Title

Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties

Authors

Amit Kumar Banerjee, Sunita M., Naveen M, U. S. N. Murty^*

Affiliation

Bioinformatics Group, Biology Division, Indian Institute of Chemical Technology, Hyderabad-500607, A.P, India.

Email

murty_usn@yahoo.com

Article Type

Hypothesis

Date

Received January 12, 2010; revised March 02, 2010; accepted April 09, 2009; published April 30, 2010

Abstract

Biological systems are highly organized and enormously coordinated maintaining greater complexity. The increment of secondary data generation and progress of modern mining techniques provided us an opportunity to discover hidden intra and inter relations among these non linear dataset. This will help in understanding the complex biological phenomenon with greater efficiency. In this paper we report comparative classification of Pyruvate Dehydrogenase protein sequences from bacterial sources based on 28 different physicochemical parameters (such as bulkiness, hydrophobicity, total positively and negatively charged residues, α helices, β strand etc.) and 20 type amino acid compositions. Logistic, MLP (Multi Layer Perceptron), SMO (Sequential Minimal Optimization), RBFN (Radial Basis Function Network) and SL (simple logistic) methods were compared in this study. MLP was found to be the best method with maximum average accuracy of 88.20%. Same dataset was subjected for clustering using 2*2 grid of a two dimensional SOM (Self Organizing Maps). Clustering analysis revealed the proximity of the unannotated sequences with the Mycobacterium and Synechococcus genus.

Keywords

Pyruvate Dehydrogenase, Data Mining, Clustering, KNIME, Self Organizing Maps (SOM).

Citation

Banerjee et al., Bioinformation 00(10): 000 (2010)

Edited by

P.Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.