Specific Major Histocompatibility Complex Molecule Research Articles

Improved pan‐specific MHC class I peptide‐binding predictions using a novel representation of the MHC‐binding cleft environment

Major histocompatibility complex (MHC) molecules play a key role in cell-mediated immune responses presenting bounded peptides for recognition by the immune system cells. Several in silico methods have been developed to predict the binding affinity of a given peptide to a specific MHC molecule. One of the current state-of-the-art methods for MHC class I is NetMHCpan, which has a core ingredient for the representation of the MHC class I molecule using a pseudo-sequence representation of the binding cleft amino acid environment. New and large MHC-peptide-binding data sets are constantly being made available, and also new structures of MHC class I molecules with a bound peptide have been published. In order to test if the NetMHCpan method can be improved by integrating this novel information, we created new pseudo-sequence definitions for the MHC-binding cleft environment from sequence and structural analyses of different MHC data sets including human leukocyte antigen (HLA), non-human primates (chimpanzee, macaque and gorilla) and other animal alleles (cattle, mouse and swine). From these constructs, we showed that by focusing on MHC sequence positions found to be polymorphic across the MHC molecules used to train the method, the NetMHCpan method achieved a significant increase in the predictive performance, in particular, of non-human MHCs. This study hence showed that an improved performance of MHC-binding methods can be achieved not only by the accumulation of more MHC-peptide-binding data but also by a refined definition of the MHC-binding environment including information from non-human species.

Major Histocompatibility Complex Class II Prediction

Major Histocompatibility complex (MHC) molecules play an essential role in introducing and regulation immune system. The MHC molecules are divided into two classes, class I and class II which are differ in size of their binding pockets. Determining which peptides bind to a specific MHC molecule is fundamental to understanding the basis of immunity, and for the development of vaccines and immunotherapeutic for autoimmune diseases and cancer. Due to the variability of the locations of the class II binding cores, the process for predicting the affinity of these peptides is difficult.This paper investigates a new method for predicting peptides binding to MHC class II molecules and its affinity using genetic algorithms and metaheuristics. The algorithm is based on a fitness function that builds a scoring matrix for all suggested motifs in a specific iteration to test the motif ability to be one of the real motifs in the nature. The genetic algorithmpresented here shows increased prediction accuracy with higher number of true positives and negatives on almost of MHC class II alleles,about 80 percent of peptides were correctly classified when testing dataset from IEDB[26]. Generally, these results indicate that GA has a strong ability for MHC Class II binding prediction.

Prediction of MHC class I binding peptides using probability distribution functions

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 A(ROC) 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.

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms.

BackgroundPeptides binding to Major Histocompatibility Complex (MHC) class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides.In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA) for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs.ResultsThe proposed methods are intended for finding peptides binding to MHC class II I-Ag7 molecule – a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-Ag7 datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1) an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2) quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules.ConclusionWe present two MOEA-based algorithms for finding motifs, one for self-discovery and the other for guided-discovery by experimentally determined motifs, and thereby predicting binding peptides to I-Ag7 molecule. Our experiments show that the proposed MOEA-based algorithms are better than earlier methods in predicting binding sites not only on I-Ag7 but also on most alleles of class II MHC benchmark datasets. This shows that our methods could be applicable to find binding motifs in a wide range of alleles.

Prediction of MHC II-binding peptides using rough set-based rule sets ensemble

Peptide binding to Major Histocompatibility Complex (MHC) is a prerequisite for any T cell-mediated immune response. Predicting which peptides can bind to a specific MHC molecule is indispensable to minimizing the number of peptides required to synthesize, to the development of vaccines and immunotherapy of cancer, and to aiding to understand the specificity of T-cell mediated immunity. At present, although predictions based on machine learning methods have good prediction performance, they cannot acquire understandable knowledge and prediction performance can be further improved. Thereupon, the Rule Sets ENsemble (RSEN) algorithm, which takes advantage of diverse attribute and attribute value reduction algorithms based on rough set (RS) theory, is proposed as the initial trial to acquire understandable rules along with enhancement of prediction performance. Finally, the RSEN is applied to predict the peptides that bind to HLA-DR4(B1* 0401). Experimentation results show: (1) prepositional rules for predicting the peptides that bind to HLA-DR4 (B1* 0401) are obtained; (2) compared with individual RS-based algorithms, the RSEN has a significant decrease (13%---38%) in prediction error rate; (3) compared with the Back-Propagation Neural Networks (BPNN), prediction error rate of the RSEN decreases by 4%---16%. The acquired rules have been applied to help experts make molecules modeling.

