T-cell epitope prediction and immune complex simulation using molecular dynamics:state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.

Class I T-cell epitope prediction:improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding

Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.

EpiJen:a server for multistep T cell epitope prediction

Background - The main processing pathway for MHC class I ligands involves degradation of proteins by the proteasome, followed by transport of products by the transporter associated with antigen processing (TAP) to the endoplasmic reticulum (ER), where peptides are bound by MHC class I molecules, and then presented on the cell surface by MHCs. The whole process is modeled here using an integrated approach, which we call EpiJen. EpiJen is based on quantitative matrices, derived by the additive method, and applied successively to select epitopes. EpiJen is available free online. Results - To identify epitopes, a source protein is passed through four steps: proteasome cleavage, TAP transport, MHC binding and epitope selection. At each stage, different proportions of non-epitopes are eliminated. The final set of peptides represents no more than 5% of the whole protein sequence and will contain 85% of the true epitopes, as indicated by external validation. Compared to other integrated methods (NetCTL, WAPP and SMM), EpiJen performs best, predicting 61 of the 99 HIV epitopes used in this study. Conclusion - EpiJen is a reliable multi-step algorithm for T cell epitope prediction, which belongs to the next generation of in silico T cell epitope identification methods. These methods aim to reduce subsequent experimental work by improving the success rate of epitope prediction.

Immunoinformatic evaluation of multiple epitope ensembles as vaccine candidates:E coli 536

Epitope prediction is becoming a key tool for vaccine discovery. Prospective analysis of bacterial and viral genomes can identify antigenic epitopes encoded within individual genes that may act as effective vaccines against specific pathogens. Since B-cell epitope prediction remains unreliable, we concentrate on T-cell epitopes, peptides which bind with high affinity to Major Histacompatibility Complexes (MHC). In this report, we evaluate the veracity of identified T-cell epitope ensembles, as generated by a cascade of predictive algorithms (SignalP, Vaxijen, MHCPred, IDEB, EpiJen), as a candidate vaccine against the model pathogen uropathogenic gram negative bacteria Escherichia coli (E-coli) strain 536 (O6:K15:H31). An immunoinformatic approach was used to identify 23 epitopes within the E-coli proteome. These epitopes constitute the most promiscuous antigenic sequences that bind across more than one HLA allele with high affinity (IC50 <50nM). The reliability of software programmes used, polymorphic nature of genes encoding MHC and what this means for population coverage of this potential vaccine are discussed.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments – data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures – offer hope for the future.

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

MHCPred : a server for quantitative prediction of peptide-MHC binding

Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at large, we have implemented, as a server on the World Wide Web, a partial least squares-base multivariate statistical approach to the quantitative prediction of peptide binding to major histocom-patibility complexes (MHC), the key checkpoint on the antigen presentation pathway within adaptive,cellular immunity. MHCPred implements robust statistical models for both Class I alleles (HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203,HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3301, HLA-A*6801, HLA-A*6802 and HLA-B*3501) and Class II alleles (HLA-DRB*0401, HLA-DRB*0401and HLA-DRB* 0701).

Protein expression of Myt272-3 recombinant clone and in silico prediction of a possible vaccine candidate against Mycobacterium tuberculosis

Purpose: To investigate the expression of Myt272-3 recombinant protein and also to predict a possible protein vaccine candidate against Mycobacterium tuberculosis . Methods: Myt272-3 protein was expressed in pET30a+-Myt272-3 clone. The purity of the protein was determined using Dynabeads® His-Tag Isolation & Pulldown. Protein sequence was analysed in silico using bioinformatics software for the prediction of allergenicity, antigenicity, MHC-I and MHC-II binding, and B-cell epitope binding. Results: The candidate protein was a non-allergen with 15.19 % positive predictive value. It was also predicted to be antigenic, with binding affinity to MHC-I and MHC-II, as well as B-cell epitope binding. Conclusion: The predicted results obtained in this study provide a guide for practical design of a new tuberculosis vaccine.

