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Research Journal of Pharmacy and Technology
Year : 2015, Volume : 8, Issue : 12
First page : ( 1679) Last page : ( 1684)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2015.00303.0

Exploring beta-tubulin-protein interactome using computational techniques

Nandan B. Lokapriya, Sequeira Velin Marita, Verma Kanika, Ramanathan K.*

Industrial Biotechnology Division, School of Bio Sciences and Technology, VIT University, Vellore-632014, Tamil Nadu, India

*Corresponding Author E-mail: kramanathan@vit.ac.in

Online published on 1 January, 2016.

Abstract

The control of cell division is a cardinal issue in the field of cancer therapy. Indeed, beta-tubulin is an essential protein in cell division process. Therefore it is the prime target of many cancer types such as breast cancer, ovarian cancer, lung cancer etc. Paclitaxel is one of the most widely used anti-cancer agents for the inhibition of beta tubulin function. Although treatment with paclitaxel often works well initially, many patients develop resistance after chronic exposure to an anti-cancer agent. Of note, hampering the activity of key proteins which interact with beta-tubulin will consequently hinder the rapid cell division during tumor formation. Keeping this in mind, the STRING tool was employed in the present investigation to derive a large network of genome level protein-protein interaction with proteins as nodes and interactions as edges. A computational approach to predict such interactions is recommended as it integrates information from several sources to generate a detailed interactome. Subsequently, the proteins obtained were divided into different groups based on their functional role by k-means clustering algorithm. The grouping into clusters assisted in picking out proteins that had regulatory roles and had connections with cancer pathways as well. We certainly hope that these proteins can be shortlisted as alternative targets to beta-tubulin in cancer chemotherapy.

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Keywords

Beta-tubulin, Interactome, STRING, Clustering.

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