Prediction Of Peptide Binding Research Articles

Prediction of binding peptides to class I Major Histocompatibility Complex using modified scoring matrices and data splitting strategies

Predicting peptides that can bind to MHC class I molecules is an important step in the vaccine design process. Computational approaches have potential to provide good predictive models that save both time and cost of the process. Position Specific Scoring Matrix (PSSM) is a reliable approach when dealing with amino acid sequences. PSSM formation involves carefully selecting its constructing data and parameters. In this work, we apply three different data splitting strategies and propose alternative values for the embedded PSSM parameters. The basic principle of data splitting is to choose train data that is able to represent the whole data. We propose using the Kennard–Stone algorithm to highlight the importance of choosing the data constituting the PSSM. Furthermore, this work proposes modifications to PSSM parameters and studies the model behavior in response to each change. The model is applied to experimental data for the Major Histocompatibility Complex of class I. Performance of modified parameters show either comparable or better results to conventional parameters. Moreover, Kennard–Stone data splitting algorithm contributed to significant model performance enhancement.

A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information.

MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter α in the weight formula. By changing α value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.

MetaMHCpan, A Meta Approach for Pan-Specific MHC Peptide Binding Prediction.

Recent computational approaches in bioinformatics can achieve high performance, by which they can be a powerful support for performing real biological experiments, making biologists pay more attention to bioinformatics than before. In immunology, predicting peptides which can bind to MHC alleles is an important task, being tackled by many computational approaches. However, this situation causes a serious problem for immunologists to select the appropriate method to be used in bioinformatics. To overcome this problem, we develop an ensemble prediction-based Web server, which we call MetaMHCpan, consisting of two parts: MetaMHCIpan and MetaMHCIIpan, for predicting peptides which can bind MHC-I and MHC-II, respectively. MetaMHCIpan and MetaMHCIIpan use two (MHC2SKpan and LApan) and four (TEPITOPEpan, MHC2SKpan, LApan, and MHC2MIL) existing predictors, respectively. MetaMHCpan is available at http://datamining-iip.fudan.edu.cn/MetaMHCpan/index.php/pages/view/info .

Abstract IA18: Fighting fire with fire: Developing a personalized neoantigen cancer vaccine

Abstract One of the critical steps to developing curative and tumor-specific immunotherapy is the identification and selection of antigens with tumor-restricted expression in order to avoid undesirable immune responses against normal tissues. Tumor neoantigens are analogous to non-self foreign proteins, therefore bypassing central tolerance and are also exquisitely tumor-specific. Seminal studies have provided compelling evidence suggesting their immunogenicity. However, the comprehensive identification of such neoantigens has only become feasible recently using next-generation massively-parallel sequencing technologies, providing unprecedented genetic information about cancer cells. In addition, refinements in class I HLA prediction algorithms, particularly NetMHCpan, have enabled the reliable prediction of peptide binding for a broad range of class I HLA alleles. Recent studies by us and others in humans and mice have leveraged these tools to demonstrate evidence of protective immunity when targeting tumor neoantigens. We have developed pipelines that leverage WES to systematically identify personal tumor mutations with immunogenic potential, which can be incorporated as antigen targets in a multi-epitope personalized therapeutic vaccine. We are pioneering this approach in a first-in-man clinical trial in melanoma. Citation Format: Catherine J. Wu. Fighting fire with fire: Developing a personalized neoantigen cancer vaccine. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy: A New Chapter; December 1-4, 2014; Orlando, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2015;3(10 Suppl):Abstract nr IA18.

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.

The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development.

Peptide Binding Prediction to Five Most Frequent HLA-DQ Proteins - a Proteochemometric Approach.

