Literature DB >> 11297664

Using subsite coupling to predict signal peptides.

K C Chou1.   

Abstract

Given a nascent protein sequence, how can one predict its signal peptide or "Zipcode" sequence? This is a first important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy. Based on a model that takes into account the coupling effect among some key subsites, the so-called [-3, -1, +1] coupling model, a new prediction algorithm is developed. The overall rate of correct prediction for 1939 secretory proteins and 1440 non-secretary proteins was over 92%. It has not escaped our attention that the new method may also serve as a useful tool for helping investigate further many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells.

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Year:  2001        PMID: 11297664     DOI: 10.1093/protein/14.2.75

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  63 in total

1.  iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.

Authors:  Hao Lin; En-Ze Deng; Hui Ding; Wei Chen; Kuo-Chen Chou
Journal:  Nucleic Acids Res       Date:  2014-10-31       Impact factor: 16.971

2.  Signal peptide prediction based on analysis of experimentally verified cleavage sites.

Authors:  Zemin Zhang; William J Henzel
Journal:  Protein Sci       Date:  2004-08-31       Impact factor: 6.725

3.  Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

Authors:  Guang-Hui Liu; Hong-Bin Shen; Dong-Jun Yu
Journal:  J Membr Biol       Date:  2015-11-12       Impact factor: 1.843

4.  iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.

Authors:  Muhammad Kabir; Maqsood Hayat
Journal:  Mol Genet Genomics       Date:  2015-08-30       Impact factor: 3.291

5.  TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition.

Authors:  Xue He; Ke Han; Jun Hu; Hui Yan; Jing-Yu Yang; Hong-Bin Shen; Dong-Jun Yu
Journal:  J Membr Biol       Date:  2015-06-10       Impact factor: 1.843

6.  XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites.

Authors:  Kewei Liu; Wei Chen; Hao Lin
Journal:  Mol Genet Genomics       Date:  2019-08-07       Impact factor: 3.291

7.  iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou's 5-step rule.

Authors:  Nguyen Quoc Khanh Le
Journal:  Mol Genet Genomics       Date:  2019-05-04       Impact factor: 3.291

8.  iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC.

Authors:  Xuan Xiao; Mengjuan Hui; Zi Liu
Journal:  J Membr Biol       Date:  2016-11-03       Impact factor: 1.843

9.  Influence of N-terminal truncations on the functional expression of Bacillus licheniformis gamma-glutamyltranspeptidase in recombinant Escherichia coli.

Authors:  Long-Liu Lin; Li-Yu Yang; Hui-Yu Hu; Huei-Fen Lo
Journal:  Curr Microbiol       Date:  2008-09-23       Impact factor: 2.188

10.  A defective signal peptide in a 19-kD alpha-zein protein causes the unfolded protein response and an opaque endosperm phenotype in the maize De*-B30 mutant.

Authors:  Cheol Soo Kim; Brenda G Hunter; Jeffery Kraft; Rebecca S Boston; Sarah Yans; Rudolf Jung; Brian A Larkins
Journal:  Plant Physiol       Date:  2003-12-04       Impact factor: 8.340

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