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Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies

32 Pages Posted: 22 Mar 2019 Sneak Peek Status: Review Complete

See all articles by Balaguru Ravikumar

Balaguru Ravikumar

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Sanna Timonen

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Zaid Alam

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Elina Parri

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Krister Wennerberg

University of Helsinki - Institute for Molecular Medicine Finland (FIMM); University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

Tero Aittokallio

University of Helsinki - Institute for Molecular Medicine Finland (FIMM); University of Turku - Department of Mathematics and Statistics

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Abstract

Due to the intrinsic polypharmacological nature of most small-molecule kinase inhibitors, there is a need for computational models that enable systematic exploration of the chemogenomic landscape underlying druggable kinome toward more efficient kinome profiling strategies. We implemented VirtualKinomeProfiler, an efficient computational platform that captures distinct representations of chemical similarity space of the druggable kinome for various drug discovery endeavors. By employing the computational platform, we profiled approximately 37 million compound-kinase pairs and made predictions for 151,708 compounds in terms of their repositioning and lead molecule potential against 248 kinases simultaneously. Experimental testing with biochemical assays validated 51 of the predicted interactions, identifying 19 small-molecule inhibitors of EGFR, HCK, FLT1, and MSK1 protein kinases. The prediction model led to a 1.5-fold increase in precision and 2.8-fold decrease in false discovery rate, when compared to traditional single-dose biochemical screening, which demonstrates its potential to drastically expedite the kinome-specific drug discovery process.

Keywords: Chemogenomic analysis, Statistical and machine learning model, High-throughput virtual kinome profiling, Compound repositioning and lead identification, Cancer-specific drug discovery

Suggested Citation

Ravikumar, Balaguru and Timonen, Sanna and Alam, Zaid and Parri, Elina and Wennerberg, Krister and Aittokallio, Tero, Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies (March 20, 2019). Available at SSRN: https://ssrn.com/abstract=3356840 or http://dx.doi.org/10.2139/ssrn.3356840
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Balaguru Ravikumar

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Helsinki, FIN-00014
Finland

Sanna Timonen

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Helsinki, FIN-00014
Finland

Zaid Alam

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Helsinki, FIN-00014
Finland

Elina Parri

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Helsinki, FIN-00014
Finland

Krister Wennerberg

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Helsinki, FIN-00014
Finland

University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

Nørregade 10
Copenhagen, København DK-2200
Denmark

Tero Aittokallio (Contact Author)

University of Helsinki - Institute for Molecular Medicine Finland (FIMM) ( email )

Helsinki, FIN-00014
Finland

University of Turku - Department of Mathematics and Statistics ( email )

FIN-20014
United States

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