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Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase.

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Zhou, Yunzhuo 
Portelli, Stephanie 
Pat, Megan 
Rodrigues, Carlos HM 
Nguyen, Thanh-Binh 


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



Abelson 1 kinase, Drug resistance, Graph-based signatures, Mutations, Structure-guided machine learning

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Comput Struct Biotechnol J

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Elsevier BV


All rights reserved
Medical Research Council (MR/M026302/1)
Wellcome Trust (200814/Z/16/Z)
S.P. was funded by a Melbourne Research Scholarship. D.B.A. and D.E.V.P. were funded by a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MR/M026302/1); the Jack Brockhoff Foundation (JBF 4186, 2016), Wellcome Trust (200814/Z/16/Z) and an Investigator Grant from the National Health and Medical Research Council (NHMRC) of Australia (GNT1174405). Supported in part by the Victorian Government’s Operational Infrastructure Support Program. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission