Structural Models for the Design of PKMzeta Inhibitors with Neurobiological Indications
| Autor: | Priyanka Purkayastha, Aruna Malapati, Perumal Yogeeswari, Reshma Alokam, Dharmarajan Sriram |
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| Rok vydání: | 2015 |
| Předmět: |
Models
Molecular Virtual screening Drug discovery Kinase Organic Chemistry Long-term potentiation Biology Pharmacology Bioinformatics Computer Science Applications chemistry.chemical_compound chemistry Structural Biology Docking (molecular) Drug Discovery Humans Molecular Medicine Homology modeling Nervous System Diseases Growth inhibition Protein Kinase Inhibitors Protein Kinase C Protein kinase C |
| Zdroj: | Molecular Informatics. 34:665-678 |
| ISSN: | 1868-1743 |
| Popis: | An atypical protein kinase C, PKMzeta has become an attractive target for various neurological disorders including long term potentiation, cognition, neuropathic pain and cancer. Drug discovery efforts have been hindered due to the non-availability of the protein structure and hence in the present study we attempted to build the open and closed models of the protein PKMzeta using homology modeling. The models were then used to identify PKMzeta inhibitors utilizing a high-throughput virtual screening protocol from a large commercial chemical database. Compounds were selected based on the binding interactions and Glide score. Compounds were then subjected to in vitro luminescent based kinase assay for their inhibitory activity on targeted protein. Seven compounds exhibited IC50 s less than or equal to 10 µM. Cell based assays revealed that Lead C3 and Lead C6 exhibited selectivity towards methylmercury treated neuroblastoma growth inhibition and suppressed reactive oxygen species with IC50 s of 0.89 and 0.17 µM, respectively. Furthermore, Lead C3 exhibited attenuation of proinflammatory response with least energy in dynamic simulation studies and thus emerged as a prototypical lead for further development as novel inhibitor of PKMzeta for neurological implications. |
| Databáze: | OpenAIRE |
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