Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

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.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Quantitative structure-activity relationships and the prediction of MHC supermotifs

The underlying assumption in quantitative structure–activity relationship (QSAR) methodology is that related chemical structures exhibit related biological activities. We review here two QSAR methods in terms of their applicability for human MHC supermotif definition. Supermotifs are motifs that characterise binding to more than one allele. Supermotif definition is the initial in silico step of epitope-based vaccine design. The first QSAR method we review here—the additive method—is based on the assumption that the binding affinity of a peptide depends on contributions from both amino acids and the interactions between them. The second method is a 3D-QSAR method: comparative molecular similarity indices analysis (CoMSIA). Both methods were applied to 771 peptides binding to 9 HLA alleles. Five of the alleles (A*0201, A* 0202, A*0203, A*0206 and A*6802) belong to the HLA-A2 superfamily and the other four (A*0301, A*1101, A*3101 and A*6801) to the HLA-A3 superfamily. For each superfamily, supermotifs defined by the two QSAR methods agree closely and are supported by many experimental data.

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.

Identifiying human MHC supertypes using bioinformatic methods

Classification of MHC molecules into supertypes in terms of peptide-binding specificities is an important issue, with direct implications for the development of epitope-based vaccines with wide population coverage. In view of extremely high MHC polymorphism (948 class I and 633 class II HLA alleles) the experimental solution of this task is presently impossible. In this study, we describe a bioinformatics strategy for classifying MHC molecules into supertypes using information drawn solely from three-dimensional protein structure. Two chemometric techniques–hierarchical clustering and principal component analysis–were used independently on a set of 783 HLA class I molecules to identify supertypes based on structural similarities and molecular interaction fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77. action fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77.

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).

EpiDOCK:a molecular docking-based tool for MHC class II binding prediction

Cellular peptide vaccines contain T-cell epitopes. The main prerequisite for a peptide to act as a T-cell epitope is that it binds to a major histocompatibility complex (MHC) protein. Peptide MHC binder identification is an extremely costly experimental challenge since human MHCs, named human leukocyte antigen, are highly polymorphic and polygenic. Here we present EpiDOCK, the first structure-based server for MHC class II binding prediction. EpiDOCK predicts binding to the 23 most frequent human, MHC class II proteins. It identifies 90% of true binders and 76% of true non-binders, with an overall accuracy of 83%. EpiDOCK is freely accessible at http://epidock.ddg-pharmfac. net. © The Author 2013. Published by Oxford University Press. All rights reserved.

Histidine hydrogen bonding in MHC at pH 5 and pH 7 modeled by molecular docking and molecular dynamics simulations

Hydrogen bonds play important roles in maintaining the structure of proteins and in the formation of most biomolecular protein-ligand complexes. All amino acids can act as hydrogen bond donors and acceptors. Among amino acids, Histidine is unique, as it can exist in neutral or positively charged forms within the physiological pH range of 5.0 to 7.0. Histidine can thus interact with other aromatic residues as well as forming hydrogen bonds with polar and charged residues. The ability of His to exchange a proton lies at the heart of many important functional biomolecular interactions, including immunological ones. By using molecular docking and molecular dynamics simulation, we examine the influence of His protonation/deprotonation on peptide binding affinity to MHC class II proteins from locus HLA-DP. Peptide-MHC interaction underlies the adaptive cellular immune response, upon which the next generation of commercially-important vaccines will depend. Consistent with experiment, we find that peptides containing protonated His residues bind better to HLA-DP proteins than those with unprotonated His. Enhanced binding at pH 5.0 is due, in part, to additional hydrogen bonds formed between peptide His+ and DP proteins. In acidic endosomes, protein His79β is predominantly protonated. As a result, the peptide binding cleft narrows in the vicinity of His79β, which stabilizes the peptide - HLA-DP protein complex. © 2014 Bentham Science Publishers.

MHC class II binding prediction by molecular docking

Proteins of the Major Histocompatibility Complex (MHC) bind self and nonself peptide antigens or epitopes within the cell and present them at the cell surface for recognition by T cells. All T-cell epitopes are MHC binders but not all MCH binders are T-cell epitopes. The MHC class II proteins are extremely polymorphic. Polymorphic residues cluster in the peptide-binding region and largely determine the MHC's peptide selectivity. The peptide binding site on MHC class II proteins consist of five binding pockets. Using molecular docking, we have modelled the interactions between peptide and MHC class II proteins from locus DRB1. A combinatorial peptide library was generated by mutation of residues at peptide positions which correspond to binding pockets (so called anchor positions). The binding affinities were assessed using different scoring functions. The normalized scoring functions for each amino acid at each anchor position were used to construct quantitative matrices (QM) for MHC class II binding prediction. Models were validated by external test sets comprising 4540 known binders. Eighty percent of the known binders are identified in the best predicted 15% of all overlapping peptides, originating from one protein. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Estudio en aves silvestres de canto ("Passeriformes") de las moléculas MHC y reclasificación molecular de la familia "Carduelini"

The Major Histocompatibility Complex (MHC) comprises the most polymorphic loci in animals. MHC plays an important role during the first steps of the immune response in vertebrates. In humans, MHC molecules (also named human leukocyte antigens, HLA) were initially regarded as class I or class II molecules. Each of them, presents to different T cells subsets. MHC class I molecules, are heterodimers in which the heavy chain (alpha) has three extracellular domains, two of which (alpha 1 and alpha 2) are polymorphic and conform the antigen recognition sites (ARS). The ARS is thought to be subjected to balancing selection for variability, which is the cause of the very high polymorphism of the MHC molecules. Different pathogenic epitopes would be the evolutionary force causing balancing selection. MHC class I genes have been completely sequenced (α1 and α2 protein domains) and thoroughly studied in Gallus gallus (chicken) as well as in mammals. In fact, the MHC locus was first defined in chicken, specifically in the highly consanguineous variety „Leghorn‟. It has been found that, in the case of chickens the MHC genetic region is considerably smaller than it is in mammals (remarkably shorter introns were found in chickens), and is organized quite differently. The noteworthy presence of short introns in chickens; supported the hypothesis that chicken‟s MHC represented a „minimal essential MHC‟. Until now, it has been assumed that chicken (order Galliformes) MHC was similar to all species included in the whole class Aves...