Mapping the molecular characteristics of Brazilian human T-cell lymphotropic virus type 1 Env (gp46) and Pol amino acid sequences for vaccine design

This study was carried out to evaluate the molecular pattern of all available Brazilian human T-cell lymphotropic virus type 1 Env (n = 15) and Pol (n = 43) nucleotide sequences via epitope prediction, physico-chemical analysis, and protein potential sites identification, giving support to the Brazilian AIDS vaccine program. In 12 previously described peptides of the Env sequences we found 12 epitopes, while in 4 peptides of the Pol sequences we found 4 epitopes. The total variation on the amino acid composition was 9 and 17% for human leukocyte antigen (HLA) class I and class II Env epitopes, respectively. After analyzing the Pol sequences, results revealed a total amino acid variation of 0.75% for HLA-I and HLA-II epitopes. In 5 of the 12 Env epitopes the physico-chemical analysis demonstrated that the mutations magnified the antigenicity profile. The potential protein domain analysis of Env sequences showed the loss of a CK-2 phosphorylation site caused by D197N mutation in one epitope, and a N-glycosylation site caused by S246Y and V247I mutations in another epitope. Besides, the analysis of selection pressure have found 8 positive selected sites (w = 9.59) using the codon-based substitution models and maximum-likelihood methods. These studies underscore the importance of this Env region for the virus fitness, for the host immune response and, therefore, for the development of vaccine candidates.

Combining MHC tetramer and intracellular cytokine staining for CD8(+) T cells to reveal antigenic epitopes naturally presented on tumor cells.

As more tumor antigens are discovered and as computer-guided T cell epitope prediction programs become more sophisticated, many potential T cell epitopes are synthesized and demonstrated to be antigenic in vitro. However, it is estimated that about 50% of such tumor antigen-specific T cells have not been demonstrated to recognize the naturally presented epitopes due to either technical difficulties, such as T cell cloning which is still challenging for many laboratories; or the predicted T cell epitopes are not generated or not generated in sufficient amounts by the antigen processing machinery. However, to potentially identify clinically relevant vaccine candidate epitopes, it is essential to demonstrate natural antigen presentation. Here we combine the advantages of MHC tetramer and intracellular cytokine staining to sensitively detect natural antigen presentation by tumor cells for epitopes of interest. The novel method does not require T cell cloning or long-term T cell culture. Because the antigen-specific T cells are positively identified, this method is much less influenced by IFNgamma producing cells with unknown specificities and should be widely applicable.

Sequence analysis of the 5′ third of glycoprotein C gene of South American bovine herpesviruses 1 and 5

Bovine herpesviruses 1 (BoHV-1) and 5 (BoHV-5) share high genetic and antigenic similarities, but exhibit marked differences in tissue tropism and neurovirulence. The amino-terminal region of glycoprotein C (gC), which is markedly different in each of the viruses, is involved in virus binding to cellular receptors and in interactions with the immune system. This study investigated the genetic and antigenic differences of the 5′ region of the gC (5′ gC) gene (amino-terminal) of South American BoHV-1 (n=19) and BoHV-5 (n=25) isolates. Sequence alignments of 374 nucleotides (104 amino acids) revealed mean similarity levels of 97.3 and 94.2% among BoHV-1 gC (gC1), respectively, 96.8 and 95.6% among BoHV-5 gC (gC5), and 62 and 53.3% between gC1 and gC5. Differences included the absence of 40 amino acid residues (27 encompassing predicted linear epitopes) scattered throughout 5′ gC1 compared to 5′ gC5. Virus neutralizing assays testing BoHV-1 and BoHV-5 antisera against each isolate revealed a high degree of cross-neutralization between the viruses, yet some isolates were neutralized at very low titers by heterologous sera, and a few BoHV-5 isolates reacted weakly with either sera. The virus neutralization differences observed within the same viral species, and more pronounced between BoHV-1 and BoHV-5, likely reflect sequence differences in neutralizing epitopes. These results demonstrate that the 5′ gC region is well conserved within each viral species but is divergent between BoHV-1 and BoHV-5, likely contributing to their biological and antigenic differences.

