Document Detail


Predicting enzymatic function from global binding site descriptors.
MedLine Citation:
PMID:  23150100     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Due to the rising number of solved protein structures, computer-based techniques for automatic protein functional annotation and classification into families are of high scientific interest. DoGSiteScorer automatically calculates global descriptors for self-predicted pockets based on the 3D structure of a protein. Protein function predictors on three levels with increasing granularity are built by use of a support vector machine (SVM), based on descriptors of 26632 pockets from enzymes with known structure and EC classification. The SVM models represent a generalization of the available descriptor space for each enzyme class, subclass, and substrate-specific sub-subclass. Cross-validation studies show accuracies of 68:2% for predicting the correct main class and accuracies between 62:8% and 80:9% for the six subclasses. Substrate-specific recall rates for a kinase subset are 53:8%. Furthermore, application studies show the ability of the method for predicting the function of unknown proteins and gaining valuable information for the function prediction field. Proteins 2012. © 2012 Wiley Periodicals, Inc.
Authors:
Andrea Volkamer; Daniel Kuhn; Friedrich Rippmann; Matthias Rarey
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-11-14
Journal Detail:
Title:  Proteins     Volume:  -     ISSN:  1097-0134     ISO Abbreviation:  Proteins     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8700181     Medline TA:  Proteins     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012 Wiley Periodicals, Inc.
Affiliation:
University of Hamburg, Center for Bioinformatics, Bundesstr. 43, 20146 Hamburg, Germany, and Darmstadt, Germany.
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