Positional Scanning Combinatorial Peptide Libraries Research Articles

GPU-Accelerated Discovery of Pathogen-Derived Molecular Mimics of a T-Cell Insulin Epitope.

The strong links between (Human Leukocyte Antigen) HLA, infection and autoimmunity combine to implicate T-cells as primary triggers of autoimmune disease (AD). T-cell crossreactivity between microbially-derived peptides and self-peptides has been shown to break tolerance and trigger AD in experimental animal models. Detailed examination of the potential for T-cell crossreactivity to trigger human AD will require means of predicting which peptides might be recognised by autoimmune T-cell receptors (TCRs). Recent developments in high throughput sequencing and bioinformatics mean that it is now possible to link individual TCRs to specific pathologies for the first time. Deconvolution of TCR function requires knowledge of TCR specificity. Positional Scanning Combinatorial Peptide Libraries (PS-CPLs) can be used to predict HLA-restriction and define antigenic peptides derived from self and pathogen proteins. In silico search of the known terrestrial proteome with a prediction algorithm that ranks potential antigens in order of recognition likelihood requires complex, large-scale computations over several days that are infeasible on a personal computer. We decreased the time required for peptide searching to under 30 min using multiple blocks on graphics processing units (GPUs). This time-efficient, cost-effective hardware accelerator was used to screen bacterial and fungal human pathogens for peptide sequences predicted to activate a T-cell clone, InsB4, that was isolated from a patient with type 1 diabetes and recognised the insulin B-derived epitope HLVEALYLV in the context of disease-risk allele HLA A*0201. InsB4 was shown to kill HLA A*0201+ human insulin producing β-cells demonstrating that T-cells with this specificity might contribute to disease. The GPU-accelerated algorithm and multispecies pathogen proteomic databases were validated to discover pathogen-derived peptide sequences that acted as super-agonists for the InsB4 T-cell clone. Peptide-MHC tetramer binding and surface plasmon resonance were used to confirm that the InsB4 TCR bound to the highest-ranked peptide agonists derived from infectious bacteria and fungi. Adoption of GPU-accelerated prediction of T-cell agonists has the capacity to revolutionise our understanding of AD by identifying potential targets for autoimmune T-cells. This approach has further potential for dissecting T-cell responses to infectious disease and cancer.

Prediction and in vitro verification of potential CTL epitopes conserved among PRRSV-2 strains

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is the causative agent of one of the most important porcine diseases with a high impact on animal health, welfare, and production economy. PRRSV exhibits a multitude of immunoevasive strategies that, in combination with a very high mutation rate, has hampered the development of safe and broadly protective vaccines. Aiming at a vaccine inducing an effective cytotoxic T cell response, a bioinformatics approach was taken to identify conserved PRRSV-derived peptides predicted to react broadly with common swine leukocyte antigen (SLA) class I alleles. Briefly, all possible 9- and 10-mer peptides were generated from 104 complete PRRSV type 2 genomes of confirmed high quality, and peptides with high binding affinity to five common SLAs were identified combining the NetMHCpan and positional scanning combinatorial peptide libraries binding predictions. Predicted binders were prioritized according to genomic conservation and SLA coverage using the PopCover algorithm. From this, 53 peptides were acquired for further analysis. Binding affinity and stability of a subset of 101 peptide-SLA combinations were validated in vitro for 4 of the 5 SLAs. Eventually, 23% of the predicted peptide-SLA combinations showed to form complexes with a dissociation half-life ≥30 min. Additionally, combining the two prediction methods proved to be more robust across alleles than either method used alone in terms of predicted-to-observed correlations. In summary, our approach represents a finely tuned epitope prediction pipeline providing a rationally selected ensemble of peptides for future in vivo experiments with pigs expressing the included SLAs.

A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01.

