Home LiteratureArticle Details
PMID: 15037084 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

A family of evolution-entropy hybrid methods for ranking protein residues by importance.

Journal of molecular biology ·Vol. 336 ·No. 5 ·2004-03-05 ·Pages 1265-82

Mihalek I, Res I, Lichtarge O

Abstract

In order to identify the amino acids that determine protein structure and function it is useful to rank them by their relative importance. Previous approaches belong to two groups; those that rely on statistical inference, and those that focus on phylogenetic analysis. Here, we introduce a class of hybrid methods that combine evolutionary and entropic information from multiple sequence alignments. A detailed analysis in insulin receptor kinase domain and tests on proteins that are well-characterized experimentally show the hybrids' greater robustness with respect to the input choice of sequences, as well as improved sensitivity and specificity of prediction. This is a further step toward proteome scale analysis of protein structure and function.

MeSH Terms
Amino Acid Sequence Amino Acids Animals Entropy Evolution, Molecular Humans Models, Genetic Models, Molecular Proteins/chemistry,genetics Receptor, Insulin/chemistry,genetics Sequence Alignment
Chemicals
Amino Acids Proteins Receptor, Insulin
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Mihalek I
Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza T921, Houston, TX 77030, USA.
Res I
Lichtarge O
Article Info
Journal
Journal of molecular biology
Abbr.
J Mol Biol
ISSN
0022-2836
Published
2004-03-05
Pages
1265-82
Language
English
Region
England
NLM ID
2985088R
Subset
IM
Grants
NIGMS NIH HHS · GM066099 · United States
Analysis Services
Analysis Services

Contact

No. 2 Wenbo Road, Zhangqiu District, Jinan, Shandong

Qilu Normal University · Genelibs Bioinformatics Lab

750 Shunhua Rd, Jinan

2F, Bldg F, University Science Park

Tel: 0531-88819269

WeChat Official Account

Follow our WeChat subscription account for real-time updates and the latest in medical and biological research.


Business Email

E-mail: product@genelibs.com