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Bioinformation. 2008; 3(4): 150–155. Published online 2008 December 6. | PMCID: PMC2639663 |
Copyright © 2007 Biomedical Informatics Publishing Group 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; Received September 21, 2008; Accepted November 5, 2008. This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium,
for non-commercial purposes, provided the original author and source are credited.
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 ANN - artificial neural network, MHC - major histocompatibility complex, Aroc - area under receiver operating characteristic,
CC - correlation coefficient, IEDB - immune epitope database. A major task of the immune system is to identify cells that have been infected by pathogens and discriminate them from healthy cells.
This is realized by the MHC class-I and II antigen processing and presentation pathway and the duty is assigned to helper T-lymphocytes
(HTL) and cytotoxic T-lymphocytes (CTL). The activation of CD8+ cytotoxic T-cells in the immune system requires presentation of
endogenous antigenic peptides by MHC class-I molecules [ 1]. The activation of
CD4+ helper T-cells is also essential for the development of adaptive immunity against pathogens. A critical step in CD4+ T cell
activation is the recognition of exogeneous peptides presented by MHC class-II molecules [ 2].
The peptides bound to MHC molecules that trigger an immune response are referred as T-cell epitopes. Identifying T-cells epitopes is of
high importance to immunologists, because it allows the development of diagnostics, peptide based vaccine and immunotherapy
[ 3]. Therefore, the computational prediction of MHC class-I and II binding
epitopes is of immense importance as their experimental identification is costly and time consuming
[ 4, 5]. A number of prediction methods for MHC binding peptides have been developed using peptide binding data from different databases such
as SYFPEITHI [ 6], MHCBN [ 7],
AntiJen [ 8] and IEDB [ 9].
The first method was based on the identification of allele-specific anchor residues [ 10].
This simple motif-based method was later replaced by various weight matrix-based methods [ 11,
12]. Similarly, other methods were based on scoring matrices derived from multiple
peptide alignments such as RANKPEP [ 13] and the contribution of different
residues in a peptide binding based on quantitative binding data such as ARB [ 14]
and SMM-align [ 15]. The accumulation of more epitope data resulted into the
development of different types of machine learning algorithms for prediction, including support vector machines
[ 16] and artificial neural networks [ 17].
The other methods are also available based on structural template information for the prediction of MHC binding peptides
[ 18, 19]. In order to assess the
current state of the MHC class-I and II binding peptide predictions, a number of research groups have established a systematic and
quantitative benchmarks [ 20, 21]. Despite, the large number of available computational methods, prediction of MHC class-I and II restricted epitopes remains a
challenging problem even today. An essential step in developing accurate prediction tool is to gather a set of experimentally consistent
training and validation dataset. In present study, we compiled a large IEDB dataset for training and blind datasets for validation to
the fourteen MHC class- I and II molecules (seven for each class) that were experimentally determined under uniform conditions collected
from Dana-Farber Repository ( http://bio.dfci.harvard.edu/DFRMLI/).
The computational methods like, Gibbs motif sampler [ 22], sequence weighting
schemes [ 23, 24] and feed-forward
backpropagation ANN [ 25] were used to predict the peptide binding to MHC
molecules. The Gibbs sampler and weight matrix approaches are well suited to describe sequence motifs of fixed length. For MHC class I
and II, the peptide binding motif is in most situations assumed to be of a fixed length of 9 amino acids. The weight-matrix approach is
only suitable for prediction of a binding event in situations where the binding specificity can be represented independently at each
position in the motif and this assumption can only be considered to be an approximation. In the binding of a peptide to the MHC molecule
