Title

Authors

Affiliation

¹Center for Development of Advanced Computing, Pune, India; ²Rajiv Gandhi Institute of Information Technology and Biotechnology, Bharati Vidyapeeth Deemed University, Pune, India; ³Center for Informatics, Shiv Nadar University, Dadri, UP, India

Email

valadi@gmail.com; *Corresponding author

Article Type

Hypothesis

Date

Received December 16, 2012; Accepted April 05, 2013; Published May 25, 2013

Penicillin-Binding Proteins are peptidases that play an important role in cell-wall biogenesis in bacteria and thus maintaining bacterial infections. A wide class of β-lactam drugs are known to act on these proteins and inhibit bacterial infections by disrupting the cell-wall biogenesis pathway. Penicillin-Binding proteins have recently gained importance with the increase in the number of multi-drug resistant bacteria. In this work, we have collected a dataset of over 700 Penicillin-Binding and non-Penicillin Binding Proteins and extracted various sequence-related features. We then created models to classify the proteins into Penicillin-Binding and non-binding using supervised machine learning algorithms such as Support Vector Machines and Random Forest. We obtain a good classification performance for both the models using both the methods.

Keywords

Penicillin-Binding Proteins, Support Vector Machines, Random Forest, Protein Classification.

Citation

Nair et al.  Bioinformation 9(9): 481-484 (2013)

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.