Literature DB >> 21080490

Prediction of subcellular locations of proteins: where to proceed?

Kenichiro Imai1, Kenta Nakai.   

Abstract

Since the proposal of the signal hypothesis on protein subcellular sorting, a number of computational analyses have been performed in this field. A typical example is the development of prediction algorithms for the subcellular localization sites of input protein sequences. In this review, we mainly focus on the biological grounds of the prediction methods rather than the algorithmic issues because we believe the former will be more fruitful for future development. Recent advances on the study of protein sorting signals will hopefully be incorporated into future prediction methods. Unfortunately, many of the state-of-the-art methods are published without sufficient objective tests. In fact, a simple test employed in this article shows that the performance of specifically developed predictors is not significantly better than that of a homology search. We suspect that this is a general problem associated with the interpretation of genome sequences, which have evolved through gene duplication and speciation.
Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Year:  2010        PMID: 21080490     DOI: 10.1002/pmic.201000274

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  35 in total

1.  The DegraBase: a database of proteolysis in healthy and apoptotic human cells.

Authors:  Emily D Crawford; Julia E Seaman; Nick Agard; Gerald W Hsu; Olivier Julien; Sami Mahrus; Huy Nguyen; Kazutaka Shimbo; Hikari A I Yoshihara; Min Zhuang; Robert J Chalkley; James A Wells
Journal:  Mol Cell Proteomics       Date:  2012-12-20       Impact factor: 5.911

Review 2.  Bioanalysis of eukaryotic organelles.

Authors:  Chad P Satori; Michelle M Henderson; Elyse A Krautkramer; Vratislav Kostal; Mark D Distefano; Mark M Distefano; Edgar A Arriaga
Journal:  Chem Rev       Date:  2013-04-10       Impact factor: 60.622

3.  LocTree3 prediction of localization.

Authors:  Tatyana Goldberg; Maximilian Hecht; Tobias Hamp; Timothy Karl; Guy Yachdav; Nadeem Ahmed; Uwe Altermann; Philipp Angerer; Sonja Ansorge; Kinga Balasz; Michael Bernhofer; Alexander Betz; Laura Cizmadija; Kieu Trinh Do; Julia Gerke; Robert Greil; Vadim Joerdens; Maximilian Hastreiter; Katharina Hembach; Max Herzog; Maria Kalemanov; Michael Kluge; Alice Meier; Hassan Nasir; Ulrich Neumaier; Verena Prade; Jonas Reeb; Aleksandr Sorokoumov; Ilira Troshani; Susann Vorberg; Sonja Waldraff; Jonas Zierer; Henrik Nielsen; Burkhard Rost
Journal:  Nucleic Acids Res       Date:  2014-05-21       Impact factor: 16.971

4.  GP4: an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting.

Authors:  Stefano Grasso; Tjeerd van Rij; Jan Maarten van Dijl
Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

5.  BUSCA: an integrative web server to predict subcellular localization of proteins.

Authors:  Castrense Savojardo; Pier Luigi Martelli; Piero Fariselli; Giuseppe Profiti; Rita Casadio
Journal:  Nucleic Acids Res       Date:  2018-07-02       Impact factor: 16.971

6.  ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes.

Authors:  Brian R King; Suleyman Vural; Sanjit Pandey; Alex Barteau; Chittibabu Guda
Journal:  BMC Res Notes       Date:  2012-07-10

7.  A survey of putative secreted and transmembrane proteins encoded in the C. elegans genome.

Authors:  Jinkyo Suh; Harald Hutter
Journal:  BMC Genomics       Date:  2012-07-23       Impact factor: 3.969

8.  Minimalist ensemble algorithms for genome-wide protein localization prediction.

Authors:  Jhih-Rong Lin; Ananda Mohan Mondal; Rong Liu; Jianjun Hu
Journal:  BMC Bioinformatics       Date:  2012-07-03       Impact factor: 3.169

9.  Plant cell wall proteomics: the leadership of Arabidopsis thaliana.

Authors:  Cécile Albenne; Hervé Canut; Elisabeth Jamet
Journal:  Front Plant Sci       Date:  2013-05-01       Impact factor: 5.753

10.  Protein localization prediction using random walks on graphs.

Authors:  Xiaohua Xu; Lin Lu; Ping He; Ling Chen
Journal:  BMC Bioinformatics       Date:  2013-05-09       Impact factor: 3.169

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