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Nucleic Acids Research 2006 34(Web Server issue):W32-W37; doi:10.1093/nar/gkl305
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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

PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

Z. R. Li1,2, H. H. Lin1, L. Y. Han1, L. Jiang1, X. Chen3 and Y. Z. Chen1,4,*

1 Bioinformatics and Drug Design Group, Department of Computational Science, National University of Singapore Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543 2 College of Chemistry, Sichuan University Chengdu, 610064, P. R. China 3 Department of Biotechnology, Zhejiang University Hangzhou, 310029, P. R. China 4 Shanghai Center for Bioinformation Technology Shanghai, 201203, P. R. China

*To whom correspondence should be addressed. Tel: +65 6516 6877; Fax: +65 6774 6756; Email: yzchen{at}cz3.nus.edu.sg

Received December 23, 2005. Revised January 17, 2006. Accepted April 10, 2006.

Sequence-derived structural and physicochemical features have frequently been used in the development of statistical learning models for predicting proteins and peptides of different structural, functional and interaction profiles. PROFEAT (Protein Features) is a web server for computing commonly-used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes six feature groups composed of ten features that include 51 descriptors and 1447 descriptor values. The computed features include amino acid composition, dipeptide composition, normalized Moreau–Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors and the composition, transition and distribution of various structural and physicochemical properties. In addition, it can also compute previous autocorrelations descriptors based on user-defined properties. Our computational algorithms were extensively tested and the computed protein features have been used in a number of published works for predicting proteins of functional classes, protein–protein interactions and MHC-binding peptides. PROFEAT is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/prof/prof.cgi


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