Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Unusual Placement of an EBV Epitope into the Groove of the Ankylosing Spondylitis-Associated HLA-B27 Allele Allows CD8+ T Cell Activation

The human leukocyte antigen HLA-B27 is a strong risk factor for Ankylosing Spondylitis (AS), an immune-mediated disorder affecting axial skeleton and sacroiliac joints. Additionally, evidence exists sustaining a strong protective role for HLA-B27 in viral infections. These two aspects could stem from common molecular mechanisms. Recently, we have found that the HLA-B*2705 presents an EBV epitope (pEBNA3A-RPPIFIRRL), lacking the canonical B27 binding motif but known as immunodominant in the HLA-B7 context of presentation. Notably, 69% of B*2705 carriers, mostly patients with AS, possess B*2705-restricted, pEBNA3A-specific CD8+ T cells. Contrarily, the non-AS-associated B*2709 allele, distinguished from the B*2705 by the single His116Asp polymorphism, is unable to display this peptide and, accordingly, B*2709 healthy subjects do not unleash specific T cell responses. Herein, we investigated whether the reactivity towards pEBNA3A could be a side effect of the recognition of the natural longer peptide (pKEBNA3A) having the classical B27 consensus (KRPPIFIRRL). The stimulation of PBMC from B*2705 positive patients with AS in parallel with both pEBNA3A and pKEBNA3A did not allow to reach an unambiguous conclusion since the differences in the magnitude of the response measured as percentage of IFNγ-producing CD8+ T cells were not statistically significant. Interestingly, computational analysis suggested a structural shift of pEBNA3A as well as of pKEBNA3A into the B27 grooves, leaving the A pocket partially unfilled. To our knowledge this is the first report of a viral peptide: HLA-B27 complex recognized by TCRs in spite of a partially empty groove. This implies a rethinking of the actual B27 immunopeptidome crucial for viral immune-surveillance and autoimmunity.

Using databases and data mining in vaccinology

Throughout time functional immunology has accumulated vast amounts of quantitative and qualitative data relevant to the design and discovery of vaccines. Such data includes, but is not limited to, components of the host and pathogen genome (including antigens and virulence factors), T- and B-cell epitopes and other components of the antigen presentation pathway and allergens. In this review the authors discuss a range of databases that archive such data. Built on such information, increasingly sophisticated data mining techniques have developed that create predictive models of utilitarian value. With special reference to epitope data, the authors discuss the strengths and weaknesses of the available techniques and how they can aid computer-aided vaccine design deliver added value for vaccinology.

PeptX: using genetic algorithms to optimize peptides for MHC binding

Background

The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different in silico techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain in silico scoring functions?

Results

Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.

Conclusion

We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.

Predicting the binding affinity of epitope-peptides with HLA-A*0201 by encoding atom-pair non-covalent interaction information between receptor and ligands

A structure-based method was used to characterize the non-covalent interactions of HLA-A*0201 with its peptide ligands. In this procedure, protein and peptide atoms were classified into 16 types in terms of their chemical property and local environment, and a 16 x 16 matrix was then defined to describe the interaction mode of 256 atom-pairs between the receptor and ligand in a complex structure. Three biologically related chemical forces as electrostatic, van der Waals, and hydrophobic potentials were separately calculated for each element of the matrix to yield 768 structural descriptors encoding the detailed information about the non-covalent interactions involved in protein-peptide binding. We employed this method to perform quantitative structure-activity relationship (QSAR) study of a data panel consisting of 419 non-apeptides with known binding affinities to HLA-A*0201 protein. Several QSAR models were constructed using partial least square regression (PLS) coupled with or without genetic algorithm (GA)-variable selection, and these models were validated rigorously and investigated systematically by using external test set and one-way analysis of variance. Results show that diverse properties have significant contributions to the HLA-A*0201-peptide binding. Particularly, the hydrophobicity and electrostatic property at the anchor residues of peptides confer a significant specificity and stability for the bound complexes.

