MHCPEP--a database of MHC-binding peptides: update 1995.

MHCPEP is a curated database comprising over 6000 peptide sequences known to bind MHC molecules. Entries are compiled from published reports as well as from direct submissions of experimental data. Each entry contains peptide sequence, MHC specificity and when available, experimental method, observed activity, binding affinity, source protein, anchor positions, as well as publication references. The present format of the database allows text string matching searches but can easily be converted for use in conjunction with sequence analysis packages. The database can be accessed via Internet using Gopher, FTP or WWW.     

MHCPEP, a database of MHC-binding peptides: update 1997.

MHCPEP (http://wehih.wehi.edu.au/mhcpep/) is a curated database comprising over 13 000 peptide sequences known to bind MHC molecules. Entries are compiled from published reports as well as from direct submissions of experimental data. Each entry contains the peptide sequence, its MHC specificity and where available, experimental method, observed activity, binding affinity, source protein and anchor positions, as well as publication references. The present format of the database allows text string matching searches but can easily be converted for use in conjunction with sequence analysis packages. The database can be accessed via Internet using WWW or FTP.     

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.     

MHCPEP: a database of MHC-binding peptides.

MHCPEP is a curated database comprising over 4000 peptide sequences known to bind MHC molecules. Entries are compiled from published reports as well as from direct submissions of experimental data. Each entry contains the source protein (when known), an estimate of binding affinity and critical anchor residues (if identified), and is fully referenced. The present format of the database allows test string matching searches. The database can be accessed via Internet using gopher.     

MHCBN: a comprehensive database of MHC binding and non-binding peptides

MHCBN is a comprehensive database of Major Histocompatibility Complex (MHC) binding and non-binding peptides compiled from published literature and existing databases. The latest version of the database has 19 777 entries including 17 129 MHC binders and 2648 MHC non-binders for more than 400 MHC molecules. The database has sequence and structure data of (a) source proteins of peptides and (b) MHC molecules. MHCBN has a number of web tools that include: (i) mapping of peptide on query sequence; (ii) search on any field; (iii) creation of data sets; and (iv) online data submission. The database also provides hypertext links to major databases like SWISS-PROT, PDB, IMGT/HLA-DB, GenBank and PUBMED.     

SH3-SPOT: an algorithm to predict preferred ligands to different members of the SH3 gene family

We have developed a procedure to predict the peptide binding specificity of an SH3 domain from its sequence. The procedure utilizes information extracted from position-specific contacts derived from six SH3/peptide or SH3/protein complexes of known structure. The framework of SH3/peptide contacts defined on the structure of the complexes is used to build a residue-residue interaction database derived from ligands obtained by panning peptide libraries displayed on filamentous phage.The SH3-specific interaction database is a multidimensional array containing frequencies of position-specific contacts. As input, SH3-SPOT requires the sequence of an SH3 domain and of a query decapeptide ligand. The array, that we call the SH3-specific matrix, is then used to evaluate the probability that the peptide would bind the given SH3 domain. This procedure is fast enough to be applied to the entire protein sequence database.Panning experiments were performed to search putative specific ligands of different SH3 domains in a database of decapeptides, or in a database of protein sequences. The procedure ranked some of the natural partners of interaction of a number of SH3 domains among the best ligands of the approximately 5. 6x10(9) different decapeptides in the SWISSPROT database. We expect the predictive power of the method to increase with the enrichment of the SH3-specific matrix by interaction data derived from new complex structures or from the characterization of new ligands. The procedure was developed using the SH3 domain family as test case but its application can easily be extended to other families of protein domains (such as, SH2, MHC, EH, PDZ, etc.).     

Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method

Background  

Many processes in molecular biology involve the recognition of short sequences of nucleic-or amino acids, such as the binding of immunogenic peptides to major histocompatibility complex (MHC) molecules. From experimental data, a model of the sequence specificity of these processes can be constructed, such as a sequence motif, a scoring matrix or an artificial neural network. The purpose of these models is two-fold. First, they can provide a summary of experimental results, allowing for a deeper understanding of the mechanisms involved in sequence recognition. Second, such models can be used to predict the experimental outcome for yet untested sequences. In the past we reported the development of a method to generate such models called the Stabilized Matrix Method (SMM). This method has been successfully applied to predicting peptide binding to MHC molecules, peptide transport by the transporter associated with antigen presentation (TAP) and proteasomal cleavage of protein sequences.     

