MHC binding prediction with KernelRLSpan and its variations

Induction of HLA-A2 restricted CD8 T cell responses against ApoB100 peptides does not affect atherosclerosis in a humanized mouse model

Cardiovascular diseases form the most common cause of death worldwide, with atherosclerosis as main etiology. Atherosclerosis is marked by cholesterol rich lipoprotein deposition in the artery wall, evoking a pathogenic immune response. Characteristic for the disease is the pathogenic accumulation of macrophages in the atherosclerotic lesion, which become foam cells after ingestion of large quantities of lipoproteins. We hypothesized that, by inducing a CD8 T cell response towards lipoprotein derived apolipoprotein-B100 (ApoB100), lesional macrophages, that are likely to cross-present lipoprotein constituents, can specifically be eliminated. Based on in silico models for protein processing and MHC-I binding, 6 putative CD8 T cell epitopes derived from ApoB100 were synthesized. HLA-A2 binding was confirmed for all peptides by T2 cell binding assays and recall responses after vaccination with the peptides proved that 5 of 6 peptides could induce CD8 T cell responses. Induction of ApoB100 specific CD8 T cells did not impact plaque size and cellular composition in HLA-A2 and human ApoB100 transgenic LDLr^−/− mice. No recall response could be detected in cultures of cells isolated from the aortic arch, which were observed in cell cultures of splenocytes and mesenteric lymph nodes, suggesting that the atherosclerotic environment impairs CD8 T cell activation.

Building proteomic tool boxes to monitor MHC class I and class II peptides

Major histocompatibility complex Class I (MHCI) and Class II (MHCII) presented peptides powerfully modulate T cell immunity and play a vital role in generating effective anti-tumor and anti-viral immune responses in mammals. Characterizing these MHCI or MHCII presented peptides can help generate therapeutic treatments, afford information on T cell mediated biomarkers, provide insight into disease progression, and reduce adverse anti-drug side effects from engineered biotherapeutics. Here, we explore the tools and techniques commonly employed to discover both MHCI- and MHCII-presented peptides. We describe complementary strategies that enhance the characterization of these peptides and the informatics tools employed for both predicting and characterizing MHCI- and MHCII-presented epitopes. The evolution of methodologies for isolating MHC-presented peptides is discussed, as are the mass spectrometric workflows that can be employed for their characterization. We provide a perspective on where this field is headed, and how these tools may be applicable to the discovery and monitoring of epitopes in a variety of scenarios.

Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules

MHC class II molecules play a fundamental role in the cellular immune system: they load short peptide fragments derived from extracellular proteins and present them on the cell surface. It is currently thought that the peptide binds lying more or less flat in the MHC groove, with a fixed distance of nine amino acids between the first and last residue in contact with the MHCII. While confirming that the great majority of peptides bind to the MHC using this canonical mode, we report evidence for an alternative, less common mode of interaction. A fraction of observed ligands were shown to have an unconventional spacing of the anchor residues that directly interact with the MHC, which could only be accommodated to the canonical MHC motif either by imposing a more stretched out peptide backbone (an 8mer core) or by the peptide bulging out of the MHC groove (a 10mer core). We estimated that on average 2% of peptides bind with a core deletion, and 0·45% with a core insertion, but the frequency of such non-canonical cores was as high as 10% for certain MHCII molecules. A mutational analysis and experimental validation of a number of these anomalous ligands demonstrated that they could only fit to their MHC binding motif with a non-canonical binding core of length different from nine. This previously undescribed mode of peptide binding to MHCII molecules gives a more complete picture of peptide presentation by MHCII and allows us to model more accurately this event.

Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation

Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.

NIEluter: Predicting peptides eluted from HLA class I molecules

The immune system has evolved to make a diverse repertoire of peptides processed from self and foreign proteomes, which are displayed in antigen-binding grooves of major histocompatibility complex (MHC) proteins at cell surface for surveillance by T cells. These antigenic peptides are termed Naturally Processed Peptides or Naturally Presented Peptides (NPPs), which play a major role in cell-mediated immunity and rational vaccine design. Therefore, it is intensely desirable to predict NPPs from a given protein antigen, or to foretell if an MHC-binding peptide can be eluted from a given MHC protein. In this paper, we describe NIEluter, an ensemble predictor based on support vector machine (SVM). It consists of a combination of five SVM models trained with position-specific amino acid composition, position-specific dipeptide composition, Hidden Markov Model, binary encoding, and BLOSUM62 feature. NIEluter can predict NPPs of length 8-11 from six HLA alleles (A0201, B0702, B3501, B4403, B5301, and B5701) at present. Evaluated with five-fold cross-validation and independent datasets if available, NIEluter shows good performance. It outperforms MHC-NP in 7 out of 24 types of situation and precedes NetMHC3.2 in most cases, indicating that it is a helpful complement to available tools. NIEluter has been implemented as a free web service, which can be accessed at http://immunet.cn/nie/cgi-bin/nieluter.pl.

Automated benchmarking of peptide-MHC class I binding predictions

MOTIVATION

Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study.

RESULTS

The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB.

AVAILABILITY AND IMPLEMENTATION

Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto_bench/mhci/join.

CONTACT

mniel@cbs.dtu.dk or bpeters@liai.org

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.