Quantitative structure-activity relationships and the prediction of MHC supermotifs

Differential patterns of diversity at neutral and adaptive loci in endangered Rhodeus pseudosericeus populations

Given the fact that threatened species are often composed of isolated small populations, spatial continuity or demography of the populations may be major factors that have shaped the species' genetic diversity. Thus, neutral loci have been the most commonly-used markers in conservation genetics. However, the populations under the influence of different environmental factors may have evolved in response to different selective pressures, which cannot be fully reflected in neutral genetic variation. Rhodeus pseudosericeus, a bitterling species (Acheilognathidae; Cypriniformes) endemic to the Korean Peninsula, are only found in some limited areas of three rivers, Daecheon, Han and Muhan, that flow into the west coast. Here, we genotyped 24 microsatellite loci and two loci (DAB1 and DAB3) of MHC class II peptide-binding β1 domain for 222 individuals collected from seven populations. Our microsatellite analysis revealed distinctive differentiation between the populations of Daecheon and Muhan Rivers and the Han River populations, and populations were structured into two subgroups within the Han River. Apparent positive selection signatures were found in the peptide-binding residues (PBRs) of the MHC loci. The allelic distribution of MHC showed a degree of differentiation between the populations of Daecheon and Muhan Rivers and the Han River populations, partially similar to the results obtained for microsatellites, however showed rather complex patterns among populations in the Han River. Considering the apparent differences in the distribution of supertypes obtained based on the physicochemical differences induced by the polymorphisms of these PBRs, the differentiation in DAB1 between the two regional groups may result in the differences in immune function. No differentiation between these two regions was observed in the supertyping of DAB3, probably indicating that only DAB1 was associated with the response to locally specialized antigenic peptides.

Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms

Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.

Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

Structural basis for the differential classification of HLA-A*6802 and HLA-A*6801 into the A2 and A3 supertypes

High polymorphism is one of the most important features of human leukocyte antigen (HLA) alleles, which were initially classified by serotyping but have recently been re-grouped into supertypes according to their peptide presentation properties. Two relatively prevalent HLA alleles HLA-A*6801 and HLA-A*6802, are classified into the same serotype HLA-A68. However, based on their distinct peptide-binding characteristics, HLA-A*6801 is grouped into A3 supertype, whereas HLA-A*6802 belongs to A2 supertype, similar to HLA-A*0201. Thusfar, the structural basis of the different supertype definitions of these serotyping-identical HLA alleles remains largely unknown. Herein, we determined the structures of HLA-A*6801 and HLA-A*6802 presenting three typical A3 and A2 supertype-restricted peptides, respectively. The binding capabilities of these peptides to HLA-A*6801, HLA-A*6802, and HLA-A*0201 were analyzed. These data indicate that the similar conformations of the residues within the F pocket contribute to close-related peptide binding features of HLA-A*6802 and HLA-A*0201. However, the overall structure and the peptide conformation of HLA-A*6802 are more similar to HLA-A*6801 rather than HLA-A*0201 which illuminates the similar serotype grouping of HLA-A*6802 and HLA-A*6801. Our findings are helpful for understanding the divergent peptide presentation and virus-specific CTL responses impacted by MHC micropolymorphisms and also elucidate the molecular basis of HLA supertype definitions.

Predicting HLA class I non-permissive amino acid residues substitutions

Prediction of peptide binding to human leukocyte antigen (HLA) molecules is essential to a wide range of clinical entities from vaccine design to stem cell transplant compatibility. Here we present a new structure-based methodology that applies robust computational tools to model peptide-HLA (p-HLA) binding interactions. The method leverages the structural conservation observed in p-HLA complexes to significantly reduce the search space and calculate the system’s binding free energy. This approach is benchmarked against existing p-HLA complexes and the prediction performance is measured against a library of experimentally validated peptides. The effect on binding activity across a large set of high-affinity peptides is used to investigate amino acid mismatches reported as high-risk factors in hematopoietic stem cell transplantation.

Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a StepRank scheme

Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A*0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A*0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.