A computational pipeline to generate MHC binding motifs

BACKGROUND: Major histocompatibility complex (MHC) class I molecules play key roles in host immunity against pathogens by presenting peptide antigens to CD8+ T-cells. Many variants of MHC molecules exist, and each has a unique preference for certain peptide ligands. Both experimental approaches and computational algorithms have been utilized to analyze these peptide MHC binding characteristics. Traditionally, MHC binding specificities have been described in terms of binding motifs. Such motifs classify certain peptide positions as primary and secondary anchors according to their impact on binding, and they list the preferred and deleterious residues at these positions. This provides a concise and easily communicatable summary of MHC binding specificities. However, so far there has been no algorithm to generate such binding motifs in an automated and uniform fashion. In this paper, we present a computational pipeline that takes peptide MHC binding data as input and produces a concise MHC binding motif. We tested our pipeline on a set of 18 MHC class I molecules and showed that the derived motifs are consistent with historic expert assignments. We have implemented a pipeline that formally codifies rules to generate MHC binding motifs. The pipeline has been incorporated into the immune epitope database and analysis resource (IEDB) and motifs can be visualized while browsing MHC alleles in the IEDB.     

Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan

CD4 positive T helper cells control many aspects of specific immunity. These cells are specific for peptides derived from protein antigens and presented by molecules of the extremely polymorphic major histocompatibility complex (MHC) class II system. The identification of peptides that bind to MHC class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent. Validation of the method includes identification of endogenously derived HLA class II ligands, cross-validation, leave-one-molecule-out, and binding motif identification for hitherto uncharacterized HLA-DR molecules. The validation shows that the method can successfully predict binding for HLA-DR molecules-even in the absence of specific data for the particular molecule in question. Moreover, when compared to TEPITOPE, currently the only other publicly available prediction method aiming at providing broad HLA-DR allelic coverage, NetMHCIIpan performs equivalently for alleles included in the training of TEPITOPE while outperforming TEPITOPE on novel alleles. We propose that the method can be used to identify those hitherto uncharacterized alleles, which should be addressed experimentally in future updates of the method to cover the polymorphism of HLA-DR most efficiently. We thus conclude that the presented method meets the challenge of keeping up with the MHC polymorphism discovery rate and that it can be used to sample the MHC "space," enabling a highly efficient iterative process for improving MHC class II binding predictions.     

Structure of myosin heavy chain in avian muscular dystrophy

We have studied the structure of myosin heavy chain (MHC) in the pectoralis muscle of genetically dystrophic (Connecticut Strain) and White Leghorn chicks. MHC was alkylated with N-ethylmaleimide, purified by Sepharose-4B chromatography, and cleaved with cyanogen bromide. The MHC CNBr peptides were analyzed by one-dimensional and two-dimensional isoelectric focusing/sodium dodecyl sulfate gradient gels and by amino acid sequencing. Specific changes were detected in the gel patterns which could be correlated with the loss of muscle function as measured by the exhaustion score (the ability of chicks to rise from a reclining position) in three experimental groups (exhaustion scores: less than 3, 10-20, greater than 30). We have also examined the amino acid sequence of a 3-methyl-histidine-containing peptide which originates from the 20-kDa fragment of pectoralis muscle MHC in dystrophic chicks: Val-Leu-Asn-Ala-Ser-Ala-Ile-Pro-Glu-Gly-*Gln-Phe-*Ile-Asp-Ser-Lys-Lys- Ala-Ser-Leu-Gln-Lys-Leu-Gly-Ser-Ile-Asp-Val-(Asp, 3-methylhistidine, Gln). Comparison of the homologous MHC sequences shows two positions at which MHC from dystrophic chicks differs from that of the White Leghorn chicks *(Glu----Gln and Met----Ile). Thus, both the peptide map and sequence analyses demonstrate that in avian muscular dystrophy an abnormal pectoralis MHC is synthesized. It is not yet clear whether the "dystrophic" MHC is a variant MHC or if it arises from the abnormal expression of an earlier developmental form (embryonic or neonatal) of pectoralis muscle MHC.     

POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties

MOTIVATION: Both modeling of antigen-processing pathway including major histocompatibility complex (MHC) binding and immunogenicity prediction of those MHC-binding peptides are essential to develop a computer-aided system of peptide-based vaccine design that is one goal of immunoinformatics. Numerous studies have dealt with modeling the immunogenic pathway but not the intractable problem of immunogenicity prediction due to complex effects of many intrinsic and extrinsic factors. Moderate affinity of the MHC-peptide complex is essential to induce immune responses, but the relationship between the affinity and peptide immunogenicity is too weak to use for predicting immunogenicity. This study focuses on mining informative physicochemical properties from known experimental immunogenicity data to understand immune responses and predict immunogenicity of MHC-binding peptides accurately. RESULTS: This study proposes a computational method to mine a feature set of informative physicochemical properties from MHC class I binding peptides to design a support vector machine (SVM) based system (named POPI) for the prediction of peptide immunogenicity. High performance of POPI arises mainly from an inheritable bi-objective genetic algorithm, which aims to automatically determine the best number m out of 531 physicochemical properties, identify these m properties and tune SVM parameters simultaneously. The dataset consisting of 428 human MHC class I binding peptides belonging to four classes of immunogenicity was established from MHCPEP, a database of MHC-binding peptides (Brusic et al., 1998). POPI, utilizing the m = 23 selected properties, performs well with the accuracy of 64.72% using leave-one-out cross-validation, compared with two sequence alignment-based prediction methods ALIGN (54.91%) and PSI-BLAST (53.23%). POPI is the first computational system for prediction of peptide immunogenicity based on physicochemical properties. AVAILABILITY: A web server for prediction of peptide immunogenicity (POPI) and the used dataset of MHC class I binding peptides (PEPMHCI) are available at http://iclab.life.nctu.edu.tw/POPI     

Ranking of binding and nonbinding peptides to MHC class I molecules using inverse folding approach: Implications for vaccine design

T cell recognition of the peptide–MHC complex initiates a cascade of immunological events necessary for immune responses. Accurate T-cell epitope prediction is an important part of the vaccine designing. Development of predictive algorithms based on sequence profile requires a very large number of experimental binding peptide data to major histocompatibility complex (MHC) molecules. Here we used inverse folding approach to study the peptide specificity of MHC Class-I molecule with the aim of obtaining a better differentiation between binding and nonbinding sequence. 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. We used the Miyazawa & Jernigan and Betancourt & Thirumalai tables for pairwise contact potentials, and two distance criteria (Nearest atom ≫ 4.0 Å & C-beta ≫ 7.0 Å) for ranking the peptides in an ascending order according to their energy values, and in most cases, known antigenic peptides are highly ranked. The predictions from threading improved when used multiple templates and average scoring scheme. 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. The proposed scheme may significantly reduce the number of peptides to be tested in wet laboratory for epitope based vaccine design.     

Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta-algorithm.

MOTIVATION: The identification of T-cell epitopes can be crucial for vaccine development. An epitope is a peptide segment that binds to both a T-cell receptor and a major histocompatibility complex (MHC) molecule. Predicting which peptide segments bind MHC molecules is the first step in epitope prediction. RESULTS: An iterative stepwise discriminant analysis meta-algorithm explores a large molecular database to derive quantitative motifs for peptide binding. The applications presented here demonstrate the algorithm's versatility by producing four closely related models for HLA-DR1. Two models use an expert initial estimate and two do not; two models use amino acid residues as the only predictors and two use amino acid groupings as additional predictors. Each model correctly classifies >90% of the peptides in the database. AVAILABILITY: Software is available commercially; data are free over the Internet.     

Predicting class II MHC/peptide multi-level binding with an iterative stepwise discriminant analysis meta-algorithm.