Major histocompatibility complex (MHC) proteins class II, are glycoproteins binding within the cell to short peptides with foreign origin, called epitopes, and present them at the cell surface for inspection by T-cells. Apart from presenting foreign antigens, they are able to present also common self-antigens and trigger autoimmune diseases as coeliac disease and diabetes mellitus type 1. The MHC proteins are extremely polymorphic. The polymorphism is located mainly in the peptide binding site. In the present study, we apply a proteochemometric approach to derive a model for prediction of peptide binding to human MHC class II proteins from locus HLA-DQ. Proteochemometrics was applied on 2624 peptides binding to five most frequent HLA-DQ proteins. The sequences of peptides and proteins were described by three z-descriptors relating to hydrophobicity, steric effects and polarity of amino acids. Cross-terms accounting for the protein-peptide interactions also were included. The derived model was validated by external test set of 660 peptides and showed rpred (2) =0.808, AUC=0.965, 92.5 % accuracy at threshold of pIC50 =5.3 and average sensitivity of 83 % among the top 10 % best predicted nonamers. The model is implemented in the server for MHC binding prediction EpiTOP and is freely available at http://www.ddg-pharmfac.net/epitop.

TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes

Despite the availability of major histocompatibility complex (MHC)-binding peptide prediction algorithms, the development of T-cell vaccines against pathogen and tumor antigens remains challenged by inefficient identification of immunogenic epitopes. CD8(+) T cells must distinguish immunogenic epitopes from nonimmunogenic self peptides to respond effectively against an antigen without endangering the viability of the host. Because this discrimination is fundamental to our understanding of immune recognition and critical for rational vaccine design, we interrogated the biochemical properties of 9,888 MHC class I peptides. We identified a strong bias toward hydrophobic amino acids at T-cell receptor contact residues within immunogenic epitopes of MHC allomorphs, which permitted us to develop and train a hydrophobicity-based artificial neural network (ANN-Hydro) to predict immunogenic epitopes. The immunogenicity model was validated in a blinded in vivo overlapping epitope discovery study of 364 peptides from three HIV-1 Gag protein variants. Applying the ANN-Hydro model on existing peptide-MHC algorithms consistently reduced the number of candidate peptides across multiple antigens and may provide a correlate with immunodominance. Hydrophobicity of TCR contact residues is a hallmark of immunogenic epitopes and marks a step toward eliminating the need for empirical epitope testing for vaccine development.

Predicting Peptide Binding Sites on Protein Surfaces by Clustering Chemical Interactions

Short peptides play important roles in cellular processes including signal transduction, immune response and transcription regulation. Correct identification of the peptide binding site on a given protein surface is of great importance not only for mechanistic investigation of these biological processes but also for therapeutic development. In the present study, we developed a novel computational approach, referred to as ACCLUSTER, for predicting the peptide binding sites on protein surfaces. Specifically, we use the twenty standard amino acids as probes to globally scan the protein surface. The poses forming good chemical interactions with the protein are identified, followed by clustering with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique. Finally, these clusters are ranked based on their sizes. The cluster with the largest size is predicted as the putative binding site. Assessment of ACCLUSTER was performed on a diverse test set of 251 non-redundant protein-peptide complexes. The results were compared with the performance of POCASA, a pocket detection method for ligand binding site prediction. Peptidb, another protein-peptide database that contains both bound structures and unbound or homologous structures was used to test the robustness of ACCLUSTER. The performance of ACCLUSTER was also compared with PepSite2 and PeptiMap, two recently developed methods developed for identifying peptide binding sites. The results showed that ACCLUSTER is a promising method for peptide binding site prediction. Additionally, ACCLUSTER was also shown to be applicable to small molecular binding site prediction and protein-protein interface prediction. Notably, ACCLUSTER is based on physical interactions rather than informatics training, and can easily be extended to other macromolecular systems, including systems like peptide-RNA complexes which lack sufficient structural data and therefore pose challenges to machine-learning based methods.

Building MHC class II epitope predictor using machine learning approaches.

Identification of T-cell epitopes binding to MHC class II molecules is an important step in epitope-based vaccine development. This process has since been accelerated with the use of bioinformatics tools to aid in the prediction of peptide binding to MHC class II molecules and also to systematically scan for candidate peptides in antigenic proteins. There have been many prediction software developed over the years using various methods and algorithms and they are becoming increasingly sophisticated. Here, we illustrate the use of machine learning algorithms to train on MHC class II peptide data represented by feature vectors describing their amino acid physicochemical properties. The developed prediction model can then be used to predict new peptide data.

