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A geometric invariant-based framework for the analysis of protein conformational space

DSpace at IIT Bombay

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Title A geometric invariant-based framework for the analysis of protein conformational space
 
Creator TENDULKAR, AV
SOHONI, MA
OGUNNAIKE, B
WANGIKAR, PP
 
Subject theoretic approach
globular-proteins
classification
families
homology
motifs
 
Description Motivation: Characterization of the restricted nature of the protein local conformational space has remained a challenge, thereby necessitating a computationally expensive conformational search in protein modeling. Moreover, owing to the lack of unilateral structural descriptors, conventional data mining techniques, such as clustering and classification, have not been applied in protein structure analysis. Results: We first map the local conformations in a fixed dimensional space by using a carefully selected suite of geometric invariants (GIs) and then reduce the number of dimensions via principal component analysis (PCA). Distribution of the conformations in the space spanned by the first four PCs is visualized as a set of conditional bivariate probability distribution plots, where the peaks correspond to the preferred conformations. The locations of the different canonical structures in the PC-space have been interpreted in the context of the weights of the GIs to the first four PCs. Clustering of the available conformations reveals that the number of preferred local conformations is several orders of magnitude smaller than that suggested previously.
 
Publisher OXFORD UNIV PRESS
 
Date 2011-08-19T11:34:29Z
2011-12-26T12:56:13Z
2011-12-27T05:44:34Z
2011-08-19T11:34:29Z
2011-12-26T12:56:13Z
2011-12-27T05:44:34Z
2005
 
Type Article
 
Identifier BIOINFORMATICS, 21(18), 3622-3628
1367-4803
http://dx.doi.org/10.1093/bioinformatics/bti621
http://dspace.library.iitb.ac.in/xmlui/handle/10054/10337
http://hdl.handle.net/10054/10337
 
Language en