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.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

MHCPEP, a database of MHC-binding peptides: Update 1996

MHCPEP is a curated database comprising over 9000 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, when available, experimental method, observed activity, binding affinity, source protein, anchor positions and 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, FTP or Gopher.

Comparison of the binding and internalization properties of 12 DOTA-coupled and ¹¹¹In-labelled CCK2/gastrin receptor binding peptides: a collaborative project under COST Action BM0607

Specific overexpression of cholecystokinin 2 (CCK2)/gastrin receptors has been demonstrated in several tumours of neuroendocrine origin. In some of these cancer types, such as medullary thyroid cancer (MTC), a sensitive diagnostic modality is still unavailable and therapeutic options for inoperable lesions are needed. Peptide receptor radionuclide therapy (PRRT) may be a viable therapeutic strategy in the management of these patients. Several CCK2R-targeted radiopharmaceuticals have been described in recent years. As part of the European Union COST Action BM0607 we studied the in vitro and in vivo characteristics of 12 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)-conjugated CCK2R binding peptides. In the present study, we analysed binding and internalization characteristics. Stability, biodistribution and imaging studies have been performed in parallel by other centres involved in the project.

Evaluation of chemically modified SLA implants (modSLA) biofunctionalized with integrin (RGD)- and heparin (KRSR)-binding peptides

Enhancing osseointegration through surface immobilization of multiple short peptide sequences that mimic extracellular matrix (ECM) proteins, such as arginine-glycine-aspartic acid (RGD) and lysine-arginine-serine-arginine (KRSR), has not yet been extensively explored. Additionally, the effect of biofunctionalizing chemically modified sandblasted and acid-etched surfaces (modSLA) is unknown. The present study evaluated modSLA implant surfaces modified with RGD and KRSR for potentially enhanced effects on bone apposition and interfacial shear strength during early stages of bone regeneration. Two sets of experimental implants were placed in the maxillae of eight miniature pigs, known for their rapid wound healing kinetics: bone chamber implants creating two circular bone defects for histomorphometric analysis on one side and standard thread configuration implants for removal torque testing on the other side. Three different biofunctionalized modSLA surfaces using poly-L-lysine-graft-poly(ethylene glycol) (PLL-g-PEG) as a carrier minimizing nonspecific protein adsorption [(i) 20 pmol cm⁻² KRSR alone (KRSR); or in combination with RGD in two different concentrations; (ii) 0.05 pmol cm⁻² RGD (KRSR/RGD-1); (iii) 1.26 pmol cm⁻² RGD (KRSR/RGD-2)] were compared with (iv) control modSLA. Animals were sacrificed at 2 weeks. Removal torque values (701.48-780.28 N mm), bone-to-implant contact (BIC) (35.22%-41.49%), and new bone fill (28.58%-30.62%) demonstrated no significant differences among treatments. It may be concluded that biofunctionalizing modSLA surfaces with KRSR and RGD derivatives of PLL-g-PEG polymer does not increase BIC, bone fill, or interfacial shear strength.

Ca2+/calmodulin-binding peptides block phototransduction in Limulus ventral photoreceptors: Evidence for direct inhibition of phospholipase C

Phototransduction in Limulus photoreceptors involves a G protein-mediated activation of phospholipase C (PLC) and subsequent steps involving InsP3-mediated release of intracellular Ca2+. While exploring the role of calmodulin in this cascade, we found that intracellular injection of Ca2+/calmodulin-binding peptides (CCBPs) strongly inhibited the light response. By chemically exciting the cascade at various stages, we found the primary target of this effect was not in late stages of the cascade but rather at the level of G protein and PLC. That PLCδ1 contains a calmodulin-like structure raised the possibility that PLC might be directly affected by CCBPs. To test this possibility, in vitro experiments were conducted on purified PLC. The activity of this enzyme was strongly inhibited by CCBPs and also inhibited by calmodulin itself. Our results suggest that the calmodulin-like region of PLC has an important role in regulating this enzyme.

The use of mRNA display to select high-affinity protein-binding peptides

We report the use of “mRNA display,” an in vitro selection technique, to identify peptide aptamers to a protein target. mRNA display allows for the preparation of polypeptide libraries with far greater complexity than is possible with phage display. Starting with a library of ≈1013 random peptides, 20 different aptamers to streptavidin were obtained, with dissociation constants as low as 5 nM. These aptamers function without the aid of disulfide bridges or engineered scaffolds, yet possess affinities comparable to those for monoclonal antibody–antigen complexes. The aptamers bind streptavidin with three to four orders of magnitude higher affinity than those isolated previously by phage display from lower complexity libraries of shorter random peptides. Like previously isolated peptides, they contain an HPQ consensus motif. This study shows that, given sufficient length and diversity, high-affinity aptamers can be obtained even from random nonconstrained peptide libraries. By engineering structural constraints into these ultrahigh complexity peptide libraries, it may be possible to produce binding agents with subnanomolar binding constants.

