In silico pattern-based analysis of the human cytomegalovirus genome.
Publication/Presentation Date
4-1-2003
Abstract
More than 200 open reading frames (ORFs) from the human cytomegalovirus genome have been reported as potentially coding for proteins. We have used two pattern-based in silico approaches to analyze this set of putative viral genes. With the help of an objective annotation method that is based on the Bio-Dictionary, a comprehensive collection of amino acid patterns that describes the currently known natural sequence space of proteins, we have reannotated all of the previously reported putative genes of the human cytomegalovirus. Also, with the help of MUSCA, a pattern-based multiple sequence alignment algorithm, we have reexamined the original human cytomegalovirus gene family definitions. Our analysis of the genome shows that many of the coded proteins comprise amino acid combinations that are unique to either the human cytomegalovirus or the larger group of herpesviruses. We have confirmed that a surprisingly large portion of the analyzed ORFs encode membrane proteins, and we have discovered a significant number of previously uncharacterized proteins that are predicted to be G-protein-coupled receptor homologues. The analysis also indicates that many of the encoded proteins undergo posttranslational modifications such as hydroxylation, phosphorylation, and glycosylation. ORFs encoding proteins with similar functional behavior appear in neighboring regions of the human cytomegalovirus genome. All of the results of the present study can be found and interactively explored online (http://cbcsrv.watson.ibm.com/virus/).
Volume
77
Issue
7
First Page
4326
Last Page
4344
ISSN
0022-538X
Published In/Presented At
Rigoutsos, I., Novotny, J., Huynh, T., Chin-Bow, S. T., Parida, L., Platt, D., Coleman, D., & Shenk, T. (2003). In silico pattern-based analysis of the human cytomegalovirus genome. Journal of virology, 77(7), 4326–4344. https://doi.org/10.1128/jvi.77.7.4326-4344.2003
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
12634390
Department(s)
Administration and Leadership
Document Type
Article