Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking.
Philipe de Olveira FernandesDiego Magno MartinsAline de Souza BozziJoão Paulo A MartinsAdolfo Henrique de MoraesVinícius Gonçalves MaltarolloPublished in: Molecular diversity (2021)
Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models-decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure-activity relationship model (SAR).
Keyphrases
- tyrosine kinase
- molecular docking
- epidermal growth factor receptor
- machine learning
- molecular dynamics simulations
- chronic myeloid leukemia
- prostate cancer
- structure activity relationship
- squamous cell carcinoma
- signaling pathway
- emergency department
- magnetic resonance imaging
- molecular dynamics
- computed tomography
- quality improvement
- decision making
- single molecule
- drug induced
- childhood cancer