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Review Article

Application of Computational Proteomics and Lipidomics in Drug Discovery

Nitish Kumar Mishra1,2*and Mamta Shukla3

1Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA

2University of Maryland Institute for Advanced Computer Studies (UMIACS), Center for Bioinformatics & Computational Biology (CBCB), University of Maryland, College Park, MD, USA

3Immunobilogy Division, CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India

*Corresponding Author:
Nitish Kumar Mishra
Department of Genetics, Cell Biology and Anatomy
University of Nebraska Medical Center
Omaha, NE, USA
E-mail: [email protected]

Received date: August 30, 2013; Accepted date: January 06, 2014; Published date: January 13, 2014

Citation: Mishra NK, Shukla M (2014) Application of Computational Proteomics and Lipidomics in Drug Discovery. J Theor Comput Sci 1:105. doi:10.4172/2376-130X.1000105

Copyright: © 2014 Mishra NK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


The process of drug discovery requires integration of biochemical and genetic tests to analyze the effects of drug molecules on biological systems. Comparative proteomic/lipidomic methods have identified a large number of differentially expressed novel proteins and lipids that can be used as prominent biomarkers for disease classification and drug resistance. Lipidomics or proteomics are not only used for target identification and deconvolution but also for analysis of off–targets and for studying the mode of action of drug molecules. In addition, they play significant roles in toxicity and preclinical trials at very early stages of drug development as well as in analysis of adverse effects of existing drug molecules. Since large-scale ‘omics’ data are now available in the public domain, bioinformatics and statistical analysis tools are needed to decipher knowledge from this vast amount of data. This review gives a brief overview of advancements in technological and computational methods in the area of lipidomics and proteomics based drug design.


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