MOTIVATION: Predicting peptides that bind to both Major Histocompatibility Complex (MHC) molecules and T cell receptors provides crucial information for vaccine development. An agretope is that portion of a peptide that interacts with an MHC molecule. The identification and prediction of agretopes is the first step towards vaccine design. RESULTS: An iterative stepwise discriminant analysis meta-algorithm is utilized to derive a quantitative motif for classifying potential agretopes as high-, moderate- or non-binders for HLA-DR1, a class II MHC molecule. A large molecular online database provides the input for this data-driven algorithm. The model correctly classifies over 85% of the peptides in the database. AVAILABILITY: Stepwise discriminant analysis software is available commercially in SPSS and BMDP statistical software packages. Peptides known to bind MHC molecules can be downloaded from http://wehih.wehi.edu.au/mhcpep/. Peptides known not to bind HLA-DR1 are available from the author upon request. CONTACT: ronna@ucsfresno.edu.     

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

Background  

The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable.     

SwePep, a database designed for endogenous peptides and mass spectrometry

A new database, SwePep, specifically designed for endogenous peptides, has been constructed to significantly speed up the identification process from complex tissue samples utilizing mass spectrometry. In the identification process the experimental peptide masses are compared with the peptide masses stored in the database both with and without possible post-translational modifications. This intermediate identification step is fast and singles out peptides that are potential endogenous peptides and can later be confirmed with tandem mass spectrometry data. Successful applications of this methodology are presented. The SwePep database is a relational database developed using MySql and Java. The database contains 4180 annotated endogenous peptides from different tissues originating from 394 different species as well as 50 novel peptides from brain tissue identified in our laboratory. Information about the peptides, including mass, isoelectric point, sequence, and precursor protein, is also stored in the database. This new approach holds great potential for removing the bottleneck that occurs during the identification process in the field of peptidomics. The SwePep database is available to the public.     

MHC class II binding of peptides derived from HLA-DR 1.

The aim of these studies was to determine whether auto- and alloreactivity can arise from T cell recognition of MHC-peptides in context of syngeneic MHC. Four synthetic peptides derived from the first domain of the HLA-DR beta 1 * 0101 chain were used in limiting dilution analysis to prime T cells from HLA-DR1- and HLA-DR1+ responders. The frequency of T cells responding to these four peptides was similar in individuals with or without HLA-DR1. In both cases, the peptide corresponding to the nonpolymorphic sequence 43-62, was less immunogenic than peptides corresponding to the three hypervariable regions 1-20, 21-42, and 66-90, eliciting a lower number of reactive T cells. Experiments using a T cell line with specific reactivity to peptide 21-42 showed, however, that this response can be efficiently blocked by adding to the culture a nonpolymorphic sequence peptide. This suggests that alloreactivity can be blocked by use of monomorphic (self) peptides. The binding of both "monomorphic" and "polymorphic" synthetic DR1 peptides to affinity purified HLA-DR 1 and DR 11 molecules was measured using radiolabeled peptides and high performance size exclusion chromatography. The data showed that the polymorphic as well as monomorphic synthetic DR1 peptides bound to both DR1 and DR11 molecules. Competitive inhibition studies indicated that the monomorphic 43-62 peptide can block the binding of the polymorphic peptides, consistent with the results obtained in T cell cultures. Taken together these data suggest that anti-MHC autoreactive T cells are present in the periphery and that both auto and alloreactivity can be elicited by MHC peptides binding to MHC class II molecules.     

Evolution and ecology of MHC molecules: from genomics to sexual selection

In the past few years the DNA sequence database for molecules of the MHC (major histocompatibility complex) has expanded greatly, yielding a more complete picture of the long-term rates and patterns of evolution of the MHC in vertebrates. Sharing of MHC allelic lineages between long-diverged species (trans-species evolution) has been detected virtually wherever it is sought, but new analyses of linked neutral regions and the complexities of sequence convergence and microrecombination in the peptide binding region challenge traditional phylogenetic analyses. Methods for estimating the intensity of selection on MHC genes suggest that viability is important, but recent studies in natural populations of mammals give inconsistent results concerning mate choice. The complex and interacting roles of microrecombination, parasite-mediated selection and mating preferences for maintaining the extraordinary levels of MHC polymorphism observed are still difficult to evaluate.