Major Histocompatibility Complex (MHC) Class II sequence polymorphism in long-finned pilot whale (Globicephala melas) from the North Atlantic

Funding Silvia S. Monteiro and Marisa Ferreira were supported by a Ph.D. grant from Fundação para a Ciência e Tecnologia (ref SFRH/BD/38735/2007 and SFRH/BD/30240/2006, respectively). Alfredo López was supported by a postdoctoral grant from Fundação para a Ciência e Tecnologia (ref SFRH/BPD/82407/2011). Catarina Eira is supported by CESAM (UID/AMB/50017), from FCT/MEC through national funds and FEDER (PT2020, Compete 2020). The work related with strandings and tissue collection in Portugal was partially supported by the SafeSea Project EEAGrants PT 0039 (supported by Iceland, Liechtenstein and Norway through the EEA Financial Mechanism), by the Project MarPro–Life09 NAT/PT/000038 (funded by the European Union–Program Life+) and by the project CetSenti FCT RECI/AAG-GLO/0470/2012; FCOMP-01-0124-FEDER-027472 (Funded by the Program COMPETE and Fundação para a Ciência e Tecnologia).

THE IMPORTANCE OF MHC-INDEPENDENT NATURAL KILLER CELL RECEPTORS IN THE IMMUNOREGULATION OF MURINE PREGNANCY

Numerous leukocyte populations are essential for pregnancy success. Uterine natural killer (uNK) cells are chief amongst these leukocytes and represent a unique lineage with limited cytotoxicity but abundant angiokine production. They possess a distinct phenotype of activating and inhibitory receptors that recognize major histocompatibility complex (MHC) molecules, such as the killer immunoglobulin like receptors (KIRs; mouse Ly49), and MHC-independent activating receptors, including the aryl hydrocarbon receptor (AHR) and natural cytotoxicity receptor 1 (NCR1). While the roles of MHC-dependent receptors are widely addressed in pregnancy, MHC-independent receptors are relatively unstudied. This thesis investigated the roles of MHC-independent receptors in promotion of mouse pregnancy and characterized early leukocyte interactions in the presence and absence of NCR1. It was hypothesized that loss of MHC-independent receptors impairs uNK cell development resulting in aberrations in leukocyte function and decidual vasculature. Implantation sites from Ahr-/- and Ncr1Gfp/Gfp mice were assessed using whole mount in situ immunohistochemistry (WM-IHC) and histochemical techniques. Leukocyte interactions identified during preliminary WM-IHC studies were confirmed as immune synapses. The novel identification of immune synapses in early mouse pregnancy compelled further examination of leukocyte conjugates in wildtype C57BL/6 and Ncr1Gfp/Gfp mice. In Ahr-/- and Ncr1Gfp/Gfp mice, receptor loss resulted in reduced uNK cell diameters, impaired decidual vasculature, and failures in spiral artery remodeling. Ahr-/- mice had severe fertility deficits whereas Ncr1Gfp/Gfp mice had increased fetal resorption indicating differing receptor requirements in pregnancy success. NCR1 loss primarily affected uNK cell maturation and function as identified by alterations in granule ultrastructure, lytic protein expression, and angiokine production. Leukocyte conjugates were frequent in early C57BL/6 decidua basalis and included uNK cells conjugating first with antigen presenting cells and then with T cells. Overall conjugate formation was reduced in the absence of NCR1, but specific uNK cell conjugations were unaffected by receptor loss. While KIR-MHC interactions are associated with numerous pregnancy complications in humans, the role of other uNK cell receptors are not well characterized. These results illustrate the importance of MHC-independent receptors in uNK cell activation during early pregnancy in mice and encourage further studies of pregnancy complications that may occur independently of maternal KIR-MHC contributions.

Continuous acquisition of MHC:peptide complexes by recipient cells contributes to the generation of anti-graft CD8+T cell immunity

Understanding the evolution of the direct and indirect pathways of allorecognition following tissue transplantation is essential in the design of tolerance-promoting protocols. On the basis that donor bone marrow-derived antigen presenting cells are eliminated within days of transplantation, it has been argued that the indirect response represents the major threat to long term transplant survival, and is consequently the key target for regulation. However, the detection of MHC transfer between cells, and particularly the capture of MHC:peptide complexes by dendritic cells, led us to propose a third, semi-direct, pathway of MHC allorecognition. Persistence of this pathway would lead to sustained activation of direct pathway T cells, arguably persisting for the life of the transplant. In this study, we focused on the contribution of acquired MHC class I, on recipient DCs, during the life span of a skin graft. We observed that MHC class I acquisition by recipient DCs occurs for at least one month following transplantation and may be the main source of alloantigen that drives CD8+ cytotoxic T cell responses. In addition, acquired MHC class I-peptide complexes stimulate T cell responses in vivo further emphasizing the need to regulate both pathways to induce indefinite survival of the graft.