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A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
Published on: December 7, 2014
Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches
Matteo Aldeghi1, Vytautas Gapsys1, Bert L de Groot1
1Computational Biomolecular Dynamics Group, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany.
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.
Insights
Predicting drug resistance mutations in proteins is crucial for developing new therapies. This study shows computational methods accurately identify mutations that cause resistance to cancer drugs like Abl kinase inhibitors.
Area of Science:
- Computational biology
- Drug discovery
- Molecular modeling
Background:
- Drug resistance is a major challenge in treating cancer, viruses, and bacteria.
- Protein mutations that affect drug binding are a common cause of resistance.
- Anticipating these mutations can aid drug development and clinical strategies.
Purpose of the Study:
- To evaluate computational methods for predicting changes in ligand binding affinity due to protein mutations.
- To assess the accuracy of these methods in identifying drug-resistant mutations for the Abl kinase target.
Main Methods:
- Utilized three distinct structure-based computational approaches: first-principle statistical mechanics, mixed physics- and knowledge-based potentials, and machine learning.
- Applied these methods to predict binding affinity changes upon mutation for the Abl kinase.
Main Results:
- The computational methods accurately estimated binding affinity changes caused by mutations.
- These approaches successfully identified mutations conferring resistance to Abl kinase inhibitors.
- Remarkable accuracy was observed in predicting resistance-causing mutations.
Conclusions:
- Computational methods can reliably predict drug resistance mutations.
- These complementary approaches offer a powerful tool for anticipating resistance in various target proteins.
- Routine prediction of resistance mutations will benefit drug development and patient treatment.
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