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Nucleic Acids Research 2006 34(Web Server issue):W177-W181; doi:10.1093/nar/gkl266
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© The Author 2006. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Article

DISULFIND: a disulfide bonding state and cysteine connectivity prediction server

Alessio Ceroni1, Andrea Passerini1, Alessandro Vullo2 and Paolo Frasconi1,*

1 Machine Learning and Neural Networks Group, Università degli Studi di Firenze, Dipartimento di Sistemi e Informatica Via di Santa Marta 3, 50139 Firenze, Italy 2 School of Computer Science and Informatics, University College Dublin Belfield, Dublin 4, Ireland

*To whom correspondence should be addressed. Tel: +39 0554796362; Fax: +39 0554796363; Email: p-f{at}dsi.unifi.it

Received March 14, 2006. Revised March 29, 2006. Accepted March 31, 2006.

DISULFIND is a server for predicting the disulfide bonding state of cysteines and their disulfide connectivity starting from sequence alone. Optionally, disulfide connectivity can be predicted from sequence and a bonding state assignment given as input. The output is a simple visualization of the assigned bonding state (with confidence degrees) and the most likely connectivity patterns. The server is available at http://disulfind.dsi.unifi.it/.

The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint first authors.


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