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

Zhou, Yunzhuo; Portelli, Stephanie; Pat, Megan; Rodrigues, Carlos H M; Nguyễn, Thanh Bình; Pires, Douglas E V; Ascher, David B.

doi:10.1016/j.csbj.2021.09.016

Cited by 12 publications

(9 citation statements)

References 50 publications

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“…In the fully active state, despite the ligand binding, the αC-helix and the activation loop (A-loop) in the kinase domain adopt the “IN” and “extended” conformations, respectively. However, as captured by different orthosteric inhibitors, the kinase domain inhibitory states of Abl are diverse [39] . Type-Ⅰ inhibitors (e.g., dasatinib and bosutinib) prefer the catalytically active conformation, while type-Ⅱ inhibitors (e.g., imatinib, nilotinib, and ponatinib) favor the inactive form [40] .…”

Section: Introductionmentioning

confidence: 99%

Allosteric regulation of autoinhibition and activation of c-Abl

Liu

Zhang²,

Tsai³

et al. 2022

Computational and Structural Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

Allosteric regulation of autoinhibition and activation of c-Abl

Liu

Zhang²,

Tsai³

et al. 2022

Computational and Structural Biotechnology Journal

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“…The atom pharmacophores are characteristics belonging to eight possible classes: hydrophobic, positive, negative, hydrogen acceptor, hydrogen donor, aromatic, sulfur, and neutral. These signatures have shown to be an effective and efficient method to model protein residue environment, its geometry and physicochemical properties, information that has been used to predict the effects of mutations on protein stability and affinity to its partners (Myung, Pires, & Ascher, 2020 ; Myung, Rodrigues, et al, 2020 ; Nguyen et al, 2021 ; Pires et al, 2014 , 2016 ; Pires & Ascher, 2016 , 2017 ; Rodrigues et al, 2019 , 2021a ; 2021b , 2024 ; Rodrigues & Ascher, 2022 , 2023 ; Ryu et al, 2023 ), pharmacodynamic and pharmacokinetics (Al‐Jarf et al, 2021 ; de Sa et al, 2022 ; Iftkhar et al, 2022 ; Morozov et al, 2023 ; Pires et al, 2015 , 2022 ; Pires & Ascher, 2020 ; Rodrigues et al, 2021c , 2022 ; Velloso et al, 2021 ), and identify drug resistance (Hawkey et al, 2018 ; Karmakar et al, 2018 , 2019 , 2020 ; Portelli et al, 2020 ; Portelli, Heaton, & Ascher, 2023 ; Zhan et al, 2021 ; Zhou et al, 2021 ) and disease mutations (Jessen‐Howard et al, 2023 ; Karmakar et al, 2022 ; Lai et al, 2021 ; Portelli et al, 2021 ; Portelli, Albanaz, et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Engineering G protein‐coupled receptors for stabilization

Velloso,

de Sá,

Pires

et al. 2024

Protein Science

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G protein‐coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time‐consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR‐tm, a novel and personalized structurally driven web‐based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR‐tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR‐tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10‐fold cross‐validation and blind test sets, respectively. We observed that the (structural) graph‐based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR‐tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR‐tm is available through a user‐friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.

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“…The proposed approach appears to be a crucial instrument for enhancing precision medicine initiatives as well as for accelerating the creation of inhibitors of the next generation that are less likely to acquire resistance. The authors have made the tool freely available on the web, giving the possibility to test it to the scientific community [ 69 ]. Similarly, Jie Su et al [ 70 ] applied AI to develop new therapeutic molecules against T315I resistance-related mutation and confirmed all the result via in vitro cultures.…”

Section: Ai For a Personalized Management Of The Therapy In Adult CMLmentioning

confidence: 99%

Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

Bernardi,

Vallati,

Gatta

2024

Cancers

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Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching “chronic myeloid leukemia” and “artificial intelligence”. The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the ‘human’ factor, which remains key in this domain.

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

Abstract: Graphical abstract

Cited by 12 publications

References 50 publications

Allosteric regulation of autoinhibition and activation of c-Abl

Allosteric regulation of autoinhibition and activation of c-Abl

Engineering G protein‐coupled receptors for stabilization

Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

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