Synthesis and immunogenicity assessment of a gold nanoparticle conjugate for the delivery of a peptide from SARS-CoV-2.

The development of vaccines is a crucial response against the COVID-19 pandemic and innovative nanovaccines could increase the potential to address this remarkable challenge. In the present study a B cell epitope (S461-493) from the spike protein of SARS-CoV-2 was selected and its immunogenicity validated in sheep. This synthetic peptide was coupled to gold nanoparticles (AuNP) functionalized with SH-PEG-NH2 via glutaraldehyde-mediated coupling to obtain the AuNP-S461-493 candidate, which showed in s.c.-immunized mice a superior immunogenicity (IgG responses) when compared to soluble S461-493; and led to increased expression of relevant cytokines in splenocyte cultures. Interestingly, the response triggered by AuNP-S461-493 was similar in magnitude to that induced using a conventional strong adjuvant (Freund's adjuvant). This study provides a platform for the development of AuNP-based nanovaccines targeting specific SARS-CoV-2 epitopes.

Antibody Specific B-Cell Epitope Predictions: Leveraging Information From Antibody-Antigen Protein Complexes.

B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.

Immunogenomic screening approach to identify new antigens for the serological diagnosis of chronic Chagas' disease.

Serological tests are preferentially used for the diagnosis of Chagas' disease (CD) during the chronic phase because of the low parasitemia and high anti-Trypanosoma cruzi antibody titers. However, the current methods showed several disadvantages, as contradictory or inconclusive results, mainly related to the characteristics of the antigens used, in general, crude or whole parasites, but also due to antigen production protocol and the experimental conditions used in serological tests. Thus, better-quality serological assays are urgently needed. Here, we performed a wide immunogenomic screen strategy to identify conserved linear B-cell epitopes in the predicted proteome based on genome sequence from T. cruzi strains to will be applied as synthetic peptides in the serodiagnosis of the chronic CD. Three B-cell epitopes derived from mucin-associated surface protein (MASP) family, expressed in both infective parasite stages, trypomastigote and amastigotes, conserved in T. cruzi strains, and highly divergent as compared with Leishmania spp. proteome, were selected for this study. The results demonstrated that synthetic peptide 2 and a mixture of peptides (Mix II: peptides 2 and 3) were able to identify all chronic CD cases, indeterminate or Chagas cardiomyopathy clinical presentation, and simultaneously able to discriminate infections caused by Leishmania parasites, with high accuracy (98.37 and 100.00%, respectively) and agreement (kappa index = 0.967 and 1.000, respectively) with direct methods as compared to current diagnostic pipeline performed by reference laboratories in Brazil. This study represents an interesting strategy for the discovery of new antigens applied to serologic diagnosis of infectious diseases and for the technological development of platforms for large-scale production of diagnostic tests.

Allergenicity Assessment of Transgenic Wheat Lines In Silico.

Agriculture biotechnology is a promising tool for developing varieties with enhanced quality and quantity. Transgenic proteins expressed by genetically modified (GM) food crops improve crop characteristics like nutritional value, taste, and texture, and endow plants with resistance against fungus, pests, and insects. Despite such potential benefits, there are concerns regarding possible adverse effects of GM crops on human health, animals and the environment. Among the proposed guidelines for GM food safety testing-the weight-of-evidence approach proposed by the Codex Alimentarius Commission (ALINORM 03/34A) is the most recent. Till date, several transgenic wheat lines have been developed and research is underway for further improvement. However, GM wheat is not being grown or consumed in any part of the world. In the present study, in silico tools were employed for safety testing of eight transgenes used for the development of transgenic wheat lines. Among the genes studied, none of them shared sequence homology with the reported allergens and may be safe for use in genetic engineering. In conclusion, gene selection for developing transgenic wheat lines should be done with utmost care to ensure its safety for feed and fodder.

Full protection of swine against foot-and-mouth disease by a bivalent B-cell epitope dendrimer peptide.

