Literature DB >> 17993650

Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM.

Hong-Bin Shen1, Kuo-Chen Chou.   

Abstract

The life processes of an eukaryotic cell are guided by its nucleus. In addition to the genetic material, the cellular nucleus contains many proteins located at its different compartments, called subnuclear locations. Information of their localization in a nucleus is indispensable for the in-depth study of system biology because, in addition to helping determine their functions, it can provide illuminative insights of how and in what kind of microenvironments these subnuclear proteins are interacting with each other and with other molecules. Facing the deluge of protein sequences generated in the post-genomic age, we are challenged to develop an automated method for fast and effectively annotating the subnuclear locations of numerous newly found nuclear protein sequences. In view of this, a new classifier, called Nuc-PLoc, has been developed that can be used to identify nuclear proteins among the following nine subnuclear locations: (1) chromatin, (2) heterochromatin, (3) nuclear envelope, (4) nuclear matrix, (5) nuclear pore complex, (6) nuclear speckle, (7) nucleolus, (8) nucleoplasm and (9) nuclear promyelocytic leukaemia (PML) body. Nuc-PLoc is featured by an ensemble classifier formed by fusing the evolution information of a protein and its pseudo-amino acid composition. The overall jackknife cross-validation accuracy obtained by Nuc-PLoc is significantly higher than those by the existing methods on the same benchmark data set through the same testing procedure. As a user-friendly web-server, Nuc-PLoc is freely accessible to the public at http://chou.med.harvard.edu/bioinf/Nuc-PLoc.

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Year:  2007        PMID: 17993650     DOI: 10.1093/protein/gzm057

Source DB:  PubMed          Journal:  Protein Eng Des Sel        ISSN: 1741-0126            Impact factor:   1.650


  37 in total

1.  An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition.

Authors:  Jiang Wu; Meng-Long Li; Le-Zheng Yu; Chao Wang
Journal:  Protein J       Date:  2010-01       Impact factor: 2.371

Review 2.  Mapping of protein- and chromatin-interactions at the nuclear lamina.

Authors:  Nard Kubben; Jan Willem Voncken; Tom Misteli
Journal:  Nucleus       Date:  2010-09-03       Impact factor: 4.197

3.  PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.

Authors:  S M Hasan Mahmud; Wenyu Chen; Yongsheng Liu; Md Abdul Awal; Kawsar Ahmed; Md Habibur Rahman; Mohammad Ali Moni
Journal:  Brief Bioinform       Date:  2021-03-12       Impact factor: 11.622

Review 4.  Some illuminating remarks on molecular genetics and genomics as well as drug development.

Authors:  Kuo-Chen Chou
Journal:  Mol Genet Genomics       Date:  2020-01-01       Impact factor: 3.291

5.  Sub-cellular localization, expression and functions of Sirt6 during the cell cycle in HeLa cells.

Authors:  Pooneh Memar Ardestani; Fengyi Liang
Journal:  Nucleus       Date:  2012-06-29       Impact factor: 4.197

6.  Characterization of LuWRKY36, a flax transcription factor promoting secoisolariciresinol biosynthesis in response to Fusarium oxysporum elicitors in Linum usitatissimum L. hairy roots.

Authors:  Lucija Markulin; Cyrielle Corbin; Sullivan Renouard; Samantha Drouet; Charlène Durpoix; Charlotte Mathieu; Tatiana Lopez; Daniel Auguin; Christophe Hano; Éric Lainé
Journal:  Planta       Date:  2019-04-29       Impact factor: 4.116

7.  Sorting the nuclear proteome.

Authors:  Denis C Bauer; Kai Willadsen; Fabian A Buske; Kim-Anh Lê Cao; Timothy L Bailey; Graham Dellaire; Mikael Bodén
Journal:  Bioinformatics       Date:  2011-07-01       Impact factor: 6.937

8.  An ensemble method for predicting subnuclear localizations from primary protein structures.

Authors:  Guo Sheng Han; Zu Guo Yu; Vo Anh; Anaththa P D Krishnajith; Yu-Chu Tian
Journal:  PLoS One       Date:  2013-02-27       Impact factor: 3.240

9.  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

10.  Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing.

Authors:  Emily Chia-Yu Su; Jia-Ming Chang; Cheng-Wei Cheng; Ting-Yi Sung; Wen-Lian Hsu
Journal:  BMC Bioinformatics       Date:  2012-12-13       Impact factor: 3.169

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