Literature DB >> 23777214

Predicting multisite protein subcellular locations: progress and challenges.

Pufeng Du1, Chao Xu.   

Abstract

In the last two decades, predicting protein subcellular locations has become a hot topic in bioinformatics. A number of algorithms and online services have been developed to computationally assign a subcellular location to a given protein sequence. With the progress of many proteome projects, more and more proteins are annotated with more than one subcellular location. However, multisite prediction has only been considered in a handful of recent studies, in which there are several common challenges. In this special report, the authors discuss what these challenges are, why these challenges are important and how the existing studies gave their solutions. Finally, a vision of the future of predicting multisite protein subcellular locations is given.

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Year:  2013        PMID: 23777214     DOI: 10.1586/epr.13.16

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


  7 in total

1.  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

Review 2.  Predicting Protein Submitochondrial Locations: The 10th Anniversary.

Authors:  Pu-Feng Du
Journal:  Curr Genomics       Date:  2017-08       Impact factor: 2.236

3.  Predicting Endoplasmic Reticulum Resident Proteins Using Auto-Cross Covariance Transformation With a U-Shaped Residue Weight-Transfer Function.

Authors:  Yang-Yang Miao; Wei Zhao; Guang-Ping Li; Yang Gao; Pu-Feng Du
Journal:  Front Genet       Date:  2019-12-20       Impact factor: 4.599

Review 4.  Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences.

Authors:  Kenichiro Imai; Kenta Nakai
Journal:  Front Genet       Date:  2020-11-25       Impact factor: 4.599

5.  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

6.  A novel approach for protein subcellular location prediction using amino acid exposure.

Authors:  Arvind Singh Mer; Miguel A Andrade-Navarro
Journal:  BMC Bioinformatics       Date:  2013-11-28       Impact factor: 3.169

7.  DPPN-SVM: Computational Identification of Mis-Localized Proteins in Cancers by Integrating Differential Gene Expressions With Dynamic Protein-Protein Interaction Networks.

Authors:  Guang-Ping Li; Pu-Feng Du; Zi-Ang Shen; Hang-Yu Liu; Tao Luo
Journal:  Front Genet       Date:  2020-10-23       Impact factor: 4.599

  7 in total

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