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

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          Abstract

          Sequence-derived structural and physicochemical features have been extensively used for analyzing and predicting structural, functional, expression and interaction profiles of proteins and peptides. PROFEAT has been developed as a web server for computing commonly used features of proteins and peptides from amino acid sequence. To facilitate more extensive studies of protein and peptides, numerous improvements and updates have been made to PROFEAT. We added new functions for computing descriptors of protein–protein and protein–small molecule interactions, segment descriptors for local properties of protein sequences, topological descriptors for peptide sequences and small molecule structures. We also added new feature groups for proteins and peptides (pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, total amino acid properties and atomic-level topological descriptors) as well as for small molecules (atomic-level topological descriptors). Overall, PROFEAT computes 11 feature groups of descriptors for proteins and peptides, and a feature group of more than 400 descriptors for small molecules plus the derived features for protein–protein and protein–small molecule interactions. Our computational algorithms have been extensively tested and used in a number of published works for predicting proteins of specific structural or functional classes, protein–protein interactions, peptides of specific functions and quantitative structure activity relationships of small molecules. PROFEAT is accessible free of charge at http://bidd.cz3.nus.edu.sg/cgi-bin/prof/protein/profnew.cgi.

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          Most cited references36

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          Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

          Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we characterize four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug–target interaction network topology. We then develop new statistical methods to predict unknown drug–target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug–target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call ‘pharmacological space’. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug–target interaction networks. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery. Availability: Softwares are available upon request. Contact: Yoshihiro.Yamanishi@ensmp.fr Supplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.
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            SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence.

            Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.
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              PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition.

              The pseudo amino acid (PseAA) composition can represent a protein sequence in a discrete model without completely losing its sequence-order information, and hence has been widely applied for improving the prediction quality for various protein attributes. However, dealing with different problems may need different kinds of PseAA composition. Here, we present a web-server called PseAAC at http://chou.med.harvard.edu/bioinf/PseAA/, by which users can generate various kinds of PseAA composition to best fit their need.
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                Author and article information

                Journal
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                1 July 2011
                1 July 2011
                23 May 2011
                23 May 2011
                : 39
                : Web Server issue , Web Server issue
                : W385-W390
                Affiliations
                1College of Chemistry, Sichuan University, Chengdu, 610064, P. R. China, 2Department of Pharmacy, Bioinformatics and Drug Design Group, National University of Singapore, Singapore 117543, 3College of Chemical Engineering, Sichuan University, Chengdu 610064 and 4State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, P. R. China
                Author notes
                *To whom correspondence should be addressed. Tel: 86-28-85406139; Fax: 86-28-85407797; Email: lizerong@ 123456scu.edu.cn

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

                Article
                gkr284
                10.1093/nar/gkr284
                3125735
                21609959
                03f47efe-0a03-43a2-8a38-98a90d667e6a
                © The Author(s) 2011. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 January 2011
                : 17 March 2011
                : 12 April 2011
                Page count
                Pages: 6
                Categories
                Articles

                Genetics
                Genetics

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