ProPred1: prediction of promiscuous MHC Class-I binding sites

SUMMARY: ProPred1 is an on-line web tool for the prediction of peptide binding to MHC class-I alleles. This is a matrix-based method that allows the prediction of MHC binding sites in an antigenic sequence for 47 MHC class-I alleles. The server represents MHC binding regions within an antigenic sequence in user-friendly formats. These formats assist user in the identification of promiscuous MHC binders in an antigen sequence that can bind to large number of alleles. ProPred1 also allows the prediction of the standard proteasome and immunoproteasome cleavage sites in an antigenic sequence. This server allows identification of MHC binders, who have the cleavage site at the C terminus. The simultaneous prediction of MHC binders and proteasome cleavage sites in an antigenic sequence leads to the identification of potential T-cell epitopes. AVAILABILITY: Server is available at http://www.imtech.res.in/raghava/propred1/. Mirror site of this server is available at http://bioinformatics.uams.edu/mirror/propred1/ Supplementary information: Matrices and document on server are available at http://www.imtech.res.in/raghava/propred1/page2.html     

Integrated modeling of the major events in the MHC class I antigen processing pathway

Rational design of epitope-driven vaccines is a key goal of immunoinformatics. Typically, candidate selection relies on the prediction of MHC-peptide binding only, as this is known to be the most selective step in the MHC class I antigen processing pathway. However, proteasomal cleavage and transport by the transporter associated with antigen processing (TAP) are essential steps in antigen processing as well. While prediction methods exist for the individual steps, no method has yet offered an integrated prediction of all three major processing events. Here we present WAPP, a method combining prediction of proteasomal cleavage, TAP transport, and MHC binding into a single prediction system. The proteasomal cleavage site prediction employs a new matrix-based method that is based on experimentally verified proteasomal cleavage sites. Support vector regression is used for predicting peptides transported by TAP. MHC binding is the last step in the antigen processing pathway and was predicted using a support vector machine method, SVMHC. The individual methods are combined in a filtering approach mimicking the natural processing pathway. WAPP thus predicts peptides that are cleaved by the proteasome at the C terminus, transported by TAP, and show significant affinity to MHC class I molecules. This results in a decrease in false positive rates compared to MHC binding prediction alone. Compared to prediction of MHC binding only, we report an increased overall accuracy and a lower rate of false positive predictions for the HLA-A*0201, HLA-B*2705, HLA-A*01, and HLA-A*03 alleles using WAPP. The method is available online through our prediction server at http://www-bs.informatik.uni-tuebingen.de/WAPP     

NetCTLpan: pan-specific MHC class I pathway epitope predictions

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at .     

MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties

Major histocompatibility complex (MHC)-binding peptides are essential for antigen recognition by T-cell receptors and are being explored for vaccine design. Computational methods have been developed for predicting MHC-binding peptides of fixed lengths, based on the training of relatively few non-binders. It is desirable to introduce methods applicable for peptides of flexible lengths and trained by using more diverse sets of non-binders. MHC-BPS is a web-based MHC-binder prediction server that uses support vector machines for predicting peptide binders of flexible lengths for 18 MHC class I and 12 class II alleles from sequence-derived physicochemical properties, which were trained by using 4,208∼3,252 binders and 234,333∼168,793 non-binders, and evaluated by an independent set of 545∼476 binders and 110,564∼84,430 non-binders. The binder prediction accuracies are 86∼99% for 25 and 70∼80% for five alleles, and the non-binder accuracies are 96∼99% for 30 alleles. A screening of HIV-1 genome identifies 0.01∼5% and 5∼8% of the constituent peptides as binders for 24 and 6 alleles, respectively, including 75∼100% of the known epitopes. This method correctly predicts 73.3% of the 15 newly published epitopes in the last 4 months of 2005. MHC-BPS is available at .Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy

