013-08-20T11:37:47Z-
dc.date.issued2013-
dc.identifier.citationMetallomics, 2013, v. 5 n. 7, p. 904-912-
dc.identifier.issn1756-5901-
dc.identifier.urihttp://hdl.handle.net/10722/185738-
dc.description.abstractSpatially clustered histidines are commonly found in protein structures. The versatility of histidine coordination favors transition metal bindings, suggesting that spatially clustered histidines are potentially involved in metal binding and thereby play an important role in protein functions. We have applied a bioinformatic approach to identify and characterize histidine-rich clusters (HrCs) protein candidates with a focus on metal coordination. The computational analysis revealed over a thousand non-homologous HrC proteins with a large portion exhibiting interaction with transition metals, particularly zinc, copper and nickel. The results reflect that multiple histidines are apparently clustered together for the corroboration of both static and dynamical metal binding. The identified HrC proteins are correlated with microbial pathogenesis, offering useful information for drug design. This approach can be readily extended to other types of biological studies, where the relationship between single amino acid-rich clusters and their structural-functional relationships can be scrutinized. © 2013 The Royal Society of Chemistry.-
dc.languageeng-
dc.publisherRoyal Society of Chemistry. The Journal's web site is located at http://www.rsc.org/Publishing/Journals/MT/About.asp-
dc.relation.ispartofMetallomics-
dc.titleStructure-oriented bioinformatic approach exploring histidine-rich clusters in proteins-
dc.typeArticle-
dc.identifier.emailCun, S: shujian@hku.hk-
dc.identifier.emailSun, H: hsun@hku.hk-
dc.identifier.authoritySun, H=rp00777-
dc.identifier.doi10.1039/C3MT00026E-
dc.identifier.pmid23771053-
dc.identifier.scopuseid_2-s2.0-84879864955-
dc.identifier.hkuros220634-
dc.identifier.volume5-
dc.identifier.issue7-
dc.identifier.spage904-
dc.identifier.epage912-
dc.identifier.isiWOS:000320951600017-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl1756-5901-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats