Immune epitope database analysis resource

The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages

Degenerate T-cell Recognition of Peptides on MHC Molecules Creates Large Holes in the T-cell Repertoire

The cellular immune system screens peptides presented by host cells on MHC molecules to assess if the cells are infected. In this study we examined whether the presented peptides contain enough information for a proper self/nonself assessment by comparing the presented human (self) and bacterial or viral (nonself) peptides on a large number of MHC molecules. For all MHC molecules tested, only a small fraction of the presented nonself peptides from 174 species of bacteria and 1000 viral proteomes (0.2%) is shown to be identical to a presented self peptide. Next, we use available data on T-cell receptor-peptide-MHC interactions to estimate how well T-cells distinguish between similar peptides. The recognition of a peptide-MHC by the T-cell receptor is flexible, and as a result, about one-third of the presented nonself peptides is expected to be indistinguishable (by T-cells) from presented self peptides. This suggests that T-cells are expected to remain tolerant for a large fraction of the presented nonself peptides, which provides an explanation for the “holes in the T-cell repertoire” that are found for a large fraction of foreign epitopes. Additionally, this overlap with self increases the need for efficient self tolerance, as many self-similar nonself peptides could initiate an autoimmune response. Degenerate recognition of peptide-MHC-I complexes by T-cells thus creates large and potentially dangerous overlaps between self and nonself

Detailed Molecular and Immune Marker Profiling of Archival Prostate Cancer Samples Reveals an Inverse Association between TMPRSS2:ERG Fusion Status and Immune Cell Infiltration

Prostate cancer is a significant global health issue and limitations to current patient management pathways often result in over- or under-treatment. New ways to stratify patients are urgently needed. We conducted a feasibility study of such novel assessments looking for associations between genomic changes and lymphocyte infiltration. An innovative workflow utilizing an in-house targeted sequencing panel, immune cell profiling using an image analysis pipeline, RNA-Seq, and exome sequencing in select cases was tested. Gene fusions were profiled by RNA-seq in 27/27 cases and a significantly higher TIL count was noted in tumors without a TMPRSS2:ERG fusion compared to those with the fusion (P = 0.01). Although this finding was not replicated in a larger validation set (n=436) of The Cancer Genome Atlas images, there was a trend in the same direction. Differential expression analysis of TIL-High and TIL-Low tumors revealed the enrichment of both innate and adaptive immune response pathways. Mutations in mismatch repair genes (MLH1 and MSH6 mutations in 1/27 cases) were identified. We describe a potential immune escape mechanism in TMPRSS2:ERG fusion positive tumors. Detailed profiling, as shown here, can provide novel insights into tumor biology. Likely differences with findings with other cohorts are related to methods used to define region of interest, but this warrants further study in a larger cohort

Development and Validation of an Epitope Prediction Tool for Swine (PigMatrix) Based on the Pocket Profile Method

Background: T cell epitope prediction tools and associated vaccine design algorithms have accelerated the development of vaccines for humans. Predictive tools for swine and other food animals are not as well developed, primarily because the data required to develop the tools are lacking. Here, we overcome a lack of T cell epitope data to construct swine epitope predictors by systematically leveraging available human information. Applying the “pocket profile method”, we use sequence and structural similarities in the binding pockets of human and swine major histocompatibility complex proteins to infer Swine Leukocyte Antigen (SLA) peptide binding preferences. We developed epitope-prediction matrices (PigMatrices), for three SLA class I alleles (SLA-1*0401, 2*0401 and 3*0401) and one class II allele (SLA-DRB1*0201), based on the binding preferences of the best-matched Human Leukocyte Antigen (HLA) pocket for each SLA pocket. The contact residues involved in the binding pockets were defined for class I based on crystal structures of either SLA (SLA-specific contacts, Ssc) or HLA supertype alleles (HLA contacts, Hc); for class II, only Hc was possible. Different substitution matrices were evaluated (PAM and BLOSUM) for scoring pocket similarity and identifying the best human match. The accuracy of the PigMatrices was compared to available online swine epitope prediction tools such as PickPocket and NetMHCpan. Results: PigMatrices that used Ssc to define the pocket sequences and PAM30 to score pocket similarity demonstrated the best predictive performance and were able to accurately separate binders from random peptides. For SLA-1*0401 and 2*0401, PigMatrix achieved area under the receiver operating characteristic curves (AUC) of 0.78 and 0.73, respectively, which were equivalent or better than PickPocket (0.76 and 0.54) and NetMHCpan version 2.4 (0.41 and 0.51) and version 2.8 (0.72 and 0.71). In addition, we developed the first predictive SLA class II matrix, obtaining an AUC of 0.73 for existing SLA-DRB1*0201 epitopes. Notably, PigMatrix achieved this level of predictive power without training on SLA binding data. Conclusions: Overall, the pocket profile method combined with binding preferences from HLA binding data shows significant promise for developing T cell epitope prediction tools for pigs. When combined with existing vaccine design algorithms, PigMatrix will be useful for developing genome-derived vaccines for a range of pig pathogens for which no effective vaccines currently exist (e.g. porcine reproductive and respiratory syndrome, influenza and porcine epidemic diarrhea)

Improved Ribo-seq enables identification of cryptic translation events.

Ribosome profiling has been used to predict thousands of short open reading frames (sORFs) in eukaryotic cells, but it suffers from substantial levels of noise. PRICE (https://github.com/erhard-lab/price) is a computational method that models experimental noise to enable researchers to accurately resolve overlapping sORFs and noncanonical translation initiation. We experimentally validated translation using major histocompatibility complex class I (MHC I) peptidomics and observed that sORF-derived peptides efficiently enter the MHC I presentation pathway and thus constitute a substantial fraction of the antigen repertoire

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

Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide-MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0 .Fil: Karosiene, Edita. Technical University Of Denmark; DinamarcaFil: Rasmussen, Michael. Universidad de Copenhagen; DinamarcaFil: Blicher, Thomas. Universidad de Copenhagen; DinamarcaFil: Lund, Ole. Technical University Of Denmark; DinamarcaFil: Buus, Soren. Universidad de Copenhagen; DinamarcaFil: Nielsen, Morten. Universidad Nacional de San Martín; Argentina. Technical University Of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Fisicoquímica Biológicas; Argentin