Linked Life Data

19000929

Source:http://linkedlifedata.com/resource/pubmed/id/19000929

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2008-12-16
pubmed:abstractText
We propose a dual-scale approach to predict the native structures of retinal proteins (RPs) by combining coarse-grained (CG) Monte-Carlo simulations and all-atom (AA) molecular dynamics simulations to pack their transmembrane helices correctly. This approach has been applied to obtain the structures of five RPs, including bacteriorhodopsin (BR), halorhodopsin (HR), sensory rhodopsin I (SRI), sensory rhodopsin II (SRII), and (bovine) rhodopsin. The proposed CG model predicts a reasonably good structure of RPs in days using a desktop computer, which also gives clear physical picture for the packing, tilting, and orientation of transmembrane helices. A high-resolution protein structure is obtained from the AA molecular dynamics simulations by refining the predicted CG structure. The root mean square deviation in coordinates of backbone atoms from the X-ray structure is 1.89A for HR, 1.92A for SRII, 2.64A for BR, and 5.54A for rhodopsin. Reasonable predictions of HR structure can be obtained by this approach in the case of using predicted secondary structures with certain alignment error. Since the crystal structure of SRI is not available in the protein data bank, the predicted structure of SRI from our dual-scale approach is compared to that obtained from homology modeling.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1095-8657
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
165
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
37-46
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
A dual-scale approach toward structure prediction of retinal proteins.
pubmed:affiliation
Department of Physics, National Taiwan Normal University, 88 sec. 4 Ting-Chou Rd., Taipei 116, Taiwan.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't