the amino acids might for instance compete for the space available in the binding grove. The neural networks with a hidden layer are
designed to describe sequence patterns with such a higher order correlations. The superiority of these sequence based approaches to the
structure are believed to be the consequence of two main features, i.e. the flexibility in optimizing the Gibbs motif sampling parameter
and sequence weighting schemes and also in optimizing the ANN training parameter according to the dataset. Finally, the developed
prediction models would predict the HTL and CTL epitopes, which shall provide better insight into further research of peptide based
vaccine and diagnostics against diseases ranging from malaria to cancer. Data collection We assembled dataset of peptide binding and nonbinding affinities for fourteen MHC class-I and II molecules (seven for each class)
from DRFMLI repository ( http://bio.dfci.harvard.edu/DFRMLI/
). These dataset of high quality MHC binding and nonbinding peptides were taken from IEDB database
[ 9] (Table 1 and 2,
see supplementary material). The binding affinities (IC 50) of these peptides were
quantitatively measured by immunological experiments. They were then scaled to binding scores ranging from 0 to 100 using linear
transformation [ 21], where score >=99 are strong binders, 90-98 are moderate
binders, 33-89 are border cases and <33 are non-binders. These dataset were used as the training data to develop computational models
based on Gibbs sampler, sequence weighting and ANN to predict MHC binding peptides. Three sets of validation data were used to evaluate the prediction performance of MHC class-I binding peptides. First, referred as
survivin dataset derived from a full overlapping study of 134 nonamer peptides spanning the full length of the tumor antigen survivin,
second, CMV dataset contains 42 peptides spanning a 50 amino acids long construct containing cytomegalovirus (CMV) internal matrix
protein pp65 peptides and third, combination of survivin and CMV dataset referred as combined-I dataset contains 176 peptides for each
seven human MHC class-I molecules. One hundred three binding and nonbinding peptides were derived from four protein antigens, i.e. bee
venom allergen, LAGE-1, dog allergen Can f1 and Nef protein for each seven MHC class-II molecules, referred as combined-II dataset for
the validation of predictions. The original binding scores were measured by iTopia TM Epitope Discovery System and then scaled to scores
ranging from 0 to100 (Table 1 and 2, see supplementary material). In an attempt
to check the ability of Gibbs sampler method to predict the 9-mer peptide cores revealed in crystal structures
of MHC-peptide complexes, a total of 10 structures were compiled from Protein Data Bank for DRB1 alleles
(Table 3 in supplementary material). Algorithms used for the prediction of MHC binding peptides Gibbs motif sampler MHC class-II binding peptides have a broad length distribution complicating the development of prediction methods. Identifying the
correct alignment of a set of peptides known to bind the MHC class-II molecule using Gibbs motif sampler is a crucial part of the
algorithm to identify the core of an MHC class-II binding peptide [ 22]. Here, we
used the default Gibbs sampling parameters to find the 9-mer motif in a set of MHC class-II binding peptide data using the web-server
EasyGibbs available at http://www.cbs.dtu.dk/biotools/EasyGibbs/
Sequence weighting Three different sequence weighting methods i.e. Henikoff and Henikoff 1/nr [ 23],
clustering at 62¢ identity [ 24] and no clustering are available, which can be
used to weight 9-mer peptide sequences. The Henikoff method is fast as the computation time only increases linearly with the number of
sequences, whereas in the Hobohm clustering algorithm, computation time increases as the square of the number of sequences. Here, we
used the web-server EasyPred available at
http://www.cbs.dtu.dk/biotools/EasyPred/ to generated the weight matrix for the prediction of MHC binding peptides by
applying all three sequence weighting schemes with weight on pseudo counts is 200. Artificial neural network Here, we used a conventional feed-forward neural network [ 25] with an input
layer (180 neurons), one hidden layer (2 neurons) and a single neuron output layer using the web server Easypred available
at http://www.cbs.dtu.dk/biotools/EasyPred/.
