Please use this identifier to cite or link to this item: https://doi.org/10.1093/nar/gkl305
Title: PROFEAT: A web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence
Authors: Li, Z.R. 
Lin, H.H.
Han, L.Y. 
Jiang, L. 
Chen, X.
Chen, Y.Z. 
Issue Date: Jul-2006
Citation: Li, Z.R., Lin, H.H., Han, L.Y., Jiang, L., Chen, X., Chen, Y.Z. (2006-07). PROFEAT: A web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Research 34 (WEB. SERV. ISS.) : W32-W37. ScholarBank@NUS Repository. https://doi.org/10.1093/nar/gkl305
Abstract: Sequence-derived structural and physicochemical features have frequently been used in the development of statistical learning models for prelicting 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, quasisequence-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. © The Author 2006. Published by Oxford University Press. All rights reserved.
Source Title: Nucleic Acids Research
URI: http://scholarbank.nus.edu.sg/handle/10635/104847
ISSN: 03051048
DOI: 10.1093/nar/gkl305
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