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Literature DB >> 12511065

Predicting protein subcellular localisation from amino acid sequence information.

Abstract

Predicting the subcellular localisation of proteins is an important part of the elucidation of their functions and interactions. Here, the amino acid sequence motifs that direct proteins to their proper subcellular compartment are surveyed, different methods for localisation prediction are discussed, and some benchmarks for the more commonly used predictors are presented.

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Year: 2002 PMID： 12511065 DOI： 10.1093/bib/3.4.361

Source DB: PubMed Journal: Brief Bioinform ISSN： 1467-5463 Impact factor: 11.622

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Cited

21 in total

1. Analysis of curated and predicted plastid subproteomes of Arabidopsis. Subcellular compartmentalization leads to distinctive proteome properties.

Authors: Qi Sun; Olof Emanuelsson; Klaas J van Wijk
Journal: Plant Physiol Date: 2004-06 Impact factor: 8.340

2. Genome-scale expression of proteins from Bacillus subtilis.

Authors: S Moy; L Dieckman; M Schiffer; N Maltsev; G-X Yu; F R Collart
Journal: J Struct Funct Genomics Date: 2004

3. Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis.

Authors: Rakesh Kaundal; Reena Saini; Patrick X Zhao
Journal: Plant Physiol Date: 2010-07-20 Impact factor: 8.340

4. NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins containing classical nuclear localization signals.

Authors: P S Hari; T S Sridhar; R Pravin Kumar
Journal: J Mol Model Date: 2017-08-09 Impact factor: 1.810

5. Combining experimental and predicted datasets for determination of the subcellular location of proteins in Arabidopsis.

Authors: Joshua L Heazlewood; Julian Tonti-Filippini; Robert E Verboom; A Harvey Millar
Journal: Plant Physiol Date: 2005-10 Impact factor: 8.340

6. Isolation and functional expression of human COQ2, a gene encoding a polyprenyl transferase involved in the synthesis of CoQ.

Authors: Margareta Forsgren; Anneli Attersand; Staffan Lake; Jacob Grünler; Ewa Swiezewska; Gustav Dallner; Isabel Climent
Journal: Biochem J Date: 2004-09-01 Impact factor: 3.857

Predicting protein subcellular localisation from amino acid sequence information.

1. Analysis of curated and predicted plastid subproteomes of Arabidopsis. Subcellular compartmentalization leads to distinctive proteome properties.

2. Genome-scale expression of proteins from Bacillus subtilis.

3. Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis.

4. NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins containing classical nuclear localization signals.

5. Combining experimental and predicted datasets for determination of the subcellular location of proteins in Arabidopsis.

6. Isolation and functional expression of human COQ2, a gene encoding a polyprenyl transferase involved in the synthesis of CoQ.

7. PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria.

8. Comparative analysis of an experimental subcellular protein localization assay and in silico prediction methods.

9. Protein-protein interaction as a predictor of subcellular location.

10. Protein localization prediction using random walks on graphs.