Literature DB >> 21679119

Recent progress in predicting protein sub-subcellular locations.

Pufeng Du1, Tingting Li, Xin Wang.   

Abstract

In the last two decades, the number of the known protein sequences increased very rapidly. However, a knowledge of protein function only exists for a small portion of these sequences. Since the experimental approaches for determining protein functions are costly and time consuming, in silico methods have been introduced to bridge the gap between knowledge of protein sequences and their functions. Knowing the subcellular location of a protein is considered to be a critical step in understanding its biological functions. Many efforts have been undertaken to predict the protein subcellular locations in silico. With the accumulation of available data, the substructures of some subcellular organelles, such as the cell nucleus, mitochondria and chloroplasts, have been taken into consideration by several studies in recent years. These studies create a new research topic, namely 'protein sub-subcellular location prediction', which goes one level deeper than classic protein subcellular location prediction.

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Year:  2011        PMID: 21679119     DOI: 10.1586/epr.11.20

Source DB:  PubMed          Journal:  Expert Rev Proteomics        ISSN: 1478-9450            Impact factor:   3.940


  14 in total

1.  Chromerid genomes reveal the evolutionary path from photosynthetic algae to obligate intracellular parasites.

Authors:  Yong H Woo; Hifzur Ansari; Thomas D Otto; Christen M Klinger; Martin Kolisko; Jan Michálek; Alka Saxena; Dhanasekaran Shanmugam; Annageldi Tayyrov; Alaguraj Veluchamy; Shahjahan Ali; Axel Bernal; Javier del Campo; Jaromír Cihlář; Pavel Flegontov; Sebastian G Gornik; Eva Hajdušková; Aleš Horák; Jan Janouškovec; Nicholas J Katris; Fred D Mast; Diego Miranda-Saavedra; Tobias Mourier; Raeece Naeem; Mridul Nair; Aswini K Panigrahi; Neil D Rawlings; Eriko Padron-Regalado; Abhinay Ramaprasad; Nadira Samad; Aleš Tomčala; Jon Wilkes; Daniel E Neafsey; Christian Doerig; Chris Bowler; Patrick J Keeling; David S Roos; Joel B Dacks; Thomas J Templeton; Ross F Waller; Julius Lukeš; Miroslav Oborník; Arnab Pain
Journal:  Elife       Date:  2015-07-15       Impact factor: 8.140

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.  SubMito-PSPCP: predicting protein submitochondrial locations by hybridizing positional specific physicochemical properties with pseudoamino acid compositions.

Authors:  Pufeng Du; Yuan Yu
Journal:  Biomed Res Int       Date:  2013-08-21       Impact factor: 3.411

4.  Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions.

Authors:  Lei Chen; Tao Huang; Jian Zhang; Ming-Yue Zheng; Kai-Yan Feng; Yu-Dong Cai; Kuo-Chen Chou
Journal:  Biomed Res Int       Date:  2013-09-04       Impact factor: 3.411

5.  Prediction of gene phenotypes based on GO and KEGG pathway enrichment scores.

Authors:  Tao Zhang; Min Jiang; Lei Chen; Bing Niu; Yudong Cai
Journal:  Biomed Res Int       Date:  2013-11-07       Impact factor: 3.411

6.  Prediction of drug indications based on chemical interactions and chemical similarities.

Authors:  Guohua Huang; Yin Lu; Changhong Lu; Mingyue Zheng; Yu-Dong Cai
Journal:  Biomed Res Int       Date:  2015-03-02       Impact factor: 3.411

7.  Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.

Authors:  Lisa M Breckels; Sean B Holden; David Wojnar; Claire M Mulvey; Andy Christoforou; Arnoud Groen; Matthew W B Trotter; Oliver Kohlbacher; Kathryn S Lilley; Laurent Gatto
Journal:  PLoS Comput Biol       Date:  2016-05-13       Impact factor: 4.475

8.  Predicting chemical toxicity effects based on chemical-chemical interactions.

Authors:  Lei Chen; Jing Lu; Jian Zhang; Kai-Rui Feng; Ming-Yue Zheng; Yu-Dong Cai
Journal:  PLoS One       Date:  2013-02-15       Impact factor: 3.240

9.  Predicting human protein subcellular locations by the ensemble of multiple predictors via protein-protein interaction network with edge clustering coefficients.

Authors:  Pufeng Du; Lusheng Wang
Journal:  PLoS One       Date:  2014-01-23       Impact factor: 3.240

10.  Prediction of cancer drugs by chemical-chemical interactions.

Authors:  Jing Lu; Guohua Huang; Hai-Peng Li; Kai-Yan Feng; Lei Chen; Ming-Yue Zheng; Yu-Dong Cai
Journal:  PLoS One       Date:  2014-02-03       Impact factor: 3.240

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