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Research Journal of Pharmacy and Technology
Year : 2017, Volume : 10, Issue : 6
First page : ( 1708) Last page : ( 1716)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2017.00302.X

Computational analysis to identify the drug targets and their lead molecules in pancreatic cancer

Nisha H., Karavadi Balasankar*,  Aswathy

Department of Bioinformatics, School of Bio-Chemical Engineering, Sathyabama University, Chennai-600119, India.

*Corresponding Author E-mail: balasankar.bioinfo@sathyabamauniversity.ac.in

Online published on 22 September, 2017.


Pancreatic cancer is the leading cause of cancer related deaths. Metastasis and drug resistance are the major causes of mortality in patients with pancreatic cancer. There are several genes which are responsible for causing pancreatic cancer; they have the ability to enhance the tumor growth. The 96 protein coding genes which are targets for cancer-causing mutations in pancreatic cancers are considered. Some of the effective proteins were identified and the structures are determined using homology modeling. In this study we performed the homology modeling studies of the Activin receptor type 1 B(ACVR1B) and Transmembrane protease serine 4(TMPRSS4) proteins which are protein coding genes and validated the nature of the receptor as a future drug target for pancreatic cancer treatment and diagnosis. We have also identified specific ligands for the above mentioned protein by using the effective structure based ligand screening approach. Protein ligand complexes have been analyzed by docking studies using Autodock vina and interactions have also been visualized. Finally, the best drugs i.e. the ligands or small molecules are validated for ADMET and Physiochemical properties.



ACVR1B, TMPRSS4, Modeling, Docking, ADMET.


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