Identification of MHC binding peptides is essential for understanding the molecular mechanism of immune response. However, most of the prediction methods use motifs/profiles derived from experimental peptide binding data for specific MHC alleles, thus limiting their applicability only to those alleles for which such data is available. In this work we have developed a structure-based method which does not require experimental peptide binding data for training. Our method models MHC-peptide complexes using crystal structures of 170 MHC-peptide complexes and evaluates the binding energies using two well known residue based statistical pair potentials, namely Betancourt-Thirumalai (BT) and Miyazawa-Jernigan (MJ) matrices. Extensive benchmarking of prediction accuracy on a data set of 1654 epitopes from class I and class II alleles available in the SYFPEITHI database indicate that BT pair-potential can predict more than 60% of the known binders in case of 14 MHC alleles with AUC values for ROC curves ranging from 0.6 to 0.9. Similar benchmarking on 29,522 class I and class II MHC binding peptides with known IC(50) values in the IEDB database showed AUC values higher than 0.6 for 10 class I alleles and 9 class II alleles in predictions involving classification of a peptide to be binder or non-binder. Comparison with recently available benchmarking studies indicated that, the prediction accuracy of our method for many of the class I and class II MHC alleles was comparable to the sequence based methods, even if it does not use any experimental data for training. It is also encouraging to note that the ranks of true binding peptides could further be improved, when high scoring peptides obtained from pair potential were re-ranked using all atom forcefield and MM/PBSA method.     

Efficient peptide-MHC-I binding prediction for alleles with few known binders

MOTIVATION: In silico methods for the prediction of antigenic peptides binding to MHC class I molecules play an increasingly important role in the identification of T-cell epitopes. Statistical and machine learning methods in particular are widely used to score candidate binders based on their similarity with known binders and non-binders. The genes coding for the MHC molecules, however, are highly polymorphic, and statistical methods have difficulties building models for alleles with few known binders. In this context, recent work has demonstrated the utility of leveraging information across alleles to improve the performance of the prediction. RESULTS: We design a support vector machine algorithm that is able to learn peptide-MHC-I binding models for many alleles simultaneously, by sharing binding information across alleles. The sharing of information is controlled by a user-defined measure of similarity between alleles. We show that this similarity can be defined in terms of supertypes, or more directly by comparing key residues known to play a role in the peptide-MHC binding. We illustrate the potential of this approach on various benchmark experiments where it outperforms other state-of-the-art methods. AVAILABILITY: The method is implemented on a web server: http://cbio.ensmp.fr/kiss. All data and codes are freely and publicly available from the authors.     

A community resource benchmarking predictions of peptide binding to MHC-I molecules

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.     

DynaPred: a structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations

MOTIVATION: The binding of endogenous antigenic peptides to MHC class I molecules is an important step during the immunologic response of a host against a pathogen. Thus, various sequence- and structure-based prediction methods have been proposed for this purpose. The sequence-based methods are computationally efficient, but are hampered by the need of sufficient experimental data and do not provide a structural interpretation of their results. The structural methods are data-independent, but are quite time-consuming and thus not suited for screening of whole genomes. Here, we present a new method, which performs sequence-based prediction by incorporating information obtained from molecular modeling. This allows us to perform large databases screening and to provide structural information of the results. RESULTS: We developed a SVM-trained, quantitative matrix-based method for the prediction of MHC class I binding peptides, in which the features of the scoring matrix are energy terms retrieved from molecular dynamics simulations. At the same time we used the equilibrated structures obtained from the same simulations in a simple and efficient docking procedure. Our method consists of two steps: First, we predict potential binders from sequence data alone and second, we construct protein-peptide complexes for the predicted binders. So far, we tested our approach on the HLA-A0201 allele. We constructed two prediction models, using local, position-dependent (DynaPred(POS)) and global, position-independent (DynaPred) features. The former model outperformed the two sequence-based methods used in our evaluation; the latter shows a much higher generalizability towards other alleles than the position-dependent models. The constructed peptide structures can be refined within seconds to structures with an average backbone RMSD of 1.53 A from the corresponding experimental structures.     

MAPPP: MHC class I antigenic peptide processing prediction

MAPPP is a bioinformatics tool for the prediction of potential antigenic epitopes presented on the cell surface by major histocompatibility complex class I (MHC I) molecules to CD8 positive T lymphocytes. It combines existing predictions for proteasomal cleavage with peptide anchoring to MHC I molecules.     

PAProC: a prediction algorithm for proteasomal cleavages available on the WWW

The first version of PAProC (Prediction Algorithm for Proteasomal Cleavages) is now available to the general public. PAProC is a prediction tool for cleavages by human and yeast proteasomes, based on experimental cleavage data. It will be particularly useful for immunologists working on antigen processing and the prediction of major histocompatibility complex class I molecule (MHC I) ligands and cytotoxic T-lymphocyte (CTL) epitopes. Likewise, in cases in which proteasomal protein degradation has been indicated in disease, PAProC can be used to assess the general cleavability of disease-linked proteins. On its web site (http://www.paproc.de), background information and hyperlinks are provided for the user (e.g., to SYFPEITHI, the database for the prediction of MHC I ligands).     

