Validation of in silico prediction by in vitro immunoserological results of fine epitope mapping on citrate synthase specific autoantibodies

In vitro identification of neutralizing epitopes of Rhipicephalus microplus serpin 17 (RmS-17)

BACKGROUND

Rhipicephalus microplus poses a significant problem for livestock worldwide and is primarily controlled with synthetic acaricides. The continuous use of acaricides results in the selection of resistance and causes environmental harm. Vaccination presents an alternative solution to this problem, although searching for the suitable antigen is still a work in progress. Salivary proteins hold promise for inclusion in vaccine formulation due to their roles in modulating host responses, assisting blood feeding and pathogen transmission. Serpins are a class of proteinase inhibitors and are among the molecules found in tick saliva that modulate host blood coagulation, inflammation, and adaptive immune responses. Previous studies have demonstrated the potential of R. microplus serpin 17 (RmS-17) to interfere with the host's defenses, and antibodies have been shown to neutralize its effects. This makes RmS-17 an putative target for vaccine development.

METHODS

Epitope mapping of RmS-17 was achieved using in silico approach combining linear B-cell epitope and antigenicity predictor. In addition, epitope mapping using overlapping peptides in an ELISA screening was used. The serpin tridimensional structure and the epitopes spatial location within the molecule were determined. Peptides were synthetized based on the predictions and used for the production of rabbit anti-sera. Purified IgG's were used to assess the antibodies capacity to neutralize RmS-17.

RESULTS

Through in silico mapping, nine potential B cell epitope regions were screened, with p1RmS-17 and p2RmS-17 selected for the experiment based on antigen prediction. In the ELISA screening using overlapping peptides, eight antibody-binding regions were identified, and p3RmS-17 and p4RmS-17 were chosen. Antibodies raised against p3RmS-17 and p4RmS-17 partially neutralized RmS-17 activity.

CONCLUSION

It was found that antibodies against a single epitope are sufficient to partially neutralize RmS-17 activity. These findings support the possibility of using an epitope-based vaccine for immunization against R. microplus.

Following Natural Autoantibodies: Further Immunoserological Evidence Regarding Their Silent Plasticity and Engagement in Immune Activation

Contradictory reports are available on vaccine-associated hyperstimulation of the immune system, provoking the formation of pathological autoantibodies. Despite being interconnected within the same network, the role of the quieter, yet important non-pathological and natural autoantibodies (nAAbs) is less defined. We hypothesize that upon a prompt immunological trigger, physiological nAAbs also exhibit a moderate plasticity. We investigated their inducibility through aged and recent antigenic triggers. Anti-viral antibodies (anti-MMR n = 1739 and anti-SARS-CoV-2 IgG n = 330) and nAAbs (anti-citrate synthase IgG, IgM n = 1739) were measured by in-house and commercial ELISAs using Croatian (Osijek) anonymous samples with documented vaccination backgrounds. The results were subsequently compared for statistical evaluation. Interestingly, the IgM isotype nAAb showed a statistically significant connection with anti-MMR IgG seropositivity (p < 0.001 in all cases), while IgG isotype nAAb levels were elevated in association with anti-SARS CoV-2 specific seropositivity (p = 0.019) and in heterogeneous vaccine regimen recipients (unvaccinated controls vector/mRNA vaccines p = 0.002). Increasing evidence supports the interplay between immune activation and the dynamic expansion of nAAbs. Consequently, further questions may emerge regarding the ability of nAAbs silently shaping the effectiveness of immunization. We suggest re-evaluating the impact of nAAbs on the complex functioning of the immunological network.

In Silico Analyses on the Comparative Potential of Therapeutic Human Monoclonal Antibodies Against Newly Emerged SARS-CoV-2 Variants Bearing Mutant Spike Protein