The proteolytic fragments generated by vertebrate proteasomes: structural relationships to major histocompatibility complex class I binding peptides.

Proteasomes are involved in the proteolytic generation of major histocompatibility complex (MHC) class I epitopes but their exact role has not been elucidated. We used highly purified murine 20S proteasomes for digestion of synthetic 22-mer and 41/44-mer ovalbumin partial sequences encompassing either an immunodominant or a marginally immunogenic epitope. At various times, digests were analyzed by pool sequencing and by semiquantitative electrospray ionization mass spectrometry. Most dual cleavage fragments derived from 22-mer peptides were 7-10 amino acids long, with octa- and nonamers predominating. Digestion of 41/44-mer peptides initially revealed major cleavage sites spaced by two size ranges, 8 or 9 amino acids and 14 or 15 amino acids, followed by further degradation of the latter as well as of larger single cleavage fragments. The final size distribution was slightly broader than that of fragments derived from 22-mer peptides. The majority of peptide bonds were cleaved, albeit with vastly different efficiencies. This resulted in multiple overlapping proteolytic fragments including a limited number of abundant peptides. The immunodominant epitope was generated abundantly whereas only small amounts of the marginally immunogenic epitope were detected. The frequency distributions of amino acids flanking proteasomal cleavage sites are correlated to that reported for corresponding positions of MHC class I binding peptides. The results suggest that proteasomal degradation products may include fragments with structural properties similar to MHC class I binding peptides. Proteasomes may thus be involved in the final stages of proteolytic epitope generation, often without the need for downstream proteolytic events.

Structural variety of arginine-rich RNA-binding peptides.

Arginine-rich domains are used by a variety of RNA-binding proteins to recognize specific RNA hairpins. It has been shown previously that a 17-aa arginine-rich peptide from the human immunodeficiency virus Rev protein binds specifically to its RNA site when the peptide is in an alpha-helical conformation. Here we show that related peptides from splicing factors, viral coat proteins, and bacteriophage antiterminators (the N proteins) also have propensities to form alpha-helices and that the N peptides require helical conformations to bind to their cognate RNAs. In contrast, introducing proline mutations into the arginine-rich domain of the human immunodeficiency virus Tat protein abolishes its potential to form an alpha-helix but does not affect RNA-binding affinity in vitro or in vivo. Based on results from several peptide-RNA model systems, we suggest that helical peptides may be used to recognize RNA structures having particularly wide major grooves, such as those found near loops or large bulges, and that nonhelical or extended peptides may be used to recognize less accessible grooves.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.

Prediction of HLA-DQ3.2 beta ligands: evidence of multiple registers in class II binding peptides

Motivation: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. Methods: We describe a structure-based prediction model for modeling peptide-DQ3.2 beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2 beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2 beta binding and non-binding peptides obtained from biochemical and functional studies. Results: Our model predicts DQ3.2 beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2 beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

SVRMHC prediction server for MHC-binding peptides

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

Computational binding assays of antigenic peptides

Computer models can be combined with laboratory experiments for the efficient determination of (i) peptides that bind MHC molecules and (ii) T-cell epitopes. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures. This requires the definition of standards and experimental protocols for model application. We describe the requirements for validation and assessment of computer models. The utility of combining accurate predictions with a limited number of laboratory experiments is illustrated by practical examples. These include the identification of T-cell epitopes from IDDM-, melanoma- and malaria-related antigens by combining computational and conventional laboratory assays. The success rate in determining antigenic peptides, each in the context of a specific HLA molecule, ranged from 27 to 71%, while the natural prevalence of MHC-binding peptides is 0.1-5%.

Transporter associated with antigen processing preselection of peptides binding to the MHC:a bioinformatic evaluation

TAP is responsible for the transit of peptides from the cytosol to the lumen of the endoplasmic reticulum. In an immunological context, this event is followed by the binding of peptides to MHC molecules before export to the cell surface and recognition by T cells. Because TAP transport precedes MHC binding, TAP preferences may make a significant contribution to epitope selection. To assess the impact of this preselection, we have developed a scoring function for TAP affinity prediction using the additive method, have used it to analyze and extend the TAP binding motif, and have evaluated how well this model acts as a preselection step in predicting MHC binding peptides. To distinguish between MHC alleles that are exclusively dependent on TAP and those exhibiting only a partial dependence on TAP, two sets of MHC binding peptides were examined: HLA-A*0201 was selected as a representative of partially TAP-dependent HLA alleles, and HLA-A*0301 represented fully TAP-dependent HLA alleles. TAP preselection has a greater impact on TAP-dependent alleles than on TAP-independent alleles. The reduction in the number of nonbinders varied from 10% (TAP-independent) to 33% (TAP-dependent), suggesting that TAP preselection is an important component in the successful in silico prediction of T cell epitopes.

Coupling in silico and in vitro analysis of peptide-MHC binding:a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.