Quantitative analysis of prion-protein degradation by constitutive and immuno-20S proteasomes indicates differences correlated with disease susceptibility

The main part of cytosolic protein degradation depends on the ubiquitin-proteasome system. Proteasomes degrade their substrates into small peptide fragments, some of which are translocated into the endoplasmatic reticulum and loaded onto MHC class I molecules, which are then transported to the cell surface for inspection by CTL. A reliable prediction of proteasomal cleavages in a given protein for the identification of CTL epitopes would benefit immensely from additional cleavage data for the training of prediction algorithms. To increase the knowledge about proteasomal specificity and to gain more insight into the relation of proteasomal activity and susceptibility to prion disease, we digested sheep prion protein with human constitutive and immuno-20S proteasomes. All fragments generated in the digest were quantified. Our results underline the different cleavage specificities of constitutive and immunoproteasomes and provide data for the training of prediction programs for proteasomal cleavages. Furthermore, the kinetic analysis of proteasomal digestion of two different alleles of prion protein shows that even small changes in a protein sequence can affect the overall efficiency of proteasomal processing and thus provides more insight into the possible molecular background of allelic variations and the pathogenicity of prion proteins.     

An algorithm for the prediction of proteasomal cleavages

Proteasomes, major proteolytic sites in eukaryotic cells, play an important part in major histocompatibility class I (MHC I) ligand generation and thus in the regulation of specific immune responses. Their cleavage specificity is of outstanding interest for this process.In order to generalize previously determined cleavage motifs of 20 S proteasomes, we developed network-based model proteasomes trained by an evolutionary algorithm with experimental cleavage data of yeast and human 20 S proteasomes. A window of ten flanking amino acid residues proved sufficient for the model proteasomes to reproduce the experimental results with 98-100 % accuracy. Actual experimental data were reproduced significantly better than randomly selected cleavage sites, suggesting that our model proteasomes were able to extract rules inherent to proteasomal cleavage data. The affinity parameters of the model, which decide for or against cleavage, correspond with the cleavage motifs determined experimentally. The predictive power of the model was verified for unknown (to the program) test conditions: the prediction of cleavage numbers in proteins and the generation of MHC I ligands from short peptides.In summary, our model proteasomes reproduce and predict proteasomal cleavages with high degree of accuracy. They present a promising approach for predicting proteasomal cleavage products in future attempts and, in combination with existing algorithms for MHC I ligand prediction, will be tested to improve cytotoxic T lymphocyte epitope prediction.     

Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the C-terminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism.     

Methods for prediction of peptide binding to MHC molecules: a comparative study

BACKGROUND: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. MATERIALS AND METHODS: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. RESULTS: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. CONCLUSION: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.     

Prediction of proteasome cleavage motifs by neural networks

We present a predictive method that can simulate an essential step in the antigen presentation in higher vertebrates, namely the step involving the proteasomal degradation of polypeptides into fragments which have the potential to bind to MHC Class I molecules. Proteasomal cleavage prediction algorithms published so far were trained on data from in vitro digestion experiments with constitutive proteasomes. As a result, they did not take into account the characteristics of the structurally modified proteasomes--often called immunoproteasomes--found in cells stimulated by gamma-interferon under physiological conditions. Our algorithm has been trained not only on in vitro data, but also on MHC Class I ligand data, which reflect a combination of immunoproteasome and constitutive proteasome specificity. This feature, together with the use of neural networks, a non-linear classification technique, make the prediction of MHC Class I ligand boundaries more accurate: 65% of the cleavage sites and 85% of the non-cleavage sites are correctly determined. Moreover, we show that the neural networks trained on the constitutive proteasome data learns a specificity that differs from that of the networks trained on MHC Class I ligands, i.e. the specificity of the immunoproteasome is different than the constitutive proteasome. The tools developed in this study in combination with a predictor of MHC and TAP binding capacity should give a more complete prediction of the generation and presentation of peptides on MHC Class I molecules. Here we demonstrate that such an approach produces an accurate prediction of the CTL the epitopes in HIV Nef. The method is available at www.cbs.dtu.dk/services/NetChop/.     

<Emphasis Type="Italic">NetMHCcons</Emphasis>: a consensus method for the major histocompatibility complex class I predictions

A key role in cell-mediated immunity is dedicated to the major histocompatibility complex (MHC) molecules that bind peptides for presentation on the cell surface. Several in silico methods capable of predicting peptide binding to MHC class I have been developed. The accuracy of these methods depends on the data available characterizing the binding specificity of the MHC molecules. It has, moreover, been demonstrated that consensus methods defined as combinations of two or more different methods led to improved prediction accuracy. This plethora of methods makes it very difficult for the non-expert user to choose the most suitable method for predicting binding to a given MHC molecule. In this study, we have therefore made an in-depth analysis of combinations of three state-of-the-art MHC–peptide binding prediction methods (NetMHC, NetMHCpan and PickPocket). We demonstrate that a simple combination of NetMHC and NetMHCpan gives the highest performance when the allele in question is included in the training and is characterized by at least 50 data points with at least ten binders. Otherwise, NetMHCpan is the best predictor. When an allele has not been characterized, the performance depends on the distance to the training data. NetMHCpan has the highest performance when close neighbours are present in the training set, while the combination of NetMHCpan and PickPocket outperforms either of the two methods for alleles with more remote neighbours. The final method, NetMHCcons, is publicly available at , and allows the user in an automatic manner to obtain the most accurate predictions for any given MHC molecule.     

Modeling the adaptive immune system: predictions and simulations

MOTIVATION: Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered. SUMMARY: Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.     

Pathways accessory to proteasomal proteolysis are less efficient in major histocompatibility complex class I antigen production

Degradation of cytosolic proteins depends largely on the proteasome, and a fraction of the cleavage products are presented as major histocompatibility complex (MHC) class I-bound ligands at the cell surface of antigen presenting cells. Proteolytic pathways accessory to the proteasome contribute to protein turnover, and their up-regulation may complement the proteasome when proteasomal proteolysis is impaired. Here we show that reduced reliance on proteasomal proteolysis allowed a reduced efficiency of MHC class I ligand production, whereas protein turnover and cellular proliferation were maintained. Using the proteasomal inhibitor adamantane-acetyl-(6-aminohexanoyl)3-(leucinyl)3-vinyl-(methyl)-sulphone, we show that covalent inhibition of all three types of proteasomal beta-subunits (beta(1), beta(2), and beta(5)) was compatible with continued growth in cells that up-regulate accessory proteolytic pathways, which include cytosolic proteases as well as deubiquitinating enzymes. However, under these conditions, we observed poor assembly of H-2D(b) molecules and inhibited presentation of endogenous tumor antigens. Thus, the tight link between protein turnover and production of MHC class I ligands can be broken by enforcing the substitution of the proteasome with alternative proteolytic pathways.     

An empirical method for the prediction of T-cell epitopes

Identification of T-cell epitopes from foreign proteins is the current focus of much research. Methods using simple two or three position motifs have proved useful in epitope prediction for major histocompatibility complex (MHC) class I, but to date not for MHC class II molecules. We utilized data from pool sequence analysis of peptides eluted from two HLA-DR13 alleles to construct a computer algorithm for predicting the probability that a given sequence will be naturally processed and presented on these alleles. We assessed the ability of this method to predict know self-peptides from these DR-13 alleles, DRB1 ^*1301 and *1302, as well as an immunodominant T-cell epitope. We also compared the predictions of this scoring procedure with the measured binding affinities of a panel of overlapping peptides from hepatitis B virus surface antigen. We concluded that this method may have wide application for the prediction of T-cell epitopes for both MHC class I and class II molecules.     

Evaluation of different generic in silico methods for predicting HLA class I binding peptide vaccine candidates using a reverse approach

Since CD8⁺ T cell response is crucial to combat intracellular infections and cancer, identification of class I HLA binding peptides is of immense clinical value. The experimental identification of such peptides is protracted and laborious. Exploiting in silico tools to discover such peptides is an attractive alternative. However, this approach needs a thorough assessment before its elaborate application. We have adopted a reverse approach to evaluate the reliability of eight different servers (inclusive of 55 predictors) by exploiting experimentally proven data. A comprehensive data set of more than 960 peptides was employed to test the efficacy of the programs. We have validated commonly used strategies to predict peptides that bind to seven most prevalent HLA class I alleles. We conclude that four of the eight servers are more adept in predictions. Although the overall predictions for class I MHC binders were superior to class II MHC binders, individual predictors for different alleles belonging to the same program were highly variable in their efficiencies. We have also addressed whether a consensus approach can yield better prediction efficiency. We observed that combining the results from different in silico programs could not increase the efficiency significantly.