Affinity and stability of peptides bound by major histocompatibility complex (MHC) class I molecules are important factors in presentation of peptides to cytotoxic T lymphocytes (CTLs). In silico prediction methods of peptide-MHC binding followed by experimental analysis of peptide-MHC interactions constitute an attractive protocol to select target peptides from the vast pool of viral proteome peptides. We have earlier reported the peptide binding motif of the porcine MHC-I molecules SLA-1*04:01 and SLA-2*04:01, identified by an ELISA affinity-based positional scanning combinatorial peptide library (PSCPL) approach. Here, we report the peptide binding motif of SLA-3*04:01 and combine two prediction methods and analysis of both peptide binding affinity and stability of peptide-MHC complexes to improve rational peptide selection. Using a peptide prediction strategy combining PSCPL binding matrices and in silico prediction algorithms (NetMHCpan), peptide ligands from a repository of 8900 peptides were predicted for binding to SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01 and validated by affinity and stability assays. From the pool of predicted peptides for SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01, a total of 71, 28, and 38% were binders with affinities below 500 nM, respectively. Comparison of peptide-SLA binding affinity and complex stability showed that peptides of high affinity generally, but not always, produce complexes of high stability. In conclusion, we demonstrate how state-of-the-art prediction and in vitro immunology tools in combination can be used for accurate selection of peptides for MHC class I binding, hence providing an expansion of the field of peptide-MHC analysis also to include pigs as a livestock experimental model.

A modern approach for epitope prediction: identification of foot-and-mouth disease virus peptides binding bovine leukocyte antigen (BoLA) class I molecules.

Major histocompatibility complex (MHC) class Imolecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8+ T cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presentation, and different antigen peptide motifs are associated with specific genetic sequences of class I molecules. Understanding bovine leukocyte antigen (BoLA), peptide-MHC class I binding specificities may facilitate development of vaccines or reagents for quantifying the adaptive immune response to intracellular pathogens, such as foot-and-mouth disease virus (FMDV). Six synthetic BoLA class I (BoLA-I) molecules were produced, and the peptide binding motif was generated for five of the six molecules using a combined approach of positional scanning combinatorial peptide libraries (PSCPLs) and neural network-based predictions (NetMHCpan). The updated NetMHCpan server was used to predict BoLA-I binding peptides within the P1 structural polyprotein sequence of FMDV (strain A24 Cruzeiro) for Bo-LA-1*01901, BoLA-2*00801, BoLA-2*01201, and BoLA-4*02401. Peptide binding affinity and stability were determined for these BoLA-I molecules using the luminescent oxygen channeling immunoassay (LOCI) and scintillation proximity assay (SPA). The functional diversity of known BoLA alleles was predicted using theMHCcluster tool, and functional predictions for peptide motifs were compared to observed data from this and prior studies. The results of these analyses showed that BoLA alleles cluster into three distinct groups with the potential to define BBoLA supertypes.^ This streamlined approach identifies potential T cell epitopes from pathogens, such as FMDV, and provides insight into T cell immunity following infection or vaccination.

Uncovering the peptide-binding specificities of HLA-C: a general strategy to determine the specificity of any MHC class I molecule.

MHC class I molecules (HLA-I in humans) present peptides derived from endogenous proteins to CTLs. Whereas the peptide-binding specificities of HLA-A and -B molecules have been studied extensively, little is known about HLA-C specificities. Combining a positional scanning combinatorial peptide library approach with a peptide-HLA-I dissociation assay, in this study we present a general strategy to determine the peptide-binding specificity of any MHC class I molecule. We applied this novel strategy to 17 of the most common HLA-C molecules, and for 16 of these we successfully generated matrices representing their peptide-binding motifs. The motifs prominently shared a conserved C-terminal primary anchor with hydrophobic amino acid residues, as well as one or more diverse primary and auxiliary anchors at P1, P2, P3, and/or P7. Matrices were used to generate a large panel of HLA-C-specific peptide-binding data and update our pan-specific NetMHCpan predictor, whose predictive performance was considerably improved with respect to peptide binding to HLA-C. The updated predictor was used to assess the specificities of HLA-C molecules, which were found to cover a more limited sequence space than HLA-A and -B molecules. Assessing the functional significance of these new tools, HLA-C*07:01 transgenic mice were immunized with stable HLA-C*07:01 binders; six of six tested stable peptide binders were immunogenic. Finally, we generated HLA-C tetramers and labeled human CD8(+) T cells and NK cells. These new resources should support future research on the biology of HLA-C molecules. The data are deposited at the Immune Epitope Database, and the updated NetMHCpan predictor is available at the Center for Biological Sequence Analysis and the Immune Epitope Database.

