Literature DB >> 15728119

AutoMotif server: prediction of single residue post-translational modifications in proteins.

Dariusz Plewczynski1, Adrian Tkacz, Lucjan Stanislaw Wyrwicz, Leszek Rychlewski.   

Abstract

UNLABELLED: The AutoMotif Server allows for identification of post-translational modification (PTM) sites in proteins based only on local sequence information. The local sequence preferences of short segments around PTM residues are described here as linear functional motifs (LFMs). Sequence models for all types of PTMs are trained by support vector machine on short-sequence fragments of proteins in the current release of Swiss-Prot database (phosphorylation by various protein kinases, sulfation, acetylation, methylation, amidation, etc.). The accuracy of the identification is estimated using the standard leave-one-out procedure. The sensitivities for all types of short LFMs are in the range of 70%. AVAILABILITY: The AutoMotif Server is available free for academic use at http://automotif.bioinfo.pl/

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Year:  2005        PMID: 15728119     DOI: 10.1093/bioinformatics/bti333

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  24 in total

1.  AutoMotif Server for prediction of phosphorylation sites in proteins using support vector machine: 2007 update.

Authors:  Dariusz Plewczynski; Adrian Tkacz; Lucjan S Wyrwicz; Leszek Rychlewski; Krzysztof Ginalski
Journal:  J Mol Model       Date:  2007-11-08       Impact factor: 1.810

2.  Prediction of protein amidation sites by feature selection and analysis.

Authors:  Weiren Cui; Shen Niu; Lulu Zheng; Lele Hu; Tao Huang; Lei Gu; Kaiyan Feng; Ning Zhang; Yudong Cai; Yixue Li
Journal:  Mol Genet Genomics       Date:  2013-06-21       Impact factor: 3.291

Review 3.  Application of Proteomics Technologies in Oil Palm Research.

Authors:  Benjamin Yii Chung Lau; Abrizah Othman; Umi Salamah Ramli
Journal:  Protein J       Date:  2018-12       Impact factor: 2.371

4.  Quantum chemical study of the mechanism of ethylene elimination in silylative coupling of olefins.

Authors:  Marcin Hoffmann; Bogdan Marciniec
Journal:  J Mol Model       Date:  2007-01-10       Impact factor: 1.810

5.  AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

Authors:  Dariusz Plewczynski; Subhadip Basu; Indrajit Saha
Journal:  Amino Acids       Date:  2012-05-04       Impact factor: 3.520

6.  AMS 3.0: prediction of post-translational modifications.

Authors:  Subhadip Basu; Dariusz Plewczynski
Journal:  BMC Bioinformatics       Date:  2010-04-28       Impact factor: 3.169

7.  Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information.

Authors:  Ashis Kumer Biswas; Nasimul Noman; Abdur Rahman Sikder
Journal:  BMC Bioinformatics       Date:  2010-05-21       Impact factor: 3.169

8.  Computational identification of post-translational modification sites and functional families reveal possible moonlighting role of rotaviral proteins.

Authors:  Shiladitya Chattopadhyay; Parikshit Bagchi; Dipanjan Dutta; Anupam Mukherjee; Nobumichi Kobayashi; Mamta Chawlasarkar
Journal:  Bioinformation       Date:  2010-04-30

9.  Functional site profiling and electrostatic analysis of cysteines modifiable to cysteine sulfenic acid.

Authors:  Freddie R Salsbury; Stacy T Knutson; Leslie B Poole; Jacquelyn S Fetrow
Journal:  Protein Sci       Date:  2008-02       Impact factor: 6.725

10.  LipocalinPred: a SVM-based method for prediction of lipocalins.

Authors:  Jayashree Ramana; Dinesh Gupta
Journal:  BMC Bioinformatics       Date:  2009-12-24       Impact factor: 3.169

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