Characterization of binding specificities of bovine leucocyte class I molecules: impacts for rational epitope discovery.

The binding of peptides to classical major histocompatibility complex (MHC) class I proteins is the single most selective step in antigen presentation. However, the peptide-binding specificity of cattle MHC (bovine leucocyte antigen, BoLA) class I (BoLA-I) molecules remains poorly characterized. Here, we demonstrate how a combination of high-throughput assays using positional scanning combinatorial peptide libraries, peptide dissociation, and peptide-binding affinity binding measurements can be combined with bioinformatics to effectively characterize the functionality of BoLA-I molecules. Using this strategy, we characterized eight BoLA-I molecules, and found the peptide specificity to resemble that of human MHC-I molecules with primary anchors most often at P2 and P9, and occasional auxiliary P1/P3/P5/P6 anchors. We analyzed nine reported CTL epitopes from Theileria parva, and in eight cases, stable and high affinity binding was confirmed. A set of peptides were tested for binding affinity to the eight BoLA proteins and used to refine the predictors of peptide-MHC binding NetMHC and NetMHCpan. The inclusion of BoLA-specific peptide-binding data led to a significant improvement in prediction accuracy for reported T. parva CTL epitopes. For reported CTL epitopes with weak or no predicted binding, these refined prediction methods suggested presence of nested minimal epitopes with high-predicted binding affinity. The enhanced affinity of the alternative peptides was in all cases confirmed experimentally. This study demonstrates how biochemical high-throughput assays combined with immunoinformatics can be used to characterize the peptide-binding motifs of BoLA-I molecules, boosting performance of MHC peptide-binding prediction methods, and empowering rational epitope discovery in cattle.

CTL responses to HSP47 associated with the prolonged survival of patients with glioblastomas

To define heat shock protein 47 (HSP47) as a novel glioma-associated antigen and to preliminarily assess the association of cytotoxic T lymphocyte (CTL) responses to HSP47 with clinical outcomes in patients with glioblastomas (GBMs). The expression of HSP47 was determined in primary GBM tissues (n = 17) and controlled brain tissues (n = 10) by Western blot. Candidate epitope peptides were predicted using the human leukocyte antigen (HLA) Peptide Binding Predictions Program. The CTL responses to HSP47 were quantified in peripheral blood mononuclear cells from 6 healthy donors and 38 patients (benign tumors = 5, astrocytoma grade II = 7, anaplastic gliomas grade III = 10, GBMs = 16) by stimulation with the mixture of the identified peptides above. Kaplan-Meier survival curves were used to analyze the association between CTL responses and clinical outcomes. Expression of HSP47 was hardly detectable in controlled brain tissues and increased in GBM tissues (p = 0.018). HSP47(184-192) (KLPEVTKDV) and HSP47(3-11) (LLLLSAFCL) were predicted as the most potent candidate epitope peptides with experimentally confirmed binding affinity to the HLA-A0201 molecule. Seven of 26 patients (26.9%) with malignant gliomas had positive CTL responses. Furthermore, patients with GBM with positive CTL responses to HSP47 experienced a prolonged progress-free survival time (12.6 ± 1.3 vs 8.1 ± 3.2 months, p = 0.01) and overall survival (13.4 ± 1.3 vs 10.4 ± 2.7 months, p = 0.035) than those with negative responses. Our data demonstrated that HSP47 is a novel glioma-associated antigen. HSP47-based vaccine will likely confer additional survival benefit to patients with GBM after surgical treatment.

Identification of MHC Class, Transports Antigenic Peptides from Naja melanoleuca Long Neurotoxin 1

Long neurotoxin 1 is a dangerous protein occurs in N. melanoleuca. It is highly dangerous snake due to the quantity of venom inject in a single bite. Peptide fragments of N. Melanoleuca Long neurotoxin 1 antigen protein having 71 amino acids, which shows 63 nanomers can be used to select nanomers for use in synthetic peptide vaccine design and to increase the understanding of roles of the immune system against snake bite. We use to apply computational intelligence algorithm PSSM (Position Specific Scoring Matrices) for the Prediction of MHC class-I binding peptides, we also predict antigenicity, Solvent accessibility, polar and nonpolar residue to analyses the membrane-spanning regions (hydrophobic) or regions that are likely exposed on the surface of proteins (hydrophilic domains) that are potentially antigenic that allows potential drug targets to identify active sites, for protection of host form Snake bites and to design synthetic peptide vaccine.