Predicting peptide binding to Major Histocompatibility Complex molecules

The Major Histocompatibility Complex (MHC) constitutes an important part of the human immune system. During infection, pathogenic proteins are processed into peptide fragments by the antigen processing machinery. These peptides bind to MHC molecules and the MHC-peptide complex is then transported to the cell membrane where it elicits an immune response via T-cell binding. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. One of the most challenging aspects of this area of research is understanding the specificity and sensitivity of the binding process. An empirical approach to the problem is unfeasible as there are over 512 billion potential binding peptides for each MHC molecule. Computational approaches offer the promise of predicting peptide binding, thus dramatically reducing the number of peptides proceeding to experimental verification. Various bioinformatic approaches have been developed to predict whether or not a particular peptide will bind to a particular MHC allele. Currently, peptide binding prediction methods can be categorised into three major groups: motif- and scoring matrix-based methods, artificial intelligence- (AI-) based methods, and structure-based methods. The first two are sequence-based approaches and are generally based on common sequence motifs in peptides known to bind to MHC molecules. The structure-based approach concerns the structural features and the distribution of energy between the binding peptide and the MHC molecule. Although knowledge of the molecular structure of the MHC molecules is expected to lead to better predictions of peptide binding, the development of structure-based methods has been relatively slow compared to sequence-based methods. Comparisons of various methods showed that the best sequence-based methods significantly outperform structure-based methods. This may be improved by producing more structures and binding data desperately needed by many alleles, especially class II molecules. On the other hand, the large number of verification methods and indicators used by structure-based studies hinders critical evaluation of the methods. Adopting commonly used assessment procedures can demonstrate the relative performance of structure-based methods in a straightforward comparison with other methods. This review provides an overview of current methods for predicting peptide binding to the MHC, with a focus on structure-based methods, and explores the potential for future development in this area.

Qualitative and quantitative analysis of peptide microarray binding experiments using SVM-PEPARRAY

A main objective of analyzing peptide array-based binding experiments is to uncover the relationship between a peptide sequence and the binding outcome. Limited by the peptide array technologies available for applications, few attempts have been made to construct qualitative or quantitative models that depict the peptide sequence:binding strength relationships in peptide microarray-based binding studies. There has been a long history of similar modeling efforts based on low-throughput binding data in the areas of T-cell epitope screening and kinase substrate mapping, however. The keen needs in peptide array applications and the success of the modeling efforts in related fields have prompted us to develop SVM-PEPARRAY, a Web-based program capable of constructing qualitative and quantitative models based on peptide microarray binding datasets using support vector machine (SVM) modeling methods. We expect that such modeling analysis will allow researchers to quickly extract sequence-based biological information from improved peptide array binding results and provide more precise and accurate information about the biological systems investigated.

Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: a bioinformatic approach to the prediction of new epitopes

Background

Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.

Methodology/Findings

Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1.

Conclusions/Significance

A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology.

Current research status of immunology in the genomic era

This review updates the current status of immunology research under the influence of genomics, both conceptually and technologically. It particularly highlights the advantages of employing the high-throughput and large-scale technology, the large genomic database, and bioinformatic power in the immunology research. The fast development in the fields of basic immunology, clinical immunology (tumor and infectious immunology) and vaccine designing is illustrated with respect to the successful usage of genomic strategy. We also speculate the future research directions of immunology in the era of genomics and post-genomics.

Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors

Major histocompatibility complex (MHC) molecules bind short peptides resulting from intracellular processing of foreign and self proteins, and present them on the cell surface for recognition by T-cell receptors. We propose a new robust approach to quantitatively model the binding affinities of MHC molecules by quantitative structure-activity relationships (QSAR) that use the physical-chemical amino acid descriptors E1-E5. These QSAR models are robust, sequence-based, and can be used as a fast and reliable filter to predict the MHC binding affinity for large protein databases.

Quantitative Structure−Activity Relationship of Peptides Binding to the Class II Major Histocompatibility Complex Molecule Aq Associated with Autoimmune Arthritis

Presentation of (glyco)peptides by the class II major histocompatibility complex molecule Aq to T cells plays a central role in collagen-induced arthritis, an animal model for the autoimmune disease rheumatoid arthritis. A peptide library was designed using statistical molecular design in amino acid space in which five positions in the minimal mouse collagen type II binding epitope CII260-267 were varied. A substantially reduced peptide library of 24 peptides with diverse and representative molecular characteristics was selected, synthesized, and evaluated for the binding strength to Aq. A multivariate QSAR model was established by correlating calculated descriptors, compressed to its principle properties, with the binding data using partial least-square regression. The model was successfully validated by an external test set. Interpretation of the model provided a molecular property binding motif for peptides interacting with Aq. The information may be useful in future research directed toward new treatments of rheumatoid arthritis.

Immunogenicity screening in protein drug development

Most therapeutic proteins in clinical trials or on the market are, to a variable extent, immunogenic. Formation of antidrug antibodies poses a risk that should be assessed during drug development, as it possibly compromises drug safety and alters pharmacokinetics. The generation of these antibodies is critically dependent on the presence of T helper cell epitopes, which have classically been identified by in vitro methods using blood cells from human donors. As a novel development, in silico methods that identify T cell epitopes have been coming on line. These methods are relatively inexpensive and allow the mapping of epitopes from virtually all human leukocyte antigen molecules derived from a wide genetic background. In vitro assays remain important, but guided by in silico data they can focus on selected peptides and human leukocyte antigen haplotypes, thereby significantly reducing time and cost.

