Title

Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and

artificial neural network

Authors

 Satarudra Prakash Singh^{1, 2} and Bhartendu Nath Mishra^2, *

Affiliation

¹Amity Institute of Biotechnology, Amity University Uttar Pradesh, Gomti Nagar, Lucknow-226010, India; ²Department of Biotechnology, Institute of Engineering and Technology,  U.P. Technical University, Sitapur Road, Lucknow-226021, India;

Email

 profbnmishra@gmail.com; *Corresponding author

Article Type

Prediction Model

Date

received September 21, 2008; accepted November 05, 2008; published December 06, 2008

Abstract

The identification of MHC restricted epitopes is an important goal in peptide based vaccine and diagnostic development. As wet lab experiments for identification of MHC binding peptide are expensive and time consuming, in silico tools have been developed as fast alternatives, however with low performance. In the present study, we used IEDB training and blind validation datasets for the prediction of peptide binding to fourteen human MHC class I and II molecules using Gibbs motif sampler, weight matrix and artificial neural network methods. As compare to MHC class I predictor based on sequence weighting (Aroc=0.95 and CC=0.56) and artificial neural network (Aroc=0.73 and CC=0.25), MHC class II predictor based on Gibbs sampler did not perform well (Aroc=0.62 and CC=0.19). The predictive accuracy of Gibbs motif sampler in identifying the 9-mer cores of a binding peptide to DRB1 alleles are also limited (40%), however above the random prediction (14%). Therefore, the size of dataset (training and validation) and the correct identification of the binding core are the two main factors limiting the performance of MHC class-II binding peptide prediction. Overall, these data suggest that there is substantial room to improve the quality of the core predictions using novel approaches that capture distinct features of MHC-peptide interactions than the current approaches.

Keywords

MHC; weight matrix; ANN; Gibbs sampler; motif; epitope

Citation

Singh & Mishra, Bioinformation 3(4): 150-155 (2008)

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.