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.

Identification of peptides from foot‐and‐mouth disease virus structural proteins bound by class I swine leukocyte antigen (SLA) alleles, SLA‐1*0401 and SLA‐2*0401

Characterization of the peptide-binding specificity of swine leukocyte antigen (SLA) class I and II molecules is critical to the understanding of adaptive immune responses of swine toward infectious pathogens. Here, we describe the complete binding motif of the SLA-2*0401 molecule based on a positional scanning combinatorial peptide library approach. By combining this binding motif with data achieved by applying the NetMHCpan peptide prediction algorithm to both SLA-1*0401 and SLA-2*0401, we identified high-affinity binding peptides. A total of 727 different 9mer and 726 different 10mer peptides within the structural proteins of foot-and-mouth disease virus (FMDV), strain A24 were analyzed as candidate T-cell epitopes. Peptides predicted by the NetMHCpan were tested in ELISA for binding to the SLA-1*0401 and SLA-2*0401 major histocompatibility complex class I proteins. Four of the 10 predicted FMDV peptides bound to SLA-2*0401, whereas five of the nine predicted FMDV peptides bound to SLA-1*0401. These methods provide the characterization of T-cell epitopes in response to pathogens in more detail. The development of such approaches to analyze vaccine performance will contribute to a more accelerated improvement of livestock vaccines by virtue of identifying and focusing analysis on bona fide T-cell epitopes.

Describing the Peptide Binding Specificity of HLA-C (106.41)

Abstract Human leukocyte antigen (HLA) presents peptides to T-cells for immune scrutiny. Whereas HLA-A and -B have been described in great detail, HLA-C has received much less attention. Here, to increase the coverage of HLA-C and the accuracy of the corresponding tools, we have generated HLA-C molecules; peptide-binding assays, data and predictors; and tetramers; representing the most prevalent HLA-C molecules. We have combined positional scanning combinatorial peptide library (PSCPL) with a homogenous high-throughput dissociation assay and generated specificity matrices for 11 different HLA-C molecules. We find preference for hydrophobic residues at the peptide C-terminus for all HLA-C molecules. Most molecules were found to have an additional strong anchor at P2 or P3, with auxiliary anchor observed at P1, P2, P3, and P7. The binding affinity is measured for peptides fitting the specificity matrix for 5 HLA-C molecules and for all, but one, molecule we find a high frequency of binders, >70%, among these peptides. To extend the examined peptide space, we use bioinformatic prediction tools to search for additional binders. Finally, we update our prediction tool, NetMHCpan, with the HLA-C affinity data and show that the predictive performance for HLA-C molecules now is increased to a level comparable withthat of HLA-A and -B. These novel HLA-C molecules and predictors are successfully used to generate HLA-C tetramers and validate HLA-C-restricted T cell responses.

Defining the Substrate Specificity Determinants Recognized by the Active Site of C-Terminal Src Kinase-Homologous Kinase (CHK) and Identification of β-Synuclein as a Potential CHK Physiological Substrate

C-Terminal Src kinase-homologous kinase (CHK) exerts its tumor suppressor function by phosphorylating the C-terminal regulatory tyrosine of the Src-family kinases (SFKs). The phosphorylation suppresses their activity and oncogenic action. In addition to phosphorylating SFKs, CHK also performs non-SFK-related functions by phosphorylating other cellular protein substrates. To define these non-SFK-related functions of CHK, we used the "kinase substrate tracking and elucidation" method to search for its potential physiological substrates in rat brain cytosol. Our search revealed β-synuclein as a potential CHK substrate, and Y127 in β-synuclein as the preferential phosphorylation site. Using peptides derived from β-synuclein and positional scanning combinatorial peptide library screening, we defined the optimal substrate phosphorylation sequence recognized by the CHK active site to be E-x-[Φ/E/D]-Y-Φ-x-Φ, where Φ and x represent hydrophobic residues and any residue, respectively. Besides β-synuclein, cellular proteins containing motifs resembling this sequence are potential CHK substrates. Intriguingly, the CHK-optimal substrate phosphorylation sequence bears little resemblance to the C-terminal tail sequence of SFKs, indicating that interactions between the CHK active site and the local determinants near the C-terminal regulatory tyrosine of SFKs play only a minor role in governing specific phosphorylation of SFKs by CHK. Our results imply that recognition of SFKs by CHK is mainly governed by interactions between motifs located distally from the active site of CHK and determinants spatially separate from the C-terminal regulatory tyrosine in SFKs. Thus, besides assisting in the identification of potential CHK physiological substrates, our findings shed new light on how CHK recognizes SFKs and other protein substrates.

Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities

In all vertebrate animals, CD8+ cytotoxic T lymphocytes (CTLs) are controlled by major histocompatibility complex class I (MHC-I) molecules. These are highly polymorphic peptide receptors selecting and presenting endogenously derived epitopes to circulating CTLs. The polymorphism of the MHC effectively individualizes the immune response of each member of the species. We have recently developed efficient methods to generate recombinant human MHC-I (also known as human leukocyte antigen class I, HLA-I) molecules, accompanying peptide-binding assays and predictors, and HLA tetramers for specific CTL staining and manipulation. This has enabled a complete mapping of all HLA-I specificities (“the Human MHC Project”). Here, we demonstrate that these approaches can be applied to other species. We systematically transferred domains of the frequently expressed swine MHC-I molecule, SLA-1*0401, onto a HLA-I molecule (HLA-A*11:01), thereby generating recombinant human/swine chimeric MHC-I molecules as well as the intact SLA-1*0401 molecule. Biochemical peptide-binding assays and positional scanning combinatorial peptide libraries were used to analyze the peptide-binding motifs of these molecules. A pan-specific predictor of peptide–MHC-I binding, NetMHCpan, which was originally developed to cover the binding specificities of all known HLA-I molecules, was successfully used to predict the specificities of the SLA-1*0401 molecule as well as the porcine/human chimeric MHC-I molecules. These data indicate that it is possible to extend the biochemical and bioinformatics tools of the Human MHC Project to other vertebrate species.Electronic supplementary materialThe online version of this article (doi:10.1007/s00251-011-0555-3) contains supplementary material, which is available to authorized users.

In vivo discovery of a peptide that prevents CUG–RNA hairpin formation and reverses RNA toxicity in myotonic dystrophy models

Myotonic dystrophy type 1 (DM1) is caused by the expansion of noncoding CTG repeats in the dystrophia myotonica-protein kinase gene. Mutant transcripts form CUG hairpins that sequester RNA-binding factors into nuclear foci, including Muscleblind-like-1 protein (MBNL1), which regulate alternative splicing and gene expression. To identify molecules that target toxic CUG transcripts in vivo, we performed a positional scanning combinatorial peptide library screen using a Drosophila model of DM1. The screen identified a D-amino acid hexapeptide (ABP1) that reduced CUG foci formation and suppressed CUG-induced lethality and muscle degeneration when administered orally. Transgenic expression of natural, L-amino acid ABP1 analogues reduced CUG-induced toxicity in fly eyes and muscles. Furthermore, ABP1 reversed muscle histopathology and splicing misregulation of MBNL1 targets in DM1 model mice. In vitro, ABP1 bound to CUG hairpins and induced a switch to a single-stranded conformation. Our findings demonstrate that ABP1 shows antimyotonic dystrophy activity by targeting the core of CUG toxicity.

Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries.

BackgroundIt has been previously shown that combinatorial peptide libraries are a useful tool to characterize the binding specificity of class I MHC molecules. Compared to other methodologies, such as pool sequencing or measuring the affinities of individual peptides, utilizing positional scanning combinatorial libraries provides a baseline characterization of MHC molecular specificity that is cost effective, quantitative and unbiased.ResultsHere, we present a large-scale application of this technology to 19 different human and mouse class I alleles. These include very well characterized alleles (e.g. HLA A*0201), alleles with little previous data available (e.g. HLA A*3201), and alleles with conflicting previous reports on specificity (e.g. HLA A*3001). For all alleles, the positional scanning combinatorial libraries were able to elucidate distinct binding patterns defined with a uniform approach, which we make available here. We introduce a heuristic method to translate this data into classical definitions of main and secondary anchor positions and their preferred residues. Finally, we validate that these matrices can be used to identify candidate MHC binding peptides and T cell epitopes in the vaccinia virus and influenza virus systems, respectively.ConclusionThese data confirm, on a large scale, including 15 human and 4 mouse class I alleles, the efficacy of the positional scanning combinatorial library approach for describing MHC class I binding specificity and identifying high affinity binding peptides. These libraries were shown to be useful for identifying specific primary and secondary anchor positions, and thereby simpler motifs, analogous to those described by other approaches. The present study also provides matrices useful for predicting high affinity binders for several alleles for which detailed quantitative descriptions of binding specificity were previously unavailable, including A*3001, A*3201, B*0801, B*1501 and B*1503.

