Journal article

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

Yunzhuo Zhou, Stephanie Portelli, Megan Pat, Carlos HM Rodrigues, Nguyen Thanh-Binh, Douglas E Pires, David B Ascher



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 m..

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Awarded by Medical Research Council

Awarded by Jack Brockhoff Foundation

Awarded by Wellcome Trust

Awarded by National Health and Medical Research Council (NHMRC) of Australia

Funding Acknowledgements

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) . D.B. A. was supported by the Jack Brockhoff Foundation (No. JBF 4186, 2016) , Wellcome Trust (200814/Z/16/Z) and an Investigator Grant from the National Health and Medical Research Council (NHMRC) of Australia (No. 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 Manu-script version arising from this submission.