Foot-and-mouth disease virus (FMDV) causes a highly contagious disease of cloven-hoofed animals. We have reported (Cubillos et al., 2008) that a synthetic dendrimeric peptide consisting of four copies of a B-cell epitope [VP1(136-154)] linked through thioether bonds to a T-cell epitope [3A(21-35)] of FMDV [B4T(thi)] elicits potent B- and T-cell specific responses and confers solid protection in pigs to type C FMDV challenge. Herein we show that downsized versions of this peptide bearing two copies of a B-cell epitope from a type O isolate and using thioether [B2T(thi)] or maleimide [B2T(mal)] conjugation chemistries for their synthesis elicited in swine similar or higher B and T-cell specific responses than tetravalent B4T(thi). Moreover, while partial protection was observed in animals immunized with B4T(thi) (60%) and B2T(thi) (80%), B2T(mal) conferred full (100%) protection against FMDV challenge, associated to high levels of circulating IgG2 and mucosal IgGA, and entirely prevented virus shedding. Interestingly, B2T(mal) is also the most advantageous option in terms of synthetic practicality. Taken together, the results reported here point out to B2T(mal) as a highly valuable, cost-effective FMDV candidate vaccine.

New salivary anti-haemostatics containing protective epitopes from Ornithodoros moubata ticks: Assessment of their individual and combined vaccine efficacy.

Ornithodoros moubata is the main vector of the pathogens causing African swine fever and human relapsing fever in Africa. The development of an efficient vaccine against this tick would facilitate its control and the prevention of the diseases it transmits to a considerable extent. Previous efforts to identify vaccine target candidates led us to the discovery of novel salivary proteins that probably act as anti-haemostatics at the host-tick interface, including a secreted phospholipase A2 (PLA2), a 7DB-like protein (7DB-like), a riboprotein 60S L10 (RP-60S), an apyrase (APY), and a new platelet aggregation inhibitor peptide, designated mougrin (MOU). In this work, the corresponding recombinant proteins were expressed in Escherichia coli and their individual vaccine efficacy was tested in rabbit vaccination trials. All of them, except the less immunogenic RP-60S, induced strong humoral responses that reduced tick feeding and survival, providing vaccine efficacies of 44.2%, 43.2% and 27.2%, 19.9% and 17.3% for PLA2, APY, MOU, RP-60S and 7DB-like, respectively. In the case of the more protective recombinant antigens (PLA2, APY and MOU), the immunodominant protective linear B-cell epitopes were identified and their combined vaccine efficacy was tested in a second vaccine trial using different adjuvants. In comparison with the best efficacy of individual antigens, the multicomponent vaccine increased vaccine efficacy by 13.6%, indicating additive protective effects rather than a synergistic effect. Tick saliva inoculated during natural tick-host contacts had a boosting effect on vaccinated animals, increasing specific antibody levels and protection.

Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering.

B-cell epitopes are regions of the antigen surface which can be recognized by certain antibodies and elicit the immune response. Identification of epitopes for a given antigen chain finds vital applications in vaccine and drug research. Experimental prediction of B-cell epitopes is time-consuming and resource intensive, which may benefit from the computational approaches to identify B-cell epitopes. In this paper, a novel cost-sensitive ensemble algorithm is proposed for predicting the antigenic determinant residues and then a spatial clustering algorithm is adopted to identify the potential epitopes. Firstly, we explore various discriminative features from primary sequences. Secondly, cost-sensitive ensemble scheme is introduced to deal with imbalanced learning problem. Thirdly, we adopt spatial algorithm to tell which residues may potentially form the epitopes. Based on the strategies mentioned above, a new predictor, called CBEP (conformational B-cell epitopes prediction), is proposed in this study. CBEP achieves good prediction performance with the mean AUC scores (AUCs) of 0.721 and 0.703 on two benchmark datasets (bound and unbound) using the leave-one-out cross-validation (LOOCV). When compared with previous prediction tools, CBEP produces higher sensitivity and comparable specificity values. A web server named CBEP which implements the proposed method is available for academic use.

B-cell epitope prediction through a graph model.

BACKGROUND: Prediction of B-cell epitopes from antigens is useful to understand the immune basis of antibody-antigen recognition, and is helpful in vaccine design and drug development. Tremendous efforts have been devoted to this long-studied problem, however, existing methods have at least two common limitations. One is that they only favor prediction of those epitopes with protrusive conformations, but show poor performance in dealing with planar epitopes. The other limit is that they predict all of the antigenic residues of an antigen as belonging to one single epitope even when multiple non-overlapping epitopes of an antigen exist. RESULTS: In this paper, we propose to divide an antigen surface graph into subgraphs by using a Markov Clustering algorithm, and then we construct a classifier to distinguish these subgraphs as epitope or non-epitope subgraphs. This classifier is then taken to predict epitopes for a test antigen. On a big data set comprising 92 antigen-antibody PDB complexes, our method significantly outperforms the state-of-the-art epitope prediction methods, achieving 24.7% higher averaged f-score than the best existing models. In particular, our method can successfully identify those epitopes with a non-planarity which is too small to be addressed by the other models. Our method can also detect multiple epitopes whenever they exist. CONCLUSIONS: Various protrusive and planar patches at the surface of antigens can be distinguishable by using graphical models combined with unsupervised clustering and supervised learning ideas. The difficult problem of identifying multiple epitopes from an antigen can be made easied by using our subgraph approach. The outstanding residue combinations found in the supervised learning will be useful for us to form new hypothesis in future studies.

