Does immune recognition of SARS-CoV2 epitopes vary between different ethnic groups?

The SARS-CoV2 mediated Covid-19 pandemic has impacted humankind at an unprecedented scale. While substantial research efforts have focused towards understanding the mechanisms of viral infection and developing vaccines/ therapeutics, factors affecting the susceptibility to SARS-CoV2 infection and manifestation of Covid-19 remain less explored. Given that the Human Leukocyte Antigen (HLA) system is known to vary among ethnic populations, it is likely to affect the recognition of the virus, and in turn, the susceptibility to Covid-19. To understand this, we used bioinformatic tools to probe all SARS-CoV2 peptides which could elicit T-cell response in humans. We also tried to answer the intriguing question of whether these potential epitopes were equally immunogenic across ethnicities, by studying the distribution of HLA alleles among different populations and their share of cognate epitopes. Results indicate that the immune recognition potential of SARS-CoV2 epitopes tend to vary between different ethnic groups. While the South Asians are likely to recognize higher number of CD8-specific epitopes, Europeans are likely to identify higher number of CD4-specific epitopes. We also hypothesize and provide clues that the newer mutations in SARS-CoV2 are unlikely to alter the T-cell mediated immunogenic responses among the studied ethnic populations. The work presented herein is expected to bolster our understanding of the pandemic, by providing insights into differential immunological response of ethnic populations to the virus as well as by gaging the possible effects of mutations in SARS-CoV2 on efficacy of potential epitope-based vaccines through evaluating ∼40,000 viral genomes.

Cell Attachment Domains of the Porcine Epidemic Diarrhea Virus Spike Protein Are Key Targets of Neutralizing Antibodies.

Porcine epidemic diarrhea virus (PEDV) causes enteric disease in pigs, resulting in significant economic losses to the swine industry worldwide. Current vaccination approaches against this emerging coronavirus are only partially effective, though natural infection protects pigs against reinfection and provides lactogenic immunity to suckling piglets. The viral spike (S) glycoprotein, responsible for receptor binding and cell entry, is the major target for neutralizing antibodies. However, knowledge of antibody epitopes, their nature and location in the spike structure, and the mechanisms by which the antibodies interfere with infection is scarce. Here we describe the generation and characterization of 10 neutralizing and nonneutralizing mouse monoclonal antibodies raised against the S1 receptor binding subunit of the S protein. By expression of different S1 protein fragments, six antibody epitope classes distributed over the five structural domains of the S1 subunit were identified. Characterization of antibodies for cross-reactivity and cross-neutralization revealed antigenic differences among PEDV strains. The epitopes of potent neutralizing antibodies segregated into two epitope classes and mapped within the N-terminal sialic acid binding domain and in the more C-terminal receptor binding domain. Antibody neutralization escape mutants displayed single amino acid substitutions that impaired antibody binding and neutralization and defined the locations of the epitopes. Our observations picture the antibody epitope landscape of the PEDV S1 subunit and reveal that its cell attachment domains are key targets of neutralizing antibodies.IMPORTANCE Porcine epidemic diarrhea virus (PEDV), an emerging porcine coronavirus, causes an economically important enteric disease in pigs. Effective PEDV vaccines for disease control are currently lacking. The spike (S) glycoprotein on the virion surface is the key player in virus cell entry and, therefore, the main target of neutralizing antibodies. To understand the antigenic landscape of the PEDV spike protein, we developed monoclonal antibodies against the spike protein's S1 receptor binding region and characterized their epitopes, neutralizing activity, and cross-reactivity toward multiple PEDV strains. Epitopes of antibodies segregated into six epitope classes dispersed over the multidomain S1 structure. Monoclonal antibodies revealed antigenic variability in B-cell epitopes between PEDV strains. The epitopes of neutralizing antibodies mapped to two distinct domains in S1 that are involved in binding to carbohydrate and proteinaceous cell surface molecules, respectively, indicating the importance of these cell attachment sites on the PEDV spike protein in eliciting a protective humoral immune response.

Classification epitopes in groups based on their protein family.

