Literature DB >> 17978804

Improved prediction of coreceptor usage and phenotype of HIV-1 based on combined features of V3 loop sequence using random forest.

Shungao Xu1, Xinxiang Huang, Huaxi Xu, Chiyu Zhang.   

Abstract

HIV-1 coreceptor usage and phenotype mainly determined by V3 loop are associated with the disease progression of AIDS. Predicting HIV-1 coreceptor usage and phenotype facilitates the monitoring of R5-to-X4 switch and treatment decision-making. In this study, we employed random forest to predict HIV-1 biological phenotype, based on 37 random features of V3 loop. In comparison with PSSM method, our RF predictor obtained higher prediction accuracy (95.1% for coreceptor usage and 92.1% for phenotype), especially for non-B non-C HIV-1 subtypes (96.6% for coreceptor usage and 95.3% for phenotype). The net charge, polarity of V3 loop and five V3 sites are seven most important features for predicting HIV-1 coreceptor usage or phenotype. Among these features, V3 polarity and four V3 sites (22, 12, 18 and 13) are first reported to have high contribution to HIV-1 biological phenotype prediction.

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Year:  2007        PMID: 17978804

Source DB:  PubMed          Journal:  J Microbiol        ISSN: 1225-8873            Impact factor:   3.422


  16 in total

1.  Genetic signatures of HIV-1 envelope-mediated bystander apoptosis.

Authors:  Anjali Joshi; Raphael T C Lee; Jonathan Mohl; Melina Sedano; Wei Xin Khong; Oon Tek Ng; Sebastian Maurer-Stroh; Himanshu Garg
Journal:  J Biol Chem       Date:  2013-11-21       Impact factor: 5.157

2.  Structure of HIV-1 quasi-species as early indicator for switches of co-receptor tropism.

Authors:  J Nikolaj Dybowski; Dominik Heider; Daniel Hoffmann
Journal:  AIDS Res Ther       Date:  2010-11-30       Impact factor: 2.250

3.  Comparative determination of HIV-1 co-receptor tropism by Enhanced Sensitivity Trofile, gp120 V3-loop RNA and DNA genotyping.

Authors:  Mattia C F Prosperi; Laura Bracciale; Massimiliano Fabbiani; Simona Di Giambenedetto; Francesca Razzolini; Genny Meini; Manuela Colafigli; Angela Marzocchetti; Roberto Cauda; Maurizio Zazzi; Andrea De Luca
Journal:  Retrovirology       Date:  2010-06-30       Impact factor: 4.602

4.  Accurate and efficient gp120 V3 loop structure based models for the determination of HIV-1 co-receptor usage.

Authors:  Majid Masso; Iosif I Vaisman
Journal:  BMC Bioinformatics       Date:  2010-10-05       Impact factor: 3.169

5.  Bioinformatic prediction programs underestimate the frequency of CXCR4 usage by R5X4 HIV type 1 in brain and other tissues.

Authors:  Megan E Mefford; Paul R Gorry; Kevin Kunstman; Steven M Wolinsky; Dana Gabuzda
Journal:  AIDS Res Hum Retroviruses       Date:  2008-09       Impact factor: 2.205

6.  Tissue-specific sequence alterations in the human immunodeficiency virus type 1 envelope favoring CCR5 usage contribute to persistence of dual-tropic virus in the brain.

Authors:  Lachlan Gray; Michael Roche; Melissa J Churchill; Jasminka Sterjovski; Anne Ellett; Pantelis Poumbourios; Shameem Sherieff; Shameem Sheffief; Bin Wang; Nitin Saksena; Damian F J Purcell; Steven Wesselingh; Anthony L Cunningham; Bruce J Brew; Dana Gabuzda; Paul R Gorry
Journal:  J Virol       Date:  2009-03-25       Impact factor: 5.103

7.  Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning Approach.

Authors:  Yue Pan; Hongmei Liu; Lisa R Metsch; Daniel J Feaster
Journal:  AIDS Behav       Date:  2017-02

8.  Machine learning on normalized protein sequences.

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

9.  Predicting protein phenotypes based on protein-protein interaction network.

Authors:  Lele Hu; Tao Huang; Xiao-Jun Liu; Yu-Dong Cai
Journal:  PLoS One       Date:  2011-03-10       Impact factor: 3.240

10.  Hybrid approach for predicting coreceptor used by HIV-1 from its V3 loop amino acid sequence.

Authors:  Ravi Kumar; Gajendra P S Raghava
Journal:  PLoS One       Date:  2013-04-15       Impact factor: 3.240

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