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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
A computational framework for the design and development of ERK2 and multi-target ERK2/HDAC inhibitors
Alen Cebzan1, Dusan Ruzic1, Nemanja Djokovic1
1Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, Belgrade 11000, Serbia.
Abstract:
The Ras-Raf-MEK-ERK/MAPK signaling pathway regulates important cellular processes such as proliferation, differentiation, apoptosis, development and stress responses. This signaling pathway is overexpressed in various types of cancer, with ERK2 (Extracellular signal - Regulated Kinase 2) serving as the final effector component. Current inhibitors of this pathway, including ERK2 inhibitors, face challenges such as safety concerns, limited efficacy and resistance, highlighting the need for new therapeutic candidates. In this study, ML-QSAR (Machine Learning - Quantitative Structure-Activity Relationships) and 3D-QSAR (Three-Dimensional Quantitative Structure-Activity Relationships) models and molecular docking of the investigated compounds were used to identify key structural features related to ERK2 inhibitory activity. This computational framework formed the basis for a fragment-based drug design that enabled the development and evaluation of new ERK2 inhibitors from two distinct structural classes, pyrimidine-2-pyridone and pyrazolyl-pyrrole derivatives. Molecular dynamics simulations were performed for the best designed ERK2 inhibitor. The developed computational framework was successfully used for the design and evaluation of multi-target ERK2/HDAC inhibitors as a basis for future cancer therapies.
Insights
New computational methods identified key structural features for developing novel ERK2 inhibitors. This research paves the way for improved cancer therapies targeting the MAPK pathway.
Area of Science:
- Oncology
- Medicinal Chemistry
- Computational Biology
Background:
- The Ras-Raf-MEK-ERK/MAPK pathway is crucial for cellular functions and often dysregulated in cancer.
- ERK2 (Extracellular signal-Regulated Kinase 2) is a key effector in this pathway, making it a target for cancer therapy.
- Existing ERK2 inhibitors have limitations including safety issues, efficacy, and resistance, necessitating novel drug development.
Purpose of the Study:
- To identify critical structural features for ERK2 inhibitory activity using computational approaches.
- To design and evaluate novel ERK2 inhibitors based on fragment-based drug design.
- To explore the potential of multi-target ERK2/HDAC inhibitors for cancer treatment.
Main Methods:
- Utilized Machine Learning-Quantitative Structure-Activity Relationships (ML-QSAR) and 3D-QSAR models.
- Employed molecular docking to analyze compound-protein interactions.
- Performed fragment-based drug design and molecular dynamics simulations for lead optimization.
Main Results:
- Identified key structural determinants for ERK2 inhibition.
- Successfully designed and evaluated new ERK2 inhibitors belonging to pyrimidine-2-pyridone and pyrazolyl-pyrrole classes.
- Demonstrated the efficacy of the computational framework in designing multi-target ERK2/HDAC inhibitors.
Conclusions:
- The developed computational framework is effective for designing novel ERK2 inhibitors.
- The identified pyrimidine-2-pyridone and pyrazolyl-pyrrole derivatives show promise as potential cancer therapeutics.
- This study provides a foundation for developing advanced multi-target inhibitors for future cancer therapies.
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