Skip Navigation



Bioinformatics Advance Access published online on August 11, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti621
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/18/3622    most recent
bti621v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Tendulkar, A. V.
Right arrow Articles by Wangikar, P. P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Tendulkar, A. V.
Right arrow Articles by Wangikar, P. P.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received June 16, 2005
Revised August 5, 2005
Accepted August 8, 2005

Article

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

Ashish V. Tendulkar 1, Milind A. Sohoni 2, Babatunde Ogunnaike 3, and Pramod P. Wangikar 4*

1 Kanwal Rekhi School of Information Technology, Indian Institute of Technology, Bombay, Powai, Mumbai 400076, India
2 Department of Computer Science and Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400076, India
3 Department of Chemical Engineering, University of Delaware, Newark DE19716
4 Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400076, India

* To whom correspondence should be addressed.
Pramod P. Wangikar, E-mail: pramodw{at}iitb.ac.in


  Abstract

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, due 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 (GI's) and then reduce the number of dimensions via principal component analysis (PCA). Distribution of the conformations in the space spanned by the first four PC's is visualized as a set of conditional bi-variate 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 GI's to the first four PC's. Clustering of the available conformations reveals that the number of preferred local conformations is several orders of magnitude smaller than that suggested previously.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Proc. Natl. Acad. Sci. USAHome page
G. E. Sims and S.-H. Kim
A method for evaluating the structural quality of protein models by using higher-order {varphi}-{psi} pairs scoring
PNAS, March 21, 2006; 103(12): 4428 - 4432.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.