Skip Navigation


Bioinformatics Advance Access originally published online on April 9, 2009
Bioinformatics 2009 25(11):1433-1434; doi:10.1093/bioinformatics/btp251
This Article
Right arrow Full Text
Right arrow Full Text (Print PDF)
Right arrow Supplementary Data
Right arrow All Versions of this Article:
25/11/1433    most recent
btp251v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
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 Xia, X.
Right arrow Articles by Sun, Z.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Xia, X.
Right arrow Articles by Sun, Z.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

MICAlign: a sequence-to-structure alignment tool integrating multiple sources of information in conditional random fields

Xuefeng Xia {dagger}, Song Zhang {dagger}, Yu Su and Zhirong Sun *

Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing 100084, China

*To whom correspondence should be addressed.


   Abstract

Summary: Sequence-to-structure alignment in template-based protein structure modeling for remote homologs remains a difficult problem even following the correct recognition of folds. Here we present MICAlign, a sequence-to-structure alignment tool that incorporates multiple sources of information from local structural contexts of template, sequence profiles, predicted secondary structures, solvent accessibilities, potential-like terms (including residue–residue contacts and solvent exposures) and pre-aligned structures and sequences. These features, together with a position-specific gap scheme, were integrated into conditional random fields through which the optimal parameters were automatically learned. MICAlign showed improved alignment accuracy over several other state-of-the-art alignment tools based on comparisons by using independent datasets.

Availability: Freely available at http://www.bioinfo.tsinghua.edu.cn/~xiaxf/micalign for both web server and source code.

Contact: sunzhr{at}mail.tsinghua.edu.cn

Supplementary information: Supplementary data are available at Bioinformatics online.

{dagger}The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

Associate Editor: Anna Tramontano


Received on January 15, 2009; revised on April 1, 2009; accepted on April 7, 2009

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




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.