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Compound prioritization methods increase rates of chemical probe discovery in model organisms.

ABSTRACT: Preselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in Saccharomyces cerevisiae and identified ~7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes, we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ~7500 growth-inhibitory molecules have been made commercially available and the computational model and filter used are provided.

SUBMITTER: Wallace IM

PROVIDER: S-EPMC4193902 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Compound prioritization methods increase rates of chemical probe discovery in model organisms.

Wallace Iain M IM Urbanus Malene L ML Luciani Genna M GM Burns Andrew R AR Han Mitchell K L MK Wang Hao H Arora Kriti K Heisler Lawrence E LE Proctor Michael M St Onge Robert P RP Roemer Terry T Roy Peter J PJ Cummins Carolyn L CL Bader Gary D GD Nislow Corey C Giaever Guri G

Chemistry & biology 20111001 10

Preselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in Saccharomyces cerevisiae and identified ~7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. E ...[more]

PMID: 22035796

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Project description:During the past two decades, the world has witnessed the emergence of various SARS-CoV-2 variants with distinct mutational profiles influencing the global health, economy, and clinical aspects of the COVID-19 pandemic. These variants or mutants have raised major concerns regarding the protection provided by neutralizing monoclonal antibodies and vaccination, rates of virus transmission, and/or the risk of reinfection. The newly emerged Omicron, a genetically distinct lineage of SARS-CoV-2, continues its spread in the face of rising vaccine-induced immunity while maintaining its replication fitness. Efforts have been made to improve the therapeutic interventions and the FDA has issued Emergency Use Authorization for a few monoclonal antibodies and drug treatments for COVID-19. However, the current situation of rapidly spreading Omicron and its lineages demands the need for effective therapeutic interventions to reduce the COVID-19 pandemic. Several experimental studies have indicated that the FDA-approved monoclonal antibodies are less effective than antiviral drugs against the Omicron variant. Thus, in this study, we aim to identify antiviral compounds against the Spike protein of Omicron, which binds to the human angiotensin-converting enzyme 2 (ACE2) receptor and facilitates virus invasion. Initially, docking-based virtual screening of the in-house database was performed to extract the potential hit compounds against the Spike protein. The obtained hits were optimized by DFT calculations to determine the electronic properties and molecular reactivity of the compounds. Further, MD simulation studies were carried out to evaluate the dynamics of protein-ligand interactions at an atomistic level in a time-dependent manner. Collectively, five compounds (AKS-01, AKS-02, AKS-03, AKS-04, and AKS-05) with diverse scaffolds were identified as potential hits against the Spike protein of Omicron. Our study paves the way for further in vitro and in vivo studies.

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Compound prioritization methods increase rates of chemical probe discovery in model organisms.

Publications

Compound prioritization methods increase rates of chemical probe discovery in model organisms.

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