MHC Class II Binding Prediction-A Little Help from a Friend

Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

Innovative bioinformatic approaches for developing peptide-based vaccines against hypervariable viruses

The application of the fields of pharmacogenomics and pharmacogenetics to vaccine design has been recently labeled 'vaccinomics'. This newly named area of vaccine research, heavily intertwined with bioinformatics, seems to be leading the charge in developing novel vaccines for currently unmet medical needs against hypervariable viruses such as human immunodeficiency virus (HIV), hepatitis C and emerging avian and swine influenza. Some of the more recent bioinformatic approaches in the area of vaccine research include the use of epitope determination and prediction algorithms for exploring the use of peptide epitopes as vaccine immunogens. This paper briefly discusses and explores some current uses of bioinformatics in vaccine design toward the pursuit of peptide vaccines for hypervariable viruses. The various informatics and vaccine design strategies attempted by other groups toward hypervariable viruses will also be briefly examined, along with the strategy used by our group in the design and synthesis of peptide immunogens for candidate HIV and influenza vaccines.

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.

Peptide binding to the HLA-DRB1 supertype: a proteochemometrics analysis

A proteochemometrics approach was applied to a set of 2666 peptides binding to 12 HLA-DRB1 proteins. Sequences of both peptide and protein were described using three z-descriptors. Cross terms accounting for adjacent positions and for every second position in the peptides were included in the models, as well as cross terms for peptide/protein interactions. Models were derived based on combinations of different blocks of variables. These models had moderate goodness of fit, as expressed by r2, which ranged from 0.685 to 0.732; and good cross-validated predictive ability, as expressed by q2, which varied from 0.678 to 0.719. The external predictive ability was tested using a set of 356 HLA-DRB1 binders, which showed an r2(pred) in the range 0.364-0.530. Peptide and protein positions involved in the interactions were analyzed in terms of hydrophobicity, steric bulk and polarity.

Current mathematical methods used in QSAR/QSPR studies

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.

Static energy analysis of MHC class I and class II peptide-binding affinity

Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

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.

Toward the atomistic simulation of T cell epitopes

Epitopes mediated by T cells lie at the heart of the adaptive immune response and form the essential nucleus of anti-tumour peptide or epitope-based vaccines. Antigenic T cell epitopes are mediated by major histocompatibility complex (MHC) molecules, which present them to T cell receptors. Calculating the affinity between a given MHC molecule and an antigenic peptide using experimental approaches is both difficult and time consuming, thus various computational methods have been developed for this purpose. A server has been developed to allow a structural approach to the problem by generating specific MHC:peptide complex structures and providing configuration files to run molecular modelling simulations upon them. A system has been produced which allows the automated construction of MHC:peptide structure files and the corresponding configuration files required to execute a molecular dynamics simulation using NAMD. The system has been made available through a web-based front end and stand-alone scripts. Previous attempts at structural prediction of MHC:peptide affinity have been limited due to the paucity of structures and the computational expense in running large scale molecular dynamics simulations. The MHCsim server (http://igrid-ext.cryst.bbk.ac.uk/MHCsim) allows the user to rapidly generate any desired MHC:peptide complex and will facilitate molecular modelling simulation of MHC complexes on an unprecedented scale.

Immunoinformatics: an overview of computational tools and techniques for understanding immune function

In recent years, there has been a rapid expansion in the application of information technology to biological data. Although the use of information science techniques is less common for the discipline of immunology, this field has seen great strides in recent years. This review addresses why in silico modeling is needed in immunology research, highlights some of the major areas of research and suggests what may be important for the future of immunoinformatics.

In silico grouping of peptide/HLA class I complexes using structural interaction characteristics

MOTIVATION

Classification of human leukocyte antigen (HLA) proteins into supertypes underpins the development of epitope-based vaccines with wide population coverage. Current methods for HLA supertype definition, based on common structural features of HLA proteins and/or their functional binding specificities, leave structural interaction characteristics among different HLA supertypes with antigenic peptides unexplored.

METHODS

We describe the use of structural interaction descriptors for the analysis of 68 peptide/HLA class I crystallographic structures. Interaction parameters computed include the number of intermolecular hydrogen bonds between each HLA protein and its corresponding bound peptide, solvent accessibility, gap volume and gap index.

RESULTS

The structural interactions patterns of peptide/HLA class I complexes investigated herein vary among individual alleles and may be grouped in a supertype dependent manner. Using the proposed methodology, eight HLA class I supertypes were defined based on existing experimental crystallographic structures which largely overlaps (77% consensus) with the definitions by binding motifs. This mode of classification, which considers conformational information of both peptide and HLA proteins, provides an alternative to the characterization of supertypes using either peptide or HLA protein information alone.