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Updated: Oct 31, 2025
Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
Published on: October 23, 2019
Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking
Philipe Oliveira Fernandes1, Diego Magno Martins2, Aline de Souza Bozzi2
1Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
Abstract:
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).
Insights
Researchers identified key structural features of compounds that activate Abelson kinase (c-Abl). This discovery advances drug development for diseases like cancer by understanding c-Abl activator binding to the myristoyl pocket.
Area of Science:
- Biochemistry and Molecular Biology
- Medicinal Chemistry
- Computational Drug Discovery
Background:
- Abelson kinase (c-Abl) is a non-receptor tyrosine kinase crucial for cellular processes like differentiation, migration, proliferation, and survival.
- Activating c-Abl presents a therapeutic strategy for conditions including chemotherapy-induced neutropenia, prostate cancer, and breast cancer.
- Recent identification of c-Abl activators offers new avenues for drug development.
Purpose of the Study:
- To characterize the critical chemical properties and interactions of identified c-Abl activators.
- To understand the structural features necessary for c-Abl activators to bind to the myristoyl pocket.
- To develop predictive structure-activity relationship (SAR) models for c-Abl activators.
Main Methods:
- Integrated structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies.
- Employed molecular docking simulations to predict binding interactions.
- Utilized tree-based machine learning models (decision tree, AdaBoost, random forest) for analysis.
Main Results:
- Developed predictive and robust machine learning models with Matthews correlation coefficient values > 0.4.
- Identified key structural characteristics of c-Abl activators responsible for binding to the myristoyl pocket.
- Established a structure-activity relationship (SAR) model based on these identified characteristics.
Conclusions:
- The study successfully combined SBDD and LBDD with machine learning to elucidate c-Abl activator mechanisms.
- The identified structural features and SAR model provide a foundation for designing novel c-Abl activating drugs.
- This work facilitates the development of targeted therapies for diseases involving c-Abl dysregulation.
