alexa In silico Discovery of Novel Protein Domains in Strepto
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

In silico Discovery of Novel Protein Domains in Streptococcus mutans

Anand Ramesh Varsale1, Amol Shriram Wadnerkar1, Rajendra Haribhau Mandage1* and Priyanka Khasherao Jadhavrao2

1Dept of Bioinformatics, Centre for Advanced Life Sciences, Deogiri College, Aurangabad 431005, M.S. India

2Dept of Microbiology, Abeda Inamdar College, Pune 411001, M.S. India

*Corresponding Author:
Rajendra Haribhau Mandage
Dept of Bioinformatics
Centre for Advanced Life Sciences
Deogiri college, station road, Aurangabad
431005, M.S. India
Tel: +91 9561585950
E-mail: [email protected]

Received Date: August 07, 2010; Accepted Date: August 29, 2010; Published Date: August 29, 2010

Citation: Varsale AR, Wadnerkar AS, Mandage RH, Jadhavrao PK (2010) In silico Discovery of Novel Protein Domains in Streptococcus mutans. J Proteomics Bioinform 3: 253-259. doi: 10.4172/jpb.1000148

Copyright: © 2010 Varsale AR, 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.



Streptococcus mutans is the principal etiological agent of dental caries worldwide and is considered to be the most cariogenic of all of the oral streptococci. We sought to expand our understanding of this organism at the molecular level through identifi cation and function prediction of novel protein domains by two automated approaches using HMM and BLAST to develop a complete set of domains, which were then subsequently manually analysed. A final set of 19 novel domains and families was identifi ed. This enhances our understanding of both S mutans and also general bacterial molecular mechanisms, including protein synthesis to catalytic action. Furthermore we demonstrate that using this type of in silico method it is possible to fairly rapid generation of new biological information from previously uncorrelated data to increase the rate of discoveries in the laboratory.


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