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Journal Article

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

MPS-Authors
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Aldeghi,  M.
Research Group of Computational Biomolecular Dynamics, MPI for biophysical chemistry, Max Planck Society;

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Gapsys,  V.
Research Group of Computational Biomolecular Dynamics, MPI for biophysical chemistry, Max Planck Society;

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de Groot,  B. L.
Research Group of Computational Biomolecular Dynamics, MPI for biophysical chemistry, Max Planck Society;

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Fulltext (public)

3159145.pdf
(Publisher version), 2MB

Supplementary Material (public)

3159145_Suppl_1.pdf
(Supplementary material), 7MB

3159145_Suppl_2.xlsx
(Supplementary material), 46KB

Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0004-9E5C-4
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.