The antibody response to Plasmodium falciparum Merozoite Surface Protein 4: comparative assessment of specificity and growth inhibitory antibody activity to infection-acquired and immunization-induced epitopes.

BACKGROUND: Malaria remains a global public health challenge. It is widely believed that an effective vaccine against malaria will need to incorporate multiple antigens from the various stages of the parasite's complex life cycle. Plasmodium falciparum Merozoite Surface Protein 4 (MSP4) is a vaccine candidate that has been selected for development for inclusion in an asexual stage subunit vaccine against malaria. METHODS: Nine monoclonal antibodies (Mabs) were produced against Escherichia coli-expressed recombinant MSP4 protein and characterized. These Mabs were used to develop an MSP4-specific competition ELISA to test the binding specificity of antibodies present in sera from naturally P. falciparum-infected individuals from a malaria endemic region of Vietnam. The Mabs were also tested for their capacity to induce P. falciparum growth inhibition in vitro and compared against polyclonal rabbit serum raised against recombinant MSP4. RESULTS: All Mabs reacted with native parasite protein and collectively recognized at least six epitopes. Four of these Mabs recognize reduction-sensitive epitopes within the epidermal growth factor-like domain found near the C-terminus of MSP4. These sera were shown to contain antibodies capable of inhibiting the binding of the six Mabs indicating infection-acquired responses to the six different epitopes of MSP4. All of the six epitopes were readily recognized by human immune sera. Competition ELISA titres varied from 20 to 640, reflecting heterogeneity in the intensity of the humoral response against the protein among different individuals. The IgG responses during acute and convalescent phases of infection were higher to epitopes in the central region than to other parts of MSP4. Immunization with full length MSP4 in Freund's adjuvant induced rabbit polyclonal antisera able to inhibit parasite growth in vitro in a manner proportionate to the antibody titre. By contrast, polyclonal antisera raised to individual recombinant fragments rMSP4A, rMSP4B, rMSP4C and rMSP4D gave negligible inhibition. Similarly, murine Mabs alone or in combination did not inhibit parasite growth. CONCLUSIONS: The panel of MSP4-specific Mabs produced were found to recognize six distinct epitopes that are also targeted by human antibodies during natural malaria infection. Antibodies directed to more than three epitope regions spread across MSP4 are likely to be required for P. falciparum growth inhibition in vitro.

Computational prediction and experimental assessment of secreted/surface proteins from Mycobacterium tuberculosis H37Rv.

The mycobacterial cell envelope has been implicated in the pathogenicity of tuberculosis and therefore has been a prime target for the identification and characterization of surface proteins with potential application in drug and vaccine development. In this study, the genome of Mycobacterium tuberculosis H37Rv was screened using Machine Learning tools that included feature-based predictors, general localizers and transmembrane topology predictors to identify proteins that are potentially secreted to the surface of M. tuberculosis, or to the extracellular milieu through different secretory pathways. The subcellular localization of a set of 8 hypothetically secreted/surface candidate proteins was experimentally assessed by cellular fractionation and immunoelectron microscopy (IEM) to determine the reliability of the computational methodology proposed here, using 4 secreted/surface proteins with experimental confirmation as positive controls and 2 cytoplasmic proteins as negative controls. Subcellular fractionation and IEM studies provided evidence that the candidate proteins Rv0403c, Rv3630, Rv1022, Rv0835, Rv0361 and Rv0178 are secreted either to the mycobacterial surface or to the extracellular milieu. Surface localization was also confirmed for the positive controls, whereas negative controls were located on the cytoplasm. Based on statistical learning methods, we obtained computational subcellular localization predictions that were experimentally assessed and allowed us to construct a computational protocol with experimental support that allowed us to identify a new set of secreted/surface proteins as potential vaccine candidates.