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

Immune and spermatogenesis-related loci are involved in the development of extreme patterns of male infertility

We conducted a genome-wide association study in a large population of infertile men due to unexplained spermatogenic failure (SPGF). More than seven million genetic variants were analysed in 1,274 SPGF cases and 1,951 unaffected controls from two independent European cohorts. Two genomic regions were associated with the most severe histological pattern of SPGF, defined by Sertoli cell-only (SCO) phenotype, namely the MHC class II gene HLA-DRB1 (rs1136759, P = 1.32E-08, OR = 1.80) and an upstream locus of VRK1 (rs115054029, P = 4.24E-08, OR = 3.14), which encodes a protein kinase involved in the regulation of spermatogenesis. The SCO-associated rs1136759 allele (G) determines a serine in the position 13 of the HLA-DRβ1 molecule located in the antigen-binding pocket. Overall, our data support the notion of unexplained SPGF as a complex trait influenced by common variation in the genome, with the SCO phenotype likely representing an immune-mediated condition.

A GWAS in a large case-control cohort of European ancestry identifies two genomic regions, the MHC class II gene HLA-DRB1 and an upstream locus of VRK1, that are associated with the most severe phenotype of spermatogenic failure.

In Silico Identification of Chikungunya Virus B- and T-Cell Epitopes with High Antigenic Potential for Vaccine Development

Reverse vaccinology is an outstanding strategy to identify antigens with high potential for vaccine development. Different parameters of five prediction programs were used to assess their sensitivity and specificity to identify B-cell epitopes of Chikungunya virus (CHIKV) strains reported in the IEDB database. The results, based on the use of 15 to 20 mer epitopes and the polyproteins to which they belong, were compared to establish the best parameters to optimize the prediction of antigenic peptides of the Mexican strain CHIKV AJV21562.1. LBtope showed the highest specificity when we used the reported epitopes and polyproteins but the worst sensitivity with polyproteins; ABCpred had similar specificity to LBtope only with the epitopes reported and showed moderate specificity when we used polyproteins for the predictions. Because LBtope was more reliable in predicting true epitopes, it was used as a reference program to predict and select six novel epitopes of the Mexican strain of CHIKV according to prediction frequency, viral genome localization, and non-homology with the human proteome. On the other hand, six bioinformatics programs were used with default parameters to predict T-cell epitopes in the CHIKV strains AJV21562.1 and AJV21561.1. The sequences of the polyproteins were analyzed to predict epitopes present in the more frequent HLA alleles of the Mexican population: DQA1*03011, DQA1*0401, DQA1*0501, DQB1*0201, DQB1*0301, DQB1*0302, and DQB1*0402. Fifteen predicted epitopes in the non-structural and 15 predicted epitopes in the structural polyprotein (9- to 16-mers) with the highest scores of each allele were compared to select epitopes with at least 80% identity. Next, the epitopes predicted with at least two programs were aligned to the human proteome, and 12 sequences without identity with the human proteome were identified as potential antigenic candidates. This strategy would be useful to evaluate vaccine candidates against other viral diseases affecting the countries of the Americas and to increase knowledge about these diseases.

The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope

As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.

A Cohesive and Integrated Platform for Immunogenicity Prediction

In silico methods for immunogenicity prediction mine the enormous quantity of data arising from deciphered genomes and proteomes to identify immunogenic proteins. While high and productive immunogenicity is essential for vaccines, therapeutic proteins and monoclonal antibodies should be minimally immunogenic. Here, we present a cohesive platform for immunogenicity and MHC class I and/or II binding affinity prediction. The platform integrates three quasi-independent modular servers: VaxiJen, EpiJen, and EpiTOP. VaxiJen (http://www.ddg-pharmfac.net/vaxijen) predicts immunogenicity of proteins of different origin; EpiJen (http://www.ddg-pharmfac.net/epijen) predicts peptide binding to MHC class I proteins; and EpiTOP (http://www.ddg-pharmfac.net/epitop) predicts peptide binding to MHC class II proteins. The platform is freely accessible and user-friendly. The protocol for immunogenicity prediction is demonstrated by selecting immunogenic proteins from Mycobacterium tuberculosis and predicting how the peptide epitopes within them bind to MHC class I and class II proteins.

Peptide Binding Prediction to Five Most Frequent HLA-DQ Proteins - a Proteochemometric Approach

Major histocompatibility complex (MHC) proteins class II, are glycoproteins binding within the cell to short peptides with foreign origin, called epitopes, and present them at the cell surface for inspection by T-cells. Apart from presenting foreign antigens, they are able to present also common self-antigens and trigger autoimmune diseases as coeliac disease and diabetes mellitus type 1. The MHC proteins are extremely polymorphic. The polymorphism is located mainly in the peptide binding site. In the present study, we apply a proteochemometric approach to derive a model for prediction of peptide binding to human MHC class II proteins from locus HLA-DQ. Proteochemometrics was applied on 2624 peptides binding to five most frequent HLA-DQ proteins. The sequences of peptides and proteins were described by three z-descriptors relating to hydrophobicity, steric effects and polarity of amino acids. Cross-terms accounting for the protein-peptide interactions also were included. The derived model was validated by external test set of 660 peptides and showed rpred (2) =0.808, AUC=0.965, 92.5 % accuracy at threshold of pIC50 =5.3 and average sensitivity of 83 % among the top 10 % best predicted nonamers. The model is implemented in the server for MHC binding prediction EpiTOP and is freely available at http://www.ddg-pharmfac.net/epitop.

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.

