Title

Authors

Affiliation

¹Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal University, Karnataka, India; ²Mody Institute of Technology and Science University, Rajasthan, India

Email

smitha.sunil@manipal.edu; *Corresponding author

Article Type

Software

Date

Received September 21, 2012; Accepted October 01, 2012; Published October 13, 2012

We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of their amino acid indices. AmylPepPred provides a user friendly web interface for the researchers to easily observe the fibril forming and non-fibril forming hexmers in a given protein sequence. We expect that this stratagem will be highly encouraging in discovering fibril forming regions in proteins thereby benefit in finding therapeutic agents that specifically aim these sequences for the inhibition and cure of amyloid illnesses.

Availability

AmylPepPred is available freely for academic use at www.zoommicro.in/amylpeppred

Keywords

Amyloid fibrils, Bio-physio-chemical properties, Auto-correlation function, Support Vector Machine, AmylPepPred

Citation

Nair et al. Bioinformation 8(20): 994-995 (2012)

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.