Since the start of the pandemic, SARS-CoV-2 has already infected more than 250 million people globally, with more than five million fatal cases and huge socio-economic losses. In addition to corticosteroids, and antiviral drugs like remdesivir, various immunotherapies including monoclonal antibodies (mAbs) to S protein of SARS-CoV-2 have been investigated to treat COVID-19 patients. These mAbs were initially developed against the wild-type SARS-CoV-2; however, emergence of variant forms of SARS-CoV-2 having mutations in the spike protein in several countries including India raised serious questions on the potential use of these mAbs against SARS-CoV-2 variants. In this study, using an in silico approach, we have examined the binding abilities of eight mAbs against several SARS-CoV-2 variants of Alpha (B.1.1.7) and Delta (B.1.617.2) lineages. The structure of the Fab region of each mAb was designed in silico and subjected to molecular docking against each mutant protein. mAbs were subjected to two levels of selection based on their binding energy, stability, and conformational flexibility. Our data reveal that tixagevimab, regdanvimab, and cilgavimab can efficiently neutralize most of the SARS-CoV-2 Alpha strains while tixagevimab, bamlanivimab, and sotrovimab can form a stable complex with the Delta variants. Based on these data, we have designed, by in silico, a chimeric antibody by conjugating the CDRH3 of regdanivimab with a sotrovimab framework to combat the variants that could potentially escape from the mAb-mediated neutralization. Our finding suggests that though currently available mAbs could be used to treat COVID-19 caused by the variants of SARS-CoV-2, better results could be expected with the chimeric antibodies.

Major histocompatibility complex linked databases and prediction tools for designing vaccines

Presently, the major histocompatibility complex (MHC) is receiving considerable interest owing to its remarkable role in antigen presentation and vaccine design. The specific databases and prediction approaches related to MHC sequences, structures and binding/nonbinding peptides have been aggressively developed in the past two decades with their own benchmarks and standards. Before using these databases and prediction tools, it is important to analyze why and how the tools are constructed along with their strengths and limitations. The current review presents insights into web-based immunological bioinformatics resources that include searchable databases of MHC sequences, epitopes and prediction tools that are linked to MHC based vaccine design, including population coverage analysis. In T cell epitope forecasts, MHC class I binding predictions are very accurate for most of the identified MHC alleles. However, these predictions could be further improved by integrating proteasome cleavage (in conjugation with transporter associated with antigen processing (TAP) binding) prediction, as well as T cell receptor binding prediction. On the other hand, MHC class II restricted epitope predictions display relatively low accuracy compared to MHC class I. To date, pan-specific tools have been developed, which not only deliver significantly improved predictions in terms of accuracy, but also in terms of the coverage of MHC alleles and supertypes. In addition, structural modeling and simulation systems for peptide-MHC complexes enable the molecular-level investigation of immune processes. Finally, epitope prediction tools, and their assessments and guidelines, have been presented to immunologist for the design of novel vaccine and diagnostics.

Immunogenic peptide discovery in cancer genomes

As immunotherapies to treat malignancy continue to diversify along with the tumor types amenable to treatment, it will become very important to predict which treatment is most likely to benefit a given patient. Tumor neoantigens, novel peptides resulting from somatic tumor mutations and recognized by the immune system as foreign, are likely to contribute significantly to the efficacy of immunotherapy. Multiple in silico methods have been developed to predict whether peptides, including tumor neoantigens, will be presented by the major histocompatibility complex (MHC) Class I or Class II, and interact with the T cell receptor (TCR). The methods for neoantigen prediction will be reviewed here, along with the most important examples of their use in the field of oncology.

Conformational changes in 2-trans-enoyl-ACP (CoA) reductase (InhA) from M. tuberculosis induced by an inorganic complex: a molecular dynamics simulation study

InhA, the NADH-dependent 2-trans-enoyl-ACP reductase enzyme from Mycobacterium tuberculosis (MTB), is involved in the biosynthesis of mycolic acids, the hallmark of mycobacterial cell wall. InhA has been shown to be the primary target of isoniazid (INH), one of the oldest synthetic antitubercular drugs. INH is a prodrug which is biologically activated by the MTB catalase-peroxidase KatG enzyme. The activation reaction promotes the formation of an isonicotinyl-NAD adduct which inhibits the InhA enzyme, resulting in reduction of mycolic acid biosynthesis. As a result of rational drug design efforts to design alternative drugs capable of inhibiting MTB's InhA, the inorganic complex pentacyano(isoniazid)ferrate(II) (PIF) was developed. PIF inhibited both wild-type and INH-resistant Ile21Val mutants of InhA and this inactivation did not require activation by KatG. Since no three-dimensional structure of the InhA-PIF complex is available to confirm the binding mode and to assess the molecular interactions with the protein active site residues, here we report the results of molecular dynamics simulations of PIF interaction with InhA. We found that PIF strongly interacts with InhA and that these interactions lead to macromolecular instabilities reflected in the long time necessary for simulation convergence. These instabilities were mainly due to perturbation of the substrate binding loop, particularly the partial denaturation of helices α6 and α7. We were also able to correlate the changes in the SASAs of Trp residues with the recent spectrofluorimetric investigation of the InhA-PIF complex and confirm their suggestion that the changes in fluorescence are due to InhA conformational changes upon PIF binding. The InhA-PIF association is very strong in the first 20.0 ns, but becomes very week at the end of the simulation, suggesting that the PIF binding mode we simulated may not reflect that of the actual InhA-PIF complex.