Inhibition of Furin by Polyarginine-containing Peptides: NANOMOLAR INHIBITION BY NONA-d-ARGININE

Polyarginine-containing peptides represent potent inhibitors of furin, a mammalian endoprotease that plays an important role in metabolism, activation of pathogenic toxins, and viral proliferation. The therapeutic use of D-polyarginines is especially interesting because they are not cleaved by furin and possess inhibitory potency almost equal to L-polyarginines. In this study we attempted to determine the important elements within polyarginines that contribute to effective inhibition. Structure-function analyses of polyarginine peptides showed that inhibition by polyarginine-containing peptides appeared to depend on the total number of basic charges of the positively charged inhibitors bound to the negatively charged substrate binding pocket; peptide positioning did not appear to be rigorously determined. Screening of L- and D-decapeptide positional scanning combinatorial peptide libraries indicated a preference for basic residues in nearly all positions, similar to previous results with hexapeptide libraries. Length and terminal modification studies showed that the most potent D-polyarginine tested was nona-D-arginine (D9R) amide with a K(i) of 1.3 nm. D9R amide was shown to protect RAW264.7 cells against anthrax toxemia with an IC(50) of 3.7 microm. Because of its high stability, specificity, low toxicity, small molecular weight, and extremely low K(i) against furin, D9R amide or its derivatives may represent promising compounds for therapeutic use.

Antigenicity and immunogenicity of peptide analogues of a low affinity peptide of the human telomerase reverse transcriptase tumor antigen.

Human telomerase reverse transcriptase (hTRT) is a potential target for therapeutic vaccination against cancer. Therefore, it is critically important to identify T cell epitopes useful to induce cytotoxic T cell responses. Here we used a positional scanning combinatorial peptide library to identify peptide analogues for a previously characterized low affinity hTRT peptide (p572). From an initial library containing over 300 billion different peptides and through successive rounds of selection, we retained 72 candidate peptide analogues for further assessment of antigenicity and in vivo immunogenicity in HLA A2.1-transgenic mice. While antigenically cross-reactive with p572, only a fraction of these peptides was immunogenic in mice. Immunogenicity appeared to correlate with the stability of binding to the MHC molecule and the presence of HLA A2.1 anchor residues in position 2 and 9. Two peptides differing by five residues from the reference p572 (p49 and p50) were more effective than p572 in inducing CTL cross-reacting with p572 in HLA A2.1-transgenic mice. Both peptides also expanded specific CTL in peripheral blood lymphocytes of normal human volunteers ex vivo. The present study shows that positional scanning combinatorial peptide libraries can be used to identify hTRT peptide analogues for inclusion in a cancer vaccine.

Peptide binding specificity of major histocompatibility complex class I resolved into an array of apparently independent subspecificities: quantitation by peptide libraries and improved prediction of binding.

Considerable interest has focused on understanding how major histocompatibility complex (MHC) specificity is generated and characterizing the specificity of MHC molecules with the ultimate goal being to predict peptide binding. We have used a strategy where all possible peptides of a particular size are distributed into positional scanning combinatorial peptide libraries (PSCPL) to develop a highly efficient, universal and unbiased approach to address MHC specificity. The PSCPL approach appeared qualitatively and quantitatively superior to other currently used strategies. The average effect of any amino acid in each position was quantitated, allowing a detailed description of extended peptide binding motifs including primary and secondary anchor residues. It also identified disfavored residues which were found to be surprisingly important in shaping MHC class I specificity. Assuming that MHC class I specificity is the result of largely independently acting subsites, the binding of unknown peptides could be predicted. Conversely, this argues that MHC class I specificities consist of an array of subspecificities acting in a combinatorial mode.