Prediction of MHC class I binding peptides using profile motifs

Peptides that bind to a given major histocompatibility complex (MHC) molecule share sequence similarity. Therefore, a position specific scoring matrix (PSSM) or profile derived from a set of peptides known to bind to a specific MHC molecule would be a suitable predictor of whether other peptides might bind, thus anticipating possible T-cell epitopes within a protein. In this approach, the binding potential of any peptide sequence (query) to a given MHC molecule is linked to its similarity to a group of aligned peptides known to bind to that MHC, and can be obtained by comparing the query to the PSSM. This article describes the derivation of alignments and profiles from a collection of peptides known to bind a specific MHC, compatible with the structural and molecular basis of the peptide-MHC class I (MHCI) interaction. Moreover, in order to apply these profiles to the prediction of peptide-MHCI binding, we have developed a new search algorithm (RANKPEP) that ranks all possible peptides from an input protein using the PSSM coefficients. The predictive power of the method was evaluated by running RANKPEP on proteins known to bear MHCI K b- and D b-restricted T-cell epitopes. Analysis of the results indicates that > 80% of these epitopes are among the top 2% of scoring peptides. Prediction of peptide-MHC binding using a variety of MHCI-specific PSSMs is available on line at our RANKPEP web server (www.mifoundation.org/Tools/rankpep.html). In addition, the RANKPEP server also allows the user to enter additional profiles, making the server a powerful and versatile computational biology benchmark for the prediction of peptide-MHC binding.

Peptide binding by class I and class II MHC molecules.

Major histocompatibility complex (MHC) antigens bind peptides of diverse sequences with high affinity. They do this in order to generate maximal immunological protection by covering the spectrum of peptides that may be seen by a host over the course of its lifetime. However, in many circumstances the immune system does not recognize a particular peptide that it should for maximum advantage over the pathogen. In other situations, the immune system goes awry and incorrectly recognizes a self-peptide that it should not. This results in disease characterized by recognition and attack of self. Rheumatoid arthritis is an example of just such a disease. In either of these situations, peptide-based modalities for immune therapy would be an advantage. However, peptide-based therapies require a thorough understanding of the forces involved in peptide binding. Great strides have been made in elucidating the mechanisms by which these MHC proteins may bind peptides with diverse sequences and high affinity. This review summarizes the current data obtained from crystallographic analyses of peptide binding for both class I and class II MHC molecules. Unfortunately, as yet these data have not allowed us to predict which peptides will bind with high affinity to a specific MHC molecule.

Ranking potential binding peptides to MHC molecules by a computational threading approach

In this paper, an approach developed to address the inverse protein folding problem is applied to prediction of potential binding peptides to a specific major histocompatibility complex (MHC) molecule. Overlapping peptides, spanning the entire protein sequence, are threaded through the backbone coordinates of a known peptide fold in the MHC groove, and their interaction energies are evaluated using statistical pairwise contact potentials. With currently available tables for pairwise potentials, promising results are obtained for MHC-peptide complexes where hydrophobic interactions predominate. By ranking the peptides in an ascending order according to their energy values, it is demonstrated that, in most cases, known antigenic peptides are highly ranked. Furthermore, predicted hierarchies are consistent with experimental binding results. Currently, predictions of potential binding peptides to a specific MHC molecule are based on the identification of allele-specific binding motifs. However, it has been demonstrated that these motifs are neither sufficient nor strictly required to ensure binding. The computational procedure presented here succeeds in determining the MHC binding potential of peptides along a protein amino acid sequence, without relying on binding motifs. The proposed scheme may significantly reduce the number of peptides to be tested, identify good binders that do not necessarily show the known allele-specific binding motifs, and identify the best candidates among those with the motifs. In general, when structural information about a protein-peptide complex is available, the current application of the threading approach can be used to screen a large library of peptides for selection of the best binders to the target protein.