Literature DB >> 8871474

Neural network prediction of the HIV-1 protease cleavage sites.

T B Thompson1, K C Chou, C Zheng.   

Abstract

A back propagation neural network method has been developed to study the pattern of polypeptides that can be cleaved by the HIV-1 protease. This method can incorporate many characteristics of the peptides, such as hydrophobicity, beta-sheet and alpha-helix propensities. Mutations can also be applied to probe the most important factors that influence the cleavage.

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Year:  1995        PMID: 8871474     DOI: 10.1006/jtbi.1995.0254

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  14 in total

1.  Predicting human immunodeficiency virus protease cleavage sites in nonlinear projection space.

Authors:  Xuehua Li; Hongli Hu; Lan Shu
Journal:  Mol Cell Biochem       Date:  2010-01-07       Impact factor: 3.396

2.  Comprehensive bioinformatic analysis of the specificity of human immunodeficiency virus type 1 protease.

Authors:  Liwen You; Daniel Garwicz; Thorsteinn Rögnvaldsson
Journal:  J Virol       Date:  2005-10       Impact factor: 5.103

3.  Machine learning on normalized protein sequences.

Authors:  Dominik Heider; Jens Verheyen; Daniel Hoffmann
Journal:  BMC Res Notes       Date:  2011-03-31

4.  A consistency-based feature selection method allied with linear SVMs for HIV-1 protease cleavage site prediction.

Authors:  Orkun Oztürk; Alper Aksaç; Abdallah Elsheikh; Tansel Ozyer; Reda Alhajj
Journal:  PLoS One       Date:  2013-08-23       Impact factor: 3.240

5.  iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.

Authors:  Yue-Nong Fan; Xuan Xiao; Jian-Liang Min; Kuo-Chen Chou
Journal:  Int J Mol Sci       Date:  2014-03-19       Impact factor: 5.923

6.  A simple structure-based model for the prediction of HIV-1 co-receptor tropism.

Authors:  Dominik Heider; Jan Nikolaj Dybowski; Christoph Wilms; Daniel Hoffmann
Journal:  BioData Min       Date:  2014-08-01       Impact factor: 2.522

7.  iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.

Authors:  Wang-Ren Qiu; Xuan Xiao; Kuo-Chen Chou
Journal:  Int J Mol Sci       Date:  2014-01-24       Impact factor: 5.923

8.  iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach.

Authors:  Wang-Ren Qiu; Xuan Xiao; Wei-Zhong Lin; Kuo-Chen Chou
Journal:  Biomed Res Int       Date:  2014-05-22       Impact factor: 3.411

9.  iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.

Authors:  Wei Chen; Peng-Mian Feng; Hao Lin; Kuo-Chen Chou
Journal:  Biomed Res Int       Date:  2014-05-21       Impact factor: 3.411

10.  iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels.

Authors:  Hui Ding; En-Ze Deng; Lu-Feng Yuan; Li Liu; Hao Lin; Wei Chen; Kuo-Chen Chou
Journal:  Biomed Res Int       Date:  2014-06-01       Impact factor: 3.411

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