Studying MHC I-dependent CD8 + T cell responses in the model of murine cutaneous leishmaniasis

Der Ausheilung von Infektionen mit Leishmania major liegt die Sekretion von IFN- von sowohl CD4+ als auch CD8+ T Zellen zugrunde.rnAktuell konnte in der Literatur nur ein Epitop aus dem parasitären LACK Protein für eine effektive CD4+ T Zell-vermittelte Immunantwort beschrieben werden. Das Ziel der vorliegenden Arbeit bestand daher darin, mögliche MHC I abhängige CD8+ T Zell Antworten zu untersuchen. rnFür diesen Ansatz wurde als erstes der Effekt einer Vakzinierung mit LACK Protein fusioniert an die Protein-Transduktionsdomäne des HIV-1 (TAT) analysiert. Die Effektivität von TAT-LACK gegenüber CD8+ T Zellen wurde mittels in vivo Protein-Vakzinierung von resistenten C57BL/6 Mäusen in Depletions-Experimenten gezeigt.rnDie Prozessierung von Proteinen vor der Präsentation immunogener Peptide gegenüber T Zellen ist unbedingt erforderlich. Daher wurde in dieser Arbeit die Rolle des IFN--induzierbaren Immunoproteasoms bei der Prozessierung von parasitären Proteinen und Präsentation von Peptiden gebunden an MHC I Moleküle durch in vivo und in vitro Experimente untersucht. Es konnte in dieser Arbeit eine Immunoproteasom-unabhängige Prozessierung aufgezeigt werden.rnWeiterhin wurde Parasitenlysat (SLA) von sowohl Promastigoten als auch Amastigoten fraktioniert. In weiterführenden Experimenten können diese Fraktionen auf immunodominante Proteine/Peptide hin untersucht werden. rnLetztlich wurden Epitop-Vorhersagen für CD8+ T Zellen mittels computergestützer Software von beiden parasitären Lebensformen durchgeführt. 300 dieser Epitope wurden synthetisiert und werden in weiterführenden Experimenten zur Charakterisierung immunogener Eigenschaften weiter verwendet. rnIn ihrer Gesamtheit trägt die vorliegende Arbeit wesentlich zum Verständnis über die komplexen Mechanismen der Prozessierung und letztendlich zur Identifikation von möglichen CD8+ T Zell Epitopen bei. Ein detailiertes Verständnis der Prozessierung von CD8+ T Zell Epitopen von Leishmania major über den MHC Klasse I Weg ist von höchster Bedeutung. Die Charakterisierung sowie die Identifikation dieser Peptide wird einen maßgeblichen Einfluss auf die weiteren Entwicklungen von Vakzinen gegen diesen bedeutenden human-pathogenen Parasiten mit sich bringen. rn

Crystal structure of an H-2Kb-ovalbumin peptide complex reveals the interplay of primary and secondary anchor positions in the major histocompatibility complex binding groove.

Sequence analysis of peptides naturally presented by major histocompatibility complex (MHC) class I molecules has revealed allele-specific motifs in which the peptide length and the residues observed at certain positions are restricted. Nevertheless, peptides containing the standard motif often fail to bind with high affinity or form physiologically stable complexes. Here we present the crystal structure of a well-characterized antigenic peptide from ovalbumin [OVA-8, ovalbumin-(257-264), SIINFEKL] in complex with the murine MHC class I H-2Kb molecule at 2.5-A resolution. Hydrophobic peptide residues Ile-P2 and Phe-P5 are packed closely together into binding pockets B and C, suggesting that the interplay of peptide anchor (P5) and secondary anchor (P2) residues can couple the preferred sequences at these positions. Comparison with the crystal structures of H-2Kb in complex with peptides VSV-8 (RGYVYQGL) and SEV-9 (FAPGNYPAL), where a Tyr residue is used as the C pocket anchor, reveals that the conserved water molecule that binds into the B pocket and mediates hydrogen bonding from the buried anchor hydroxyl group could not be likewise positioned if the P2 side chain were of significant size. Based on this structural evidence, H-2Kb has at least two submotifs: one with Tyr at P5 (or P6 for nonamer peptides) and a small residue at P2 (i.e., Ala or Gly) and another with Phe at P5 and a medium-sized hydrophobic residue at P2 (i.e., Ile). Deciphering of these secondary submotifs from both crystallographic and immunological studies of MHC peptide binding should increase the accuracy of T-cell epitope prediction.