Title

Prediction of MHC class I binding peptides using probability distribution functions

Authors

S. S. Soam¹, Feroz Khan², B. Bhasker³, B. N. Mishra¹*

Affiliation

¹Institute of Engineering & Technology, (A Constituent College of Uttar Pradesh Technical University, Lucknow) Lucknow, India; ²Bioinformatics & In Silico Biology Division, Central Institute of Medicinal & Aromatic Plants (CSIR), Lucknow, India, ³ Indian Institute of Management, Prabandh Nagar, Lucknow India

Email

profbmishra@rediffmail.com

Article Type

Hypothesis

Date

received January 19, 2009; revised March 31, 2009; accepted April 19, 2009; published June 28, 2009

Abstract

Binding of peptides to specific Major Histo-compatibility Complex (MHC) molecule is important for understanding immunity and has applications to vaccine discovery and design of immunotherapy. Artificial neural networks (ANN) are widely used by predictions tools to classify the peptides as binders or non-binders (BNB). However, the number of known binders to a specific MHC molecule is limited in many cases, which poses a computational challenge for prediction of BNB and hence, needs improvement in learning of ANN. Here, we describe, the application of probability distribution functions to initialize the weights and biases of the artificial neural network in order to predict HLA-A*0201 binders and non-binders. The 10-fold cross validation has been used to validate the results. It is evident from the results that the AROC for 90% of test cases for Weibull, Uniform and Rayleigh distributions is in the range 0.90-1.0. Further, the standard deviation for AROC was minimum for Weibull distribution, and may be used to train the artificial neural network for HLA-A*0201 MHC Class-I binders and non-binders prediction.

Keywords

T-cell Epitope, ANN, Probability distribution, MHC binder/non-binder.

Citation

Soam et al, Bioinformation 3(9): 403-408 (2009)

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.