ArticleAIDS research and therapy2010
Autoprocessing of human immunodeficiency virus type 1 protease miniprecursor fusions in mammalian cells.
Article in AIDS research and therapy, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 16 citations in OpenAlex.
- Molecular mechanisms of protease precursor autoprocessing of RNA viruses: a comprehensive review.Virology journal · 2026Review
- High-throughput screening and characterization of novel inhibitors targeting HIV-1 protease precursor autoprocessing.Scientific reports · 2025Article
- Article
- Nanoscale flow cytometry reveals interpatient variability in HIV protease activity that correlates with viral infectivity and identifies drug-resistant viruses.Scientific reports · 2020Article
- Potent antiviral HIV-1 protease inhibitor combats highly drug resistant mutant PR20.Biochemical and biophysical research communications · 2019Article
- Targeting HIV-1 Protease Autoprocessing for High-throughput Drug Discovery and Drug Resistance Assessment.Scientific reports · 2019Article
- Context-dependent autoprocessing of human immunodeficiency virus type 1 protease precursors.PloS one · 2018Article
- Modified Roller Tube Method for Precisely Localized and Repetitive Intermittent Imaging During Long-term Culture of Brain Slices in an Enclosed System.Journal of visualized experiments : JoVE · 2017Article
- The HIV-1 late domain-2 S40A polymorphism in antiretroviral (or ART)-exposed individuals influences protease inhibitor susceptibility.Retrovirology · 2016Article
- Article
- TPC2 mediates new mechanisms of platelet dense granule membrane dynamics through regulation of Ca2+ release.Molecular biology of the cell · 2015Article
- Article
- Understanding HIV-1 protease autoprocessing for novel therapeutic development.Future medicinal chemistry · 2013Article
- Flexible catalytic site conformations implicated in modulation of HIV-1 protease autoprocessing reactions.Retrovirology · 2011Article
- Inhibition of autoprocessing of natural variants and multidrug resistant mutant precursors of HIV-1 protease by clinical inhibitors.Proceedings of the National Academy of Sciences of the United States of America · 2011Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHIV protease (PR) is a virus-encoded aspartic protease that is essential for viral replication and infectivity. The fully active and mature dimeric protease is released from the Gag-Pol polyprotein as a result of precursor autoprocessing.
resultsWe here describe a simple model system to directly examine HIV protease autoprocessing in transfected mammalian cells. A fusion precursor was engineered encoding GST fused to a well-characterized miniprecursor, consisting of the mature protease along with its upstream transframe region (TFR), and small peptide epitopes to facilitate detection of the precursor substrate and autoprocessing products. In HEK 293T cells, the resulting chimeric precursor undergoes effective autoprocessing, producing mature protease that is rapidly degraded likely via autoproteolysis. The known protease inhibitors Darunavir and Indinavir suppressed both precursor autoprocessing and autoproteolysis in a dose-dependent manner. Protease mutations that inhibit Gag processing as characterized using proviruses also reduced autoprocessing efficiency when they were introduced to the fusion precursor. Interestingly, autoprocessing of the fusion precursor requires neither the full proteolytic activity nor the majority of the N-terminal TFR region.
conclusionsWe suggest that the fusion precursors provide a useful system to study protease autoprocessing in mammalian cells, and may be further developed for screening of new drugs targeting HIV protease autoprocessing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.