Dataset Information

Mapping the cellular response to small molecules using chemogenomic fitness signatures.

ABSTRACT: Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes.

SUBMITTER: Lee AY

PROVIDER: S-EPMC4254748 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Mapping the cellular response to small molecules using chemogenomic fitness signatures.

Lee Anna Y AY St Onge Robert P RP Proctor Michael J MJ Wallace Iain M IM Nile Aaron H AH Spagnuolo Paul A PA Jitkova Yulia Y Gronda Marcela M Wu Yan Y Kim Moshe K MK Cheung-Ong Kahlin K Torres Nikko P NP Spear Eric D ED Han Mitchell K L MK Schlecht Ulrich U Suresh Sundari S Duby Geoffrey G Heisler Lawrence E LE Surendra Anuradha A Fung Eula E Urbanus Malene L ML Gebbia Marinella M Lissina Elena E Miranda Molly M Chiang Jennifer H JH Aparicio Ana Maria AM Zeghouf Mahel M Davis Ronald W RW Cherfils Jacqueline J Boutry Marc M Kaiser Chris A CA Cummins Carolyn L CL Trimble William S WS Brown Grant W GW Schimmer Aaron D AD Bankaitis Vytas A VA Nislow Corey C Bader Gary D GD Giaever Guri G

Science (New York, N.Y.) 20140401 6180

Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that ...[more]

PMID: 24723613

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Project description:The development of new, effective, and safe drugs to treat cancer remains a challenging and time-consuming task due to limited hit rates, restraining subsequent development efforts. Despite the impressive progress of quantitative structure-activity relationship and machine learning-based models that have been developed to predict molecule pharmacodynamics and bioactivity, they have had mixed success at identifying compounds with anticancer properties against multiple cell lines. Here, we have developed a novel predictive tool, pdCSM-cancer, which uses a graph-based signature representation of the chemical structure of a small molecule in order to accurately predict molecules likely to be active against one or multiple cancer cell lines. pdCSM-cancer represents the most comprehensive anticancer bioactivity prediction platform developed till date, comprising trained and validated models on experimental data of the growth inhibition concentration (GI50%) effects, including over 18,000 compounds, on 9 tumor types and 74 distinct cancer cell lines. Across 10-fold cross-validation, it achieved Pearson's correlation coefficients of up to 0.74 and comparable performance of up to 0.67 across independent, non-redundant blind tests. Leveraging the insights from these cell line-specific models, we developed a generic predictive model to identify molecules active in at least 60 cell lines. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.94 on 10-fold cross-validation and up to 0.94 on independent non-redundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a valuable resource to optimizing and enriching screening libraries for the identification of effective and safe anticancer molecules. To provide a simple and integrated platform to rapidly screen for potential biologically active molecules with favorable anticancer properties, we made pdCSM-cancer freely available online at http://biosig.unimelb.edu.au/pdcsm_cancer.

Dataset Information

Mapping the cellular response to small molecules using chemogenomic fitness signatures.

Publications

Mapping the cellular response to small molecules using chemogenomic fitness signatures.

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