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

artificial neural network




 Satarudra Prakash Singh1, 2 and Bhartendu Nath Mishra2, *





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; *Corresponding author


Article Type


Prediction Model




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



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.




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



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

Edited by


P. Kangueane








Biomedical Informatics





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.