MHC class II epitope predictive algorithms

SUMMARY

Major histocompatibility complex class II (MHC-II) molecules sample peptides from the extracellular space, allowing the immune system to detect the presence of foreign microbes from this compartment. To be able to predict the immune response to given pathogens, a number of methods have been developed to predict peptide-MHC binding. However, few methods other than the pioneering TEPITOPE/ProPred method have been developed for MHC-II. Despite recent progress in method development, the predictive performance for MHC-II remains significantly lower than what can be obtained for MHC-I. One reason for this is that the MHC-II molecule is open at both ends allowing binding of peptides extending out of the groove. The binding core of MHC-II-bound peptides is therefore not known a priori and the binding motif is hence not readily discernible. Recent progress has been obtained by including the flanking residues in the predictions. All attempts to make ab initio predictions based on protein structure have failed to reach predictive performances similar to those that can be obtained by data-driven methods. Thousands of different MHC-II alleles exist in humans. Recently developed pan-specific methods have been able to make reasonably accurate predictions for alleles that were not included in the training data. These methods can be used to define supertypes (clusters) of MHC-II alleles where alleles within each supertype have similar binding specificities. Furthermore, the pan-specific methods have been used to make a graphical atlas such as the MHCMotifviewer, which allows for visual comparison of specificities of different alleles.

Bioinformatics for study of autoimmunity

Recent years have witnessed an explosive growth in available biological data pertaining to autoimmunity research. This includes a tremendous quantity of sequence data (biological structures, genetic and physical maps, pathways, etc.) generated by genome and proteome projects plus extensive clinical and epidemiological data. Autoimmunity research stands to greatly benefit from this data so long as appropriate strategies are available to enable full access to and utilization of this data. The quantity and complexity of this biological data necessitates use of advanced bioinformatics strategies for its efficient retrieval, analysis and interpretation. Major progress has been made in development of specialized tools for storage, analysis and modeling of immunological data, and this has led to development of a whole new field know as immunoinformatics. With advances in novel high-throughput immunology technologies immunoinformatics is transforming understanding of how the immune system functions. This paper reviews advances in the field of immunoinformatics pertinent to autoimmunity research including databases, tools in genomics and proteomics, tools for study of B- and T-cell epitopes, integrative approaches, and web servers.

Detailed analyses of antibodies recognizing mitochondrial antigens suggest similar or identical mechanism for production of natural antibodies and natural autoantibodies

Because of their endosymbiotic evolutionary origin, proteins compartmentalized into mitochondria represent an interesting transition from prokaryotic foreign to essential self molecules. We investigated the presence of naturally occurring antibodies (nAbs) recognizing mitochondrial inner membrane enzymes. Epitope mapping analysis of a mitochondrial inner membrane enzyme, citrate synthase (CS) by synthetic overlapping peptides and phage display libraries using sera from healthy individuals and from patients having systemic autoimmune disease revealed CS recognizing nAbs with IgM isotype. We analyzed cross-reactive epitopes on human CS, bacterial CS, and various standard autoantigens. We have found that the fine epitope pattern on CS is different under physiological and pathological conditions. Moreover sera affinity purified on CS cross reacts with nucleosome antigen, which cross-reactivity could be mapped to a short epitope on human CS. These data indicate that in theory, nAbs "specific" for a given self antigen could fulfill the function of participating in innate defense mechanisms and at the same time recognize a target antigen in a systemic autoimmune disease. Thus, at the level of recognized epitopes there is a possible link between the innate like part and the adaptive-autoimmune arm of the humoral immune system.

From functional genomics to functional immunomics: new challenges, old problems, big rewards

The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology—a spatially addressable, large-scale technology for measurement of specific immunological response—the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models.