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ

Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide-MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0 .

The ever-growing peptide knowledge promotes the improvement of HLA class I peptide-binding prediction

Computational prediction methods for peptide binding to human leukocyte antigen (HLA) molecules have played an instrumental role in the development of epitope-based vaccines. These methods are based on experimentally verified peptides. However, the available peptide data continue increasing and contain significant biases. In this study, we report the feedback effect of peptide data on a frequently used matrix-based prediction method. We implemented the weighted and unweighted models of this method and evaluated the relative performance of the two models on several benchmark datasets. Improvements on both models were obtained by optimizing the components of a training dataset based on the effect of peptide data on the performance of prediction models. Moreover, the variation of the relative performance of the weighted and unweighted models with the evaluated data indicated that the increased number of binding peptides required the modification of the predictive engine. Our results suggest that prediction methods for HLA-binding peptides should be updated as HLA-peptide-binding knowledge increases.

Integrating peptides' sequence and energy of contact residues information improves prediction of peptide and HLA-I binding with unknown alleles

BackgroundThe HLA (human leukocyte antigen) class I is a kind of molecule encoded by a large family of genes and is characteristic of high polymorphism. Now the number of the registered HLA-I molecules has exceeded 3000. Slight differences in the amino acid sequences of HLAs would make them bind to different sets of peptides. In the past decades, although many methods have been proposed to predict the binding between peptides and HLA-I molecules and achieved good performance, most experimental data used by them is limited to the HLAs with a small number of alleles. Thus they are inclined to obtain high prediction accuracy only for data with similar alleles. Because the peptides and HLAs together determine the binding, it's necessary to consider their contribution meanwhile.ResultsBy taking into account the features of the peptides sequence and the energy of contact residues, in this paper a method based on the artificial neural network is proposed to predict the binding of peptides and HLA-I even when the HLAs' potential alleles are unknown. Two experiments in the allele-specific and super-type cases are performed respectively to validate our method. In the first case, we collect 14 HLA-A and 14 HLA-B molecules on Bjoern Peters dataset, and compare our method with the ARB, SMM, NetMHC and other 16 online methods. Our method gets the best average AUC (Area under the ROC) value as 0.909. In the second one, we use leave one out cross validation on MHC-peptide binding data that has different alleles but shares the common super-type. Compared to gold standard methods like NetMHC and NetMHCpan, our method again achieves the best average AUC value as 0.847.ConclusionsOur method achieves satisfactory results. Whenever it's tested on the HLA-I with single definite gene or with super-type gene locus, it gets better classification accuracy. Especially, when the training set is small, our method still works better than the other methods in the comparison. Therefore, we could make a conclusion that by combining the peptides' information, HLAs amino acid residues' interaction information and contact energy, our method really could improve prediction of the peptide HLA-I binding even when there aren't the prior experimental dataset for HLAs with various alleles.

Quantitative Prediction of Peptide Binding to HLA-DP1 Protein

The exogenous proteins are processed by the host antigen-processing cells. Peptidic fragments of them are presented on the cell surface bound to the major hystocompatibility complex (MHC) molecules class II and recognized by the CD4+ T lymphocytes. The MHC binding is considered as the crucial prerequisite for T-cell recognition. Only peptides able to form stable complexes with the MHC proteins are recognized by the T-cells. These peptides are known as T-cell epitopes. All T-cell epitopes are MHC binders, but not all MHC binders are T-cell epitopes. The T-cell epitope prediction is one of the main priorities of immunoinformatics. In the present study, three chemometric techniques are combined to derive a model for in silico prediction of peptide binding to the human MHC class II protein HLA-DP1. The structures of a set of known peptide binders are described by amino acid z-descriptors. Data are processed by an iterative self-consisted algorithm using the method of partial least squares, and a quantitative matrix (QM) for peptide binding prediction to HLA-DP1 is derived. The QM is validated by two sets of proteins and showed an average accuracy of 86 percent.