BACKGROUND: The humoral immune system response is based on the interaction between antibodies and antigens for the clearance of pathogens and foreign molecules. The interaction between these proteins occurs at specific positions known as antigenic determinants or B-cell epitopes. The experimental identification of epitopes is costly and time consuming. Therefore the use of in silico methods, to help discover new epitopes, is an appealing alternative due the importance of biomedical applications such as vaccine design, disease diagnostic, anti-venoms and immune-therapeutics. However, the performance of predictions is not optimal been around 70% of accuracy. Further research could increase our understanding of the biochemical and structural properties that characterize a B-cell epitope. RESULTS: We investigated the possibility of linear epitopes from the same protein family to share common properties. This hypothesis led us to analyze physico-chemical (PCP) and predicted secondary structure (PSS) features of a curated dataset of epitope sequences available in the literature belonging to two different groups of antigens (metalloproteinases and neurotoxins). We discovered statistically significant parameters with data mining techniques which allow us to distinguish neurotoxin from metalloproteinase and these two from random sequences. After a five cross fold validation we found that PCP based models obtained area under the curve values (AUC) and accuracy above 0.9 for regression, decision tree and support vector machine. CONCLUSIONS: We demonstrated that antigen's family can be inferred from properties within a single group of linear epitopes (metalloproteinases or neurotoxins). Also we discovered the characteristics that represent these two epitope groups including their similarities and differences with random peptides and their respective amino acid sequence. These findings open new perspectives to improve epitope prediction by considering the specific antigen's protein family. We expect that these findings will help to improve current computational mapping methods based on physico-chemical due it's potential application during epitope discovery.

Analysis of the effects of polymorphism on pollen profilin structural functionality and the generation of conformational, T- and B-cell epitopes.

An extensive polymorphism analysis of pollen profilin, a fundamental regulator of the actin cytoskeleton dynamics, has been performed with a major focus in 3D-folding maintenance, changes in the 2-D structural elements, surface residues involved in ligands-profilin interactions and functionality, and the generation of conformational and lineal B- and T-cell epitopes variability. Our results revealed that while the general fold is conserved among profilins, substantial structural differences were found, particularly affecting the special distribution and length of different 2-D structural elements (i.e. cysteine residues), characteristic loops and coils, and numerous micro-heterogeneities present in fundamental residues directly involved in the interacting motifs, and to some extension these residues nearby to the ligand-interacting areas. Differential changes as result of polymorphism might contribute to generate functional variability among the plethora of profilin isoforms present in the olive pollen from different genetic background (olive cultivars), and between plant species, since biochemical interacting properties and binding affinities to natural ligands may be affected, particularly the interactions with different actin isoforms and phosphoinositides lipids species. Furthermore, conspicuous variability in lineal and conformational epitopes was found between profilins belonging to the same olive cultivar, and among different cultivars as direct implication of sequences polymorphism. The variability of the residues taking part of IgE-binding epitopes might be the final responsible of the differences in cross-reactivity among olive pollen cultivars, among pollen and plant-derived food allergens, as well as between distantly related pollen species, leading to a variable range of allergy reactions among atopic patients. Identification and analysis of commonly shared and specific epitopes in profilin isoforms is essential to gain knowledge about the interacting surface of these epitopes, and for a better understanding of immune responses, helping design and development of rational and effective immunotherapy strategies for the treatment of allergy diseases.

Predicting linear B-cell epitopes using string kernels.

The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B-cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross-validation on a homology-reduced data set of 701 linear B-cell epitopes, extracted from Bcipep database, and 701 non-epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B-cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC = 0.758) outperforms 11 SVM-based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC = 0.7), a recently proposed method for predicting linear B-cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B-cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B-cell epitope prediction methods drawn on the basis of experiments using data sets of unique B-cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology-reduced data sets in comparing B-cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homology-reduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools.

A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.

Selection and combination of machine learning classifiers for prediction of linear B-cell epitopes on proteins.

Recently, new machine learning classifiers for the prediction of linear B-cell epitopes were presented. Here we show the application of Receiver Operator Characteristics (ROC) convex hulls to select optimal classifiers as well as possibilities to improve the post test probability (PTP) to meet real world requirements such as high throughput epitope screening of whole proteomes. The major finding is that ROC convex hulls present an easy to use way to rank classifiers based on their prediction conservativity as well as to select candidates for ensemble classifiers when validating against the antigenicity profile of 10 HIV-1 proteins. We also show that linear models are at least equally efficient to model the available data when compared to multi-layer feed-forward neural networks.

Machine learning approaches for prediction of linear B-cell epitopes on proteins.

Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set.