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  Predicting kinase inhibitor resistance: Physics-based and data-driven approaches.

Aldeghi, M., Gapsys, V., & de Groot, B. L. (2019). Predicting kinase inhibitor resistance: Physics-based and data-driven approaches. ACS Central Science, 5(8), 1468-1474. doi:10.1021/acscentsci.9b00590.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-9E5C-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-9E63-B
Genre: Journal Article

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 Creators:
Aldeghi, M.1, Author              
Gapsys, V.1, Author              
de Groot, B. L.1, Author              
Affiliations:
1Research Group of Computational Biomolecular Dynamics, MPI for biophysical chemistry, Max Planck Society, ou_578573              

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 Abstract: Resistance to small molecule drugs often emerges in cancer cells, viruses, and bacteria as a result of the evolutionary pressure exerted by the therapy. Protein mutations that directly impair drug binding are frequently involved in resistance, and the ability to anticipate these mutations would be beneficial in drug development and clinical practice. Here, we evaluate the ability of three distinct computational methods to predict ligand binding affinity changes upon protein mutation for the cancer target Abl kinase. These structure-based approaches rely on first-principle statistical mechanics, mixed physics- and knowledge-based potentials, and machine learning, and were able to estimate binding affinity changes and identify resistant mutations with remarkable accuracy. We expect that these complementary approaches will enable the routine prediction of resistance-causing mutations in a variety of other target proteins.

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Language(s): eng - English
 Dates: 2019-08-132019
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: DOI: 10.1021/acscentsci.9b00590
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Title: ACS Central Science
Source Genre: Journal
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Pages: - Volume / Issue: 5 (8) Sequence Number: - Start / End Page: 1468 - 1474 Identifier: -