The Immune Epitope Database and Analysis Resource Program 2003-2018: reflections and outlook

Whole proteome analysis of MDR Klebsiella pneumoniae to identify mRNA and multiple epitope based vaccine targets against emerging nosocomial and lungs associated infections

Klebsiella pneumonia is a Gram negative facultative anaerobic bacterium involved in various community-acquired pneumonia, nosocomial and lungs associated infections. Frequent usage of several antibiotics and acquired resistance mechanisms has made this bacterium multi-drug resistance (MDR), complicating the treatment of patients. To avoid the spread of this bacterium, there is an urgent need to develop a vaccine based on immuno-informatics approaches that is more efficient than conventional method of vaccine prediction or development. Initially, the complete proteomic sequence of K. pneumonia was picked over for specific and prospective vaccine targets. From the annotation of the whole proteome, eight immunogenic proteins were selected, and these shortlisted proteins were interpreted for CTL, B-cells, and HTL epitopes prediction, to construct mRNA and multi-epitope vaccines. The Antigenicity, allergenicity and toxicity analysis validate the