The default setting parameters (one bin for balanced training, running upto 300 training epochs and top 80¢ of the training set)
were used to train the neural network. Evaluation parameters Based on these datasets and algorithms, we have developed computational models which could predict the binding affinity between MHC
molecules and peptides. The efficiency of algorithms was determined by discrimination between binders and nonbinders. A predicted
peptide belongs to one of the four categories, i.e. True Positive (TP); an experimentally binding peptide predicted as a binder, False
Positive (FP); an experimentally nonbinding peptide predicted as a binder, True Negative (TN); an experimentally nonbinding peptide
predicted as a nonbinder and False Negative (FN); an experimentally binding peptide predicted as nonbinder. Here, we used non-parametric
performance measures, area under receiver operator characteristic (Aroc) curve and Pearson correlation coefficient (CC) to evaluate the
predictive performance of the applied algorithms. The ROC curve is a plot of the true positive rate TP/(TP+FN) on the vertical axis vs
false positive rate FP/(TN+FP) on the horizontal axis for the complete range of the decision thresholds and the Pearson correlation
coefficient (CC) is used to measure the association between pairs of values i.e. predicted and experimental
[ 26]. We assembled a dataset of peptide binding and nonbinding affinities for fourteen MHC class-I and II molecules (seven for each class)
from DRFMLI repository (
http://bio.dfci.harvard.edu/DFRMLI/). The Table 1 and 2 (shown under supplementary material)
gives an overview of the training and validation dataset, encompassing a total of 16,771 peptide
training data determined experimentally including 10,303 MHC class-I and 6,468 MHC class-II binding affinities
[ 21]. Compared to the training datasets publicly available on the IEDB database
[ 9], our evaluation dataset expands the number of measured peptide-MHC
interactions, 1,232 for MHC class-I from Survivin and CMV whereas, 712 for MHC class-II molecules from four protein antigens bee venom
allergen, LAGE-1, dog allergen Can f1 and Nef protein. As the validation dataset not included in IEDB database, it is equivalent to a
blind test. From the experimental data, peptides were classified into binders (IC 50<1000 nM) and nonbinders
(IC 50>=1000 nM) based
on measured affinities. From these dataset, the performance of the prediction methods were then measured by area under ROC curves
(Aroc) and Pearson correlation coefficient (CC). The calculation of Aroc provides a highly useful measure of prediction quality, which
is 0.5 for random predictions and 1.0 for perfect predictions and correlation coefficient value of one corresponds to a perfect
correlation, a value of zero corresponds to a random prediction and a value of minus one to a perfect anti-correlation. The prediction performances of the weight matrix are better than the non-linear predictor (ANN) for the MHC class-I molecules using
all the validation datasets measured in terms of Aroc and CC (). The weight matrix
performance in term of Aroc and CC is maximum (1.0, 0.69) using CMV validation dataset and the minimum (0.85, 0.51) using combined-I
validation dataset for allele HLA-A*1101 (). The prediction performance of the Gibbs
motif sampler for the DRB1*1301(MHC class-II) molecule is maximum (Aroc=0.71, CC=0.32) using Henikoff and Henikoff 1/nr weighting scheme
() which is lower than the minimum performance of MHC class I binding prediction
(). The average performance of MHC class-II predictor based on Gibbs sampler (Aroc=0.62,
CC=0.19) is also lower than the MHC class-I predictor based on sequence weighting (Aroc=0.95, CC=0.56) and artificial neural network
(Aroc=0.73, CC=0.25). From the above results it is clear that the size of training dataset may be an important factor contributing to better performance
of sequence weighting and artificial neural network methods for the prediction of MHC class-I binding peptides. A key difference between
MHC class-I and MHC class-II molecule is that the binding groove of class-II molecules is open at both ends. As a result, the length of
peptide binding to class-II molecules can vary considerably, typically ranges 13-25 amino acids long. Therefore, a requisite for all
MHC class-II binding prediction approaches is the capacity to identify the correct 9-mer core residues within longer peptide sequences
that mediate the binding interaction. For Gibbs sampler method, we compared the predicted cores with the true cores extracted from
crystal structures (Table 3 under supplementary material). Gibbs sampler methods had limited
success (40¢) as shown in the Table 4 (see supplementary material), although they still perform
above random prediction (the probability to randomly guess the right core for a 15-mer peptide is 1 out of 7 or 14¢). Thus the
Gibbs sampler and weight matrix approaches are only suited to describe sequence motifs of fixed length e.g. 9-mer amino acids and
suitable for prediction of a binding event in situations, where the binding specificity can be represented independently at each
position in the motif This assumption can only be considered to be an approximation and in the binding of a peptide to the MHC molecule
the amino acids might for instance compete for the space available in the binding grove where artificial neural networks with a hidden
layer are generally used to describe sequence patterns. Overall, these data suggest that there is a substantial room to improve the
quality of the core predictions using novel approaches that capture distinct features of MHC-peptide interactions. As, the identification of MHC class-I and II restricted epitopes using wet lab experiments are expensive and time consuming,
the computational methods can be used as a fast alternatives. Although the prediction of peptide that bind to MHC class-II did not
perform well as the MHC class-I molecules, however, it is able to identify the 9-mer cores of a binding peptide with limited accuracy
(40¢) 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 binding prediction and thus, there is a
substantial room to improve the quality of the core predictions. Finally, we hope that novel approaches that capture distinct features
of MHC class-I and II peptide interactions could lead to more useful predictions than the current approaches. We are grateful to Mr. Akhilesh Singh, Amity Institute of Biotechnology, Amity University, Lucknow campus for their critical
reading of the manuscript and valuable suggestions.We are also thankful to U.P Technical University, Lucknow and Amity University
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