Predicting peptides bound to I-Ag7 class II histocompatibility molecules using a novel expectation-maximization alignment algorithm

The useful structural features of class II MHC molecules are rarely integrated into T-cell epitope predictions. We propose an approach that applies a novel expectation-maximization algorithm to align the naturally processed peptides selected by the class II MHC I-A(g7) molecule - focusing on the five MHC-specific anchor positions. Based on the alignment profile, log of odds (LOD) scores supplemented with the Laplace plus-one pseudocounts method are applied to identify the potential T-cell epitopes. In addition, an innovative computational concept of hindering residues using statistical and structural information is developed to refine the prediction. Performance analysis by receiver operating characteristics statistics and the experimental validation of the LOD scores demonstrate the accuracy of our predictive model. Furthermore, our model successfully predicts T-cell epitopes of hen egg-white lysozyme protein antigen. Our study provides a framework for predicting T-cell epitopes in class II MHC molecules.

Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties

T-lymphocyte (T-cell) is a very important component in human immune system. It possesses a receptor (TCR) that is specific for the foreign epitopes which are in a form of short peptides bound to the major histocompatibility complex (MHC). When T-cell receives the message about the peptides bound to MHC, it makes the immune system active and results in the disposal of the immunogen. The antigenic determinants recognized and bound by the T-cell receptor is known as T-cell epitope. The accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. For the first time we developed new models using least squares support vector machine (LSSVM) and amino acid properties for T-cell epitopes prediction. A dataset including 203 short peptides (167 non-epitopes and 36 epitopes) was used as the input dataset and it was randomly divided into a training set and a test set. The models based on LSSVM and amino acid properties were evaluated using leave-one-out cross-validation method and the predictive ability of the test set, and obtained the results of 0.9875 and 0.9734 under the ROC curves, respectively. This result is more satisfactory than that were reported before. Especially, the accuracy of true positive gets a marked enhancement.

Definition of MHC supertypes through clustering of MHC peptide-binding repertoires

Identification of peptides that can bind to major histocompatibility complex (MHC) molecules is important for anticipation of T-cell epitopes and for the design of epitope-based vaccines. Population coverage of epitope vaccines is, however, compromised by the extreme polymorphism of MHC molecules, which is in fact the basis for their differential peptide binding. Therefore, grouping of MHC molecules into supertypes according to peptide-binding specificity is relevant for optimizing the composition of epitope-based vaccines. Despite the fact that the peptide-binding specificity of MHC molecules is linked to their specific amino acid sequences, it is unclear how amino sequence differences correlate with peptide-binding specificities. In this chapter, we detail a method for defining MHC supertypes based on the analysis and subsequent clustering of their peptide-binding repertoires.

In silico identification of supertypes for class II MHCs

The development of epitope-based vaccines, which have wide population coverage, is greatly complicated by MHC polymorphism. The grouping of alleles into supertypes, on the basis of common structural and functional features, addresses this problem directly. In the present study we applied a combined bioinformatics approach, based on analysis of both protein sequence and structure, to identify similarities in the peptide binding sites of 2225 human class II MHC molecules, and thus define supertypes and supertype fingerprints. Two chemometric techniques were used: hierarchical clustering using three-dimensional Comparative Similarity Indices Analysis fields and nonhierarchical k-means clustering using sequence-based z-descriptors. An average consensus of 84% was achieved, i.e., 1872 of 2225 class II molecules were classified in the same supertype by both techniques. Twelve class II supertypes were defined: five DRs, three DQs, and four DPs. The HLA class II supertypes and their fingerprints given in parenthesis are DR1 (Trp(9beta)), DR3 (Glu(9beta), Gln(70beta), and Gln/Arg(74beta)), DR4 (Glu(9beta), Gln/Arg(70beta), and Glu/Ala(74beta)), DR5 (Glu(9beta), Asp(70beta)), and DR9 (Lys/Gln(9beta)); DQ1 (Ala/Gly(86beta)), DQ2 (Glu(86beta), Lys(71beta)), and DQ3 (Glu(86beta), Thr/Asp(71beta)); DPw1 (Asp(84beta) and Lys(69beta)), DPw2 (Gly/Val(84beta) and Glu(69beta)), DPw4 (Gly/Val(84beta) and Lys(69beta)), and DPw6 (Asp(84beta) and Glu(69beta)). Apart from the good agreement between known binding motifs and our classification, several new supertypes, and corresponding thematic binding motifs, were also defined.