Protein-Peptide Complex Prediction through Fragment Interaction Patterns

The number of protein-peptide interactions in a cell isso large that experimental determination of all these complex structures would be a daunting task. Although homology modeling and refinement protocols have vastly improved the number and quality of predicted structural models, ab initio methods are still challenged by both the large number of possible docking sites and the conformational space accessible to flexible peptides. We present a method that addresses these challenges by sampling the entire accessible surface of a protein with a reduced conformational space of interacting backbone fragment pairs from unrelated structures. We demonstrate its potential by predicting ab initio the bound structure for a variety of protein-peptide complexes. In addition, we show the potential of our method forthe discovery of domain interaction sites and domain-domain docking.

Induction of prominent Th1 response in C57Bl/6 mice immunized with an E.coli-expressed multi T-cell epitope EgA31 antigen against Echinococcus granulosus

First step in developing an epitope-based vaccine is to predict peptide binding to the major histocompatibility complex (MHC) molecules. We performed computational analysis of unique available EgA31 sequence to locate appropriate antigenic propensity positions. T-cell epitopes with best binding affinity values of < 50% inhibitory concentration were selected using different available servers (Propred and IEDB). Peptides with 100% population coverage were selected. A DNA fragment corresponding to the furin linker enriched in Golgi apparatus was inserted sequentially between each epitope sequences in a synthetic DNA in order to cleave the chimeric protein into four separated peptides. Subsequently, the synthetic DNA was cloned into the pGEX4T-1 and pEGFP-N1 vectors and GST-ChEgA31 was expressed in E. coli strain BL21-DE3. The recombinant protein was detected by western blotting using an HRP-conjugated polyclonal anti-GST antibody. Fusion protein purified by affinity chromatography was used to raise antisera in rabbits. Results in agar gel immunodiffusion assay indicated induction of specific antibodies against multiepitope antigen in the tested rabbits. Cytokine assay was carried out in C57Bl/6 mice and the levels of cytokines were analyzed by sandwich ELISA. Interestingly, production of specific IFN-gamma was prominently higher in mice immunized with GST-ChEgA31 and pEGFP-ChEgA31 (650-1300 pg/ml) compared to control groups. No difference was observed in the level of IL-10 and IL-4 in immunized and GST control group. Challenge study with 500 live protoscolices of Echinococcus granulosus on immunized mice demonstrated protectivity level (50-60%). Based on our results, it appeared that the chimeric protein in the study was able to stimulate T-helper cell-1 (Th1) development and high level of cell mediated immunity in mice.

In silico peptide-binding predictions of passerine MHC class I reveal similarities across distantly related species, suggesting convergence on the level of protein function

The major histocompatibility complex (MHC) genes are the most polymorphic genes found in the vertebrate genome, and they encode proteins that play an essential role in the adaptive immune response. Many songbirds (passerines) have been shown to have a large number of transcribed MHC class I genes compared to most mammals. To elucidate the reason for this large number of genes, we compared 14 MHC class I alleles (α1-α3 domains), from great reed warbler, house sparrow and tree sparrow, via phylogenetic analysis, homology modelling and in silico peptide-binding predictions to investigate their functional and genetic relationships. We found more pronounced clustering of the MHC class I allomorphs (allele specific proteins) in regards to their function (peptide-binding specificities) compared to their genetic relationships (amino acid sequences), indicating that the high number of alleles is of functional significance. The MHC class I allomorphs from house sparrow and tree sparrow, species that diverged 10 million years ago (MYA), had overlapping peptide-binding specificities, and these similarities across species were also confirmed in phylogenetic analyses based on amino acid sequences. Notably, there were also overlapping peptide-binding specificities in the allomorphs from house sparrow and great reed warbler, although these species diverged 30 MYA. This overlap was not found in a tree based on amino acid sequences. Our interpretation is that convergent evolution on the level of the protein function, possibly driven by selection from shared pathogens, has resulted in allomorphs with similar peptide-binding repertoires, although trans-species evolution in combination with gene conversion cannot be ruled out.