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Identification of Kinase-substrate Pairs Using High Throughput Screening
Published on: August 29, 2015
Computational approaches for the identification and optimization of Src family kinases inhibitors
G Poli, A Martinelli, T Tuccinardi1
1Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy. tiziano.tuccinardi@farm.unipi.it.
Abstract:
Src family kinases (SFKs) are a group of non-receptor tyrosine kinases whose activity is involved in the regulation of cellular morphology, motility, proliferation and survival. An aberrant activation and expression of these kinases contribute to the pathogenesis and progression of a broad range of diseases, such as a large number of solid tumors, various hematological malignancies and some neuronal pathologies. The search for SFK inhibitors is therefore a promising research topic in medicinal chemistry. Computational studies such as receptor-based and/or ligand-based virtual screening, docking, and molecular modeling proved to be a powerful tool for identifying new SFKs inhibitors. In this review we report and analyze the main examples of computational approaches that allowed the identification of new SFKs ligands and the optimization of either activity and pharmacokinetic profile of lead compounds.
Insights
Src family kinases (SFKs) are crucial for cell functions, and their abnormal activity drives diseases like cancer. Computational methods are effective in discovering new SFK inhibitors for therapeutic development.
Area of Science:
- Biochemistry and Medicinal Chemistry
- Molecular Biology
- Computational Drug Discovery
Background:
- Src family kinases (SFKs) regulate critical cellular processes including morphology, motility, proliferation, and survival.
- Aberrant SFK activation is implicated in the pathogenesis of various cancers and neurological disorders.
- Targeting SFKs represents a promising strategy in medicinal chemistry for disease treatment.
Purpose of the Study:
- To review and analyze computational approaches for identifying novel Src family kinase (SFK) inhibitors.
- To highlight the role of computational methods in optimizing lead compounds for improved activity and pharmacokinetics.
Main Methods:
- Receptor-based virtual screening
- Ligand-based virtual screening
- Molecular docking
- Molecular modeling
Main Results:
- Computational studies have successfully identified new ligands targeting SFKs.
- These methods aid in optimizing the potency and pharmacokinetic properties of potential drug candidates.
- A range of computational strategies have proven effective in SFK inhibitor discovery.
Conclusions:
- Computational approaches are powerful tools for the identification and optimization of SFK inhibitors.
- This review consolidates key computational strategies applied to SFK drug discovery.
- Further research leveraging these methods holds significant promise for developing new therapeutics against SFK-related diseases.
