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Zhou Y, Portelli S, Pat M, Rodrigues CH, Nguyen TB, Pires DE, Ascher DB. Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase. Comput Struct Biotechnol J 2021; 19:5381-5391. [PMID: 34667533 PMCID: PMC8495037 DOI: 10.1016/j.csbj.2021.09.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/15/2021] [Accepted: 09/15/2021] [Indexed: 02/02/2023] Open
Abstract
Kinases play crucial roles in cellular signalling and biological processes with their dysregulation associated with diseases, including cancers. Kinase inhibitors, most notably those targeting ABeLson 1 (ABL1) kinase in chronic myeloid leukemia, have had a significant impact on cancer survival, yet emergence of resistance mutations can reduce their effectiveness, leading to therapeutic failure. Limited effort, however, has been devoted to developing tools to accurately identify ABL1 resistance mutations, as well as providing insights into their molecular mechanisms. Here we investigated the structural basis of ABL1 mutations modulating binding affinity of eight FDA-approved drugs. We found mutations impair affinity of type I and type II inhibitors differently and used this insight to developed a novel web-based diagnostic tool, SUSPECT-ABL, to pre-emptively predict resistance profiles and binding free-energy changes (ΔΔG) of all possible ABL1 mutations against inhibitors with different binding modes. Resistance mutations in ABL1 were successfully identified, achieving a Matthew's Correlation Coefficient of up to 0.73 and the resulting change in ligand binding affinity with a Pearson's correlation of up to 0.77, with performances consistent across non-redundant blind tests. Through an in silico saturation mutagenesis, our tool has identified possibly emerging resistance mutations, which offers opportunities for in vivo experimental validation. We believe SUSPECT-ABL will be an important tool not just for improving precision medicine efforts, but for facilitating the development of next-generation inhibitors that are less prone to resistance. We have made our tool freely available at http://biosig.unimelb.edu.au/suspect_abl/.
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Affiliation(s)
- Yunzhuo Zhou
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Stephanie Portelli
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Megan Pat
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Carlos H.M. Rodrigues
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Thanh-Binh Nguyen
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
| | - Douglas E.V. Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B. Ascher
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biochemistry, University of Cambridge, 80 Tennis Ct Rd, Cambridge CB2 1GA, UK
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