Literature DB >> 17620050

Bioinformatic approaches for modeling the substrate specificity of HIV-1 protease: an overview.

Thorsteinn Rögnvaldsson1, Liwen You, Daniel Garwicz.   

Abstract

HIV-1 protease has a broad and complex substrate specificity, which hitherto has escaped a simple comprehensive definition. This, and the relatively high mutation rate of the retroviral protease, makes it challenging to design effective protease inhibitors. Several attempts have been made during the last two decades to elucidate the enigmatic cleavage specificity of HIV-1 protease and to predict cleavage of novel substrates using bioinformatic analysis methods. This review describes the methods that have been utilized to date to address this important problem and the results achieved. The data sets used are also reviewed and important aspects of these are highlighted.

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Year:  2007        PMID: 17620050     DOI: 10.1586/14737159.7.4.435

Source DB:  PubMed          Journal:  Expert Rev Mol Diagn        ISSN: 1473-7159            Impact factor:   5.225


  5 in total

1.  The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity.

Authors:  Timmy Manning; Paul Walsh
Journal:  Bioengineered       Date:  2016-04-02       Impact factor: 3.269

2.  PMeS: prediction of methylation sites based on enhanced feature encoding scheme.

Authors:  Shao-Ping Shi; Jian-Ding Qiu; Xing-Yu Sun; Sheng-Bao Suo; Shu-Yun Huang; Ru-Ping Liang
Journal:  PLoS One       Date:  2012-06-15       Impact factor: 3.240

3.  Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction.

Authors:  Hui Liu; Xiaomiao Shi; Dongmei Guo; Zuowei Zhao
Journal:  Biomed Res Int       Date:  2015-04-15       Impact factor: 3.411

4.  How to find simple and accurate rules for viral protease cleavage specificities.

Authors:  Thorsteinn Rögnvaldsson; Terence A Etchells; Liwen You; Daniel Garwicz; Ian Jarman; Paulo J G Lisboa
Journal:  BMC Bioinformatics       Date:  2009-05-16       Impact factor: 3.169

5.  A genetic approach for building different alphabets for peptide and protein classification.

Authors:  Loris Nanni; Alessandra Lumini
Journal:  BMC Bioinformatics       Date:  2008-01-24       Impact factor: 3.169

  5 in total

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