Literature DB >> 17427231

Computational proteomics analysis of HIV-1 protease interactome.

Aleksejs Kontijevskis1, Jarl E S Wikberg, Jan Komorowski.   

Abstract

HIV-1 protease is a small homodimeric enzyme that ensures maturation of HIV virions by cleaving the viral precursor Gag and Gag-Pol polyproteins into structural and functional elements. The cleavage sites in the viral polyproteins share neither sequence homology nor binding motif and the specificity of the HIV-1 protease is therefore only partially understood. Using an extensive data set collected from 16 years of HIV proteome research we have here created a general and predictive rule-based model for HIV-1 protease specificity based on rough sets. We demonstrate that HIV-1 protease specificity is much more complex than previously anticipated, which cannot be defined based solely on the amino acids at the substrate's scissile bond or by any other single substrate amino acid position only. Our results show that the combination of at least three particular amino acids is needed in the substrate for a cleavage event to occur. Only by combining and analyzing massive amounts of HIV proteome data it was possible to discover these novel and general patterns of physico-chemical substrate cleavage determinants. Our study is an example how computational biology methods can advance the understanding of the viral interactomes. 2007 Wiley-Liss, Inc.

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Year:  2007        PMID: 17427231     DOI: 10.1002/prot.21415

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  11 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.  Identification of structural mechanisms of HIV-1 protease specificity using computational peptide docking: implications for drug resistance.

Authors:  Sidhartha Chaudhury; Jeffrey J Gray
Journal:  Structure       Date:  2009-12-09       Impact factor: 5.006

3.  A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome.

Authors:  Marcin Kierczak; Krzysztof Ginalski; Michał Dramiński; Jacek Koronacki; Witold Rudnicki; Jan Komorowski
Journal:  Bioinform Biol Insights       Date:  2009-10-05

4.  Genome-wide search for the genes accountable for the induced resistance to HIV-1 infection in activated CD4+ T cells: apparent transcriptional signatures, co-expression networks and possible cellular processes.

Authors:  Wen-Wen Xu; Miao-Jun Han; Dai Chen; Ling Chen; Yan Guo; Andrew Willden; Di-Qiu Liu; Hua-Tang Zhang
Journal:  BMC Med Genomics       Date:  2013-05-01       Impact factor: 3.063

5.  A complete map of potential pathogenicity markers of avian influenza virus subtype H5 predicted from 11 expressed proteins.

Authors:  Zeeshan Khaliq; Mikael Leijon; Sándor Belák; Jan Komorowski
Journal:  BMC Microbiol       Date:  2015-06-26       Impact factor: 3.605

6.  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

7.  Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features.

Authors:  Onkar Singh; Emily Chia-Yu Su
Journal:  BMC Bioinformatics       Date:  2016-12-23       Impact factor: 3.169

8.  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

9.  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

10.  Ciruvis: a web-based tool for rule networks and interaction detection using rule-based classifiers.

Authors:  Susanne Bornelöv; Simon Marillet; Jan Komorowski
Journal:  BMC Bioinformatics       Date:  2014-05-12       Impact factor: 3.169

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