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      KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites

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          Abstract

          KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at http://KinasePhos.mbc.nctu.edu.tw/.

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          Profile hidden Markov models.

          S. Eddy (1998)
          The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
          Article 0 comments Cited 1254 times – based on 0 reviews     Review now
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            Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry.

            Majbrit Hjerrild, Allan Stensballe, Thomas Rasmussen … (2015)
            Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA in vitro and 13 PKA phosphorylation sites were identified by mass spectrometry. NetPhosK was 100% sensitive and 41% specific in predicting PKA sites in the four proteins. These results demonstrate the potential of using integrated computational and experimental methods for detailed investigations of the phosphoproteome.
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              Reduced bio basis function neural network for identification of protein phosphorylation sites: comparison with pattern recognition algorithms.

              Andrew R. Dalby, E. Berry, Zheng Yang (2004)
              Protein phosphorylation is a post-translational modification performed by a group of enzymes known as the protein kinases or phosphotransferases (Enzyme Commission classification 2.7). It is essential to the correct functioning of both proteins and cells, being involved with enzyme control, cell signalling and apoptosis. The major problem when attempting prediction of these sites is the broad substrate specificity of the enzymes. This study employs back-propagation neural networks (BPNNs), the decision tree algorithm C4.5 and the reduced bio-basis function neural network (rBBFNN) to predict phosphorylation sites. The aim is to compare prediction efficiency of the three algorithms for this problem, and examine knowledge extraction capability. All three algorithms are effective for phosphorylation site prediction. Results indicate that rBBFNN is the fastest and most sensitive of the algorithms. BPNN has the highest area under the ROC curve and is therefore the most robust, and C4.5 has the highest prediction accuracy. C4.5 also reveals the amino acid 2 residues upstream from the phosporylation site is important for serine/threonine phosphorylation, whilst the amino acid 3 residues upstream is important for tyrosine phosphorylation.
              Article 0 comments Cited 12 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (publisher-id): Nucleic Acids Research
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 01 July 2005
                Publication date (Print): 01 July 2005
                Publication date (Electronic): 27 June 2005
                Volume: 33
                Issue: Web Server issue
                Pages: W226-W229
                Affiliations
                Department of Biological Science and Technology, Institute of Bioinformatics, National Chiao Tung University Hsin-Chu 300, Taiwan
                1Department of Life Science, National Central University Chung-Li 320, Taiwan
                2Department of Computer Science and Information Engineering, National Central University Chung-Li 320, Taiwan
                Author notes
                *To whom correspondence should be addressed. Tel: +886 3 5712121, ext. 56952; Fax: +886 3 5729288; Email: bryan@ 123456mail.nctu.edu.tw

                Correspondence may also be addressed to Jorng-Tzong Horng. Tel: +886 3 4227151, ext. 35307; Fax: +886 3 4222681; Email: horng@ 123456db.csie.ncu.edu.tw

                Article
                DOI: 10.1093/nar/gki471
                PMC ID: 1160232
                PubMed ID: 15980458
                SO-VID: 21b98a82-e76a-4ce0-834e-de735aec347b
                Copyright © © The Author 2005. Published by Oxford University Press. All rights reserved
                License:

                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@ 123456oupjournals.org

                History
                Date received : 13 February 2005
                Date revision received : 15 April 2005
                Date accepted : 15 April 2005
                Categories
                Subject: Article

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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