Dataset Information

Targeting HIV-1 Protease Autoprocessing for High-throughput Drug Discovery and Drug Resistance Assessment.

ABSTRACT: HIV-1 protease autoprocessing liberates the free mature protease from its Gag-Pol polyprotein precursor through a series of highly regulated autoproteolysis reactions. Herein, we report the development and validation (Z'???0.50) of a cell-based functional assay for high-throughput screening (HTS) of autoprocessing inhibitors using fusion precursors in combination with AlphaLISA (amplified luminescent proximity homogeneous assay ELISA). Through pilot screening of a collection of 130 known protease inhibitors, the AlphaLISA assay confirmed all 11 HIV protease inhibitors in the library capable of suppressing precursor autoprocessing at low micromolar concentrations. Meanwhile, other protease inhibitors had no impact on precursor autoprocessing. We next conducted HTS of ~23,000 compounds but found no positive hits. Such high selectivity is advantageous for large-scale HTS campaigns and as anticipated based on assay design because a positive hit needs simultaneously to be nontoxic, cell permeable, and inhibiting precursor autoprocessing. Furthermore, AlphaLISA quantification of fusion precursors carrying mutations known to cause resistance to HIV protease inhibitors faithfully recapitulated the reported resistance, suggesting that precursor autoprocessing is a critical step contributing to drug resistance. Taken together, this reported AlphaLISA platform will provide a useful tool for drug discovery targeting HIV-1 protease autoprocessing and for quantification of PI resistance.

SUBMITTER: Huang L

PROVIDER: S-EPMC6343032 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Micro- and nanoscale technologies have radically transformed biological research from genomics to tissue engineering, with the relative exception of microbial cell culture, which is still largely performed in microtiter plates and petri dishes. Here, we present nanoscale culture of the opportunistic fungal pathogen Candida albicans on a microarray platform. The microarray consists of 1,200 individual cultures of 30 nl of C. albicans biofilms ("nano-biofilms") encapsulated in an inert alginate matrix. We demonstrate that these nano-biofilms are similar to conventional macroscopic biofilms in their morphological, architectural, growth, and phenotypic characteristics. We also demonstrate that the nano-biofilm microarray is a robust and efficient tool for accelerating the drug discovery process: (i) combinatorial screening against a collection of 28 antifungal compounds in the presence of immunosuppressant FK506 (tacrolimus) identified six drugs that showed synergistic antifungal activity, and (ii) screening against the NCI challenge set small-molecule library identified three heretofore-unknown hits. This cell-based microarray platform allows for miniaturization of microbial cell culture and is fully compatible with other high-throughput screening technologies.Microorganisms are typically still grown in petri dishes, test tubes, and Erlenmeyer flasks in spite of the latest advances in miniaturization that have benefitted other allied research fields, including genomics and proteomics. Culturing microorganisms in small scale can be particularly valuable in cutting down time, cost, and reagent usage. This paper describes the development, characterization, and application of nanoscale culture of an opportunistic fungal pathogen, Candida albicans. Despite a more than 2,000-fold reduction in volume, the growth characteristics and drug response profiles obtained from the nanoscale cultures were comparable to the industry standards. The platform also enabled rapid identification of new drug candidates that were effective against C. albicans biofilms, which are a major cause of mortality in hospital-acquired infections.

Dataset Information

Targeting HIV-1 Protease Autoprocessing for High-throughput Drug Discovery and Drug Resistance Assessment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets