Document Detail


PECB: prediction of enzyme catalytic residues based on Naive Bayes classification.
MedLine Citation:
PMID:  18640905     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In the post-genome era, huge numbers of protein structures accumulate, but little is known about their function. It is time consuming and labour intensive to investigate them, e.g., enzyme catalytic properties, through in vivo or in vitro work. So in silico predictions could be a promising strategy to greatly shrink the list of potential targets. This work incorporated both structural and physico-chemical information into a Naive Bayes classification system, and gained much better performance. The ten-fold cross validation results of this method could reach 88.6% of sensitivity and 93.7% of specificity. The improvement of prediction accuracy is detailed in this paper. The PECB is also applied to predict other important sites.
Authors:
Kunpeng Zhang; Yun Xu; Guoliang Chen
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  International journal of bioinformatics research and applications     Volume:  4     ISSN:  1744-5485     ISO Abbreviation:  -     Publication Date:  2008  
Date Detail:
Created Date:  2008-07-21     Completed Date:  2008-10-07     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101253758     Medline TA:  Int J Bioinform Res Appl     Country:  Switzerland    
Other Details:
Languages:  eng     Pagination:  295-305     Citation Subset:  IM    
Affiliation:
Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230027, China. kpzhang@mail.ustc.edu.cn
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MeSH Terms
Descriptor/Qualifier:
Amino Acid Sequence
Amino Acids / chemistry*
Bayes Theorem
Binding Sites
Catalysis
Computer Simulation
Enzyme Activation
Enzymes / chemistry*
Models, Chemical*
Molecular Sequence Data
Protein Binding
Sequence Analysis, Protein / methods*
Chemical
Reg. No./Substance:
0/Amino Acids; 0/Enzymes

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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