Visually interpretable models of kinase selectivity related features derived from field-based proteochemometrics

Achieving selectivity for small organic molecules toward biological targets is a main focus of drug discovery but has been proven difficult, for example, for kinases because of the high similarity of their ATP binding pockets. To support the design of more selective inhibitors with fewer side effects or with altered target profiles for improved efficacy, we developed a method combining ligand- and receptor-based information. Conventional QSAR models enable one to study the interactions of multiple ligands toward a single protein target, but in order to understand the interactions between multiple ligands and multiple proteins, we have used proteochemometrics, a multivariate statistics method that aims to combine and correlate both ligand and protein descriptions with affinity to receptors. The superimposed binding sites of 50 unique kinases were described by molecular interaction fields derived from knowledge-based potentials and Schrödinger's WaterMap software. Eighty ligands were described by Mold(2), Open Babel, and Volsurf descriptors. Partial least-squares regression including cross-terms, which describe the selectivity, was used for model building. This combination of methods allows interpretation and easy visualization of the models within the context of ligand binding pockets, which can be translated readily into the design of novel inhibitors.

Identification of Candidate Vaccine Antigens In Silico

The identification of immunogenic whole-protein antigens is fundamental to the successful discovery of candidate subunit vaccines and their rapid, effective, and efficient transformation into clinically useful, commercially successful vaccine formulations. In the wider context of the experimental discovery of vaccine antigens, with particular reference to reverse vaccinology, this chapter adumbrates the principal computational approaches currently deployed in the hunt for novel antigens: genome-level prediction of antigens, antigen identification through the use of protein sequence alignment-based approaches, antigen detection through the use of subcellular location prediction, and the use of alignment-independent approaches to antigen discovery. Reference is also made to the recent emergence of various expert systems for protein antigen identification.

HLA-DP2 binding prediction by molecular dynamics simulations

Major histocompatibility complex (MHC) II proteins bind peptide fragments derived from pathogen antigens and present them at the cell surface for recognition by T cells. MHC proteins are divided into Class I and Class II. Human MHC Class II alleles are grouped into three loci: HLA-DP, HLA-DQ, and HLA-DR. They are involved in many autoimmune diseases. In contrast to HLA-DR and HLA-DQ proteins, the X-ray structure of the HLA-DP2 protein has been solved quite recently. In this study, we have used structure-based molecular dynamics simulation to derive a tool for rapid and accurate virtual screening for the prediction of HLA-DP2-peptide binding. A combinatorial library of 247 peptides was built using the "single amino acid substitution" approach and docked into the HLA-DP2 binding site. The complexes were simulated for 1 ns and the short range interaction energies (Lennard-Jones and Coulumb) were used as binding scores after normalization. The normalized values were collected into quantitative matrices (QMs) and their predictive abilities were validated on a large external test set. The validation shows that the best performing QM consisted of Lennard-Jones energies normalized over all positions for anchor residues only plus cross terms between anchor-residues.

Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function

The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods.

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.

Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets

Proteochemometric modeling is founded on the principles of QSAR but is able to benefit from additional information in model training due to the inclusion of target information.

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

An integrated approach to epitope analysis I: Dimensional reduction, visualization and prediction of MHC binding using amino acid principal components and regression approaches

Background

Operation of the immune system is multivariate. Reduction of the dimensionality is essential to facilitate understanding of this complex biological system. One multi-dimensional facet of the immune system is the binding of epitopes to the MHC-I and MHC-II molecules by diverse populations of individuals. Prediction of such epitope binding is critical and several immunoinformatic strategies utilizing amino acid substitution matrices have been designed to develop predictive algorithms. Contemporaneously, computational and statistical tools have evolved to handle multivariate and megavariate analysis, but these have not been systematically deployed in prediction of MHC binding. Partial least squares analysis, principal component analysis, and associated regression techniques have become the norm in handling complex datasets in many fields. Over two decades ago Wold and colleagues showed that principal components of amino acids could be used to predict peptide binding to cellular receptors. We have applied this observation to the analysis of MHC binding, and to derivation of predictive methods applicable on a whole proteome scale.

Results

We show that amino acid principal components and partial least squares approaches can be utilized to visualize the underlying physicochemical properties of the MHC binding domain by using commercially available software. We further show the application of amino acid principal components to develop both linear partial least squares and non-linear neural network regression prediction algorithms for MHC-I and MHC-II molecules. Several visualization options for the output aid in understanding the underlying physicochemical properties, enable confirmation of earlier work on the relative importance of certain peptide residues to MHC binding, and also provide new insights into differences among MHC molecules. We compared both the linear and non-linear MHC binding prediction tools to several predictive tools currently available on the Internet.

Conclusions

As opposed to the highly constrained user-interaction paradigms of web-server approaches, local computational approaches enable interactive analysis and visualization of complex multidimensional data using robust mathematical tools. Our work shows that prediction tools such as these can be constructed on the widely available JMP^® platform, can operate in a spreadsheet environment on a desktop computer, and are capable of handling proteome-scale analysis with high throughput.

EpiTOP--a proteochemometric tool for MHC class II binding prediction

MOTIVATION

T-cell epitope identification is a critical immunoinformatic problem within vaccine design. To be an epitope, a peptide must bind an MHC protein.

RESULTS

Here, we present EpiTOP, the first server predicting MHC class II binding based on proteochemometrics, a QSAR approach for ligands binding to several related proteins. EpiTOP uses a quantitative matrix to predict binding to 12 HLA-DRB1 alleles. It identifies 89% of known epitopes within the top 20% of predicted binders, reducing laboratory labour, materials and time by 80%. EpiTOP is easy to use, gives comprehensive quantitative predictions and will be expanded and updated with new quantitative matrices over time.

AVAILABILITY

EpiTOP is freely accessible at http://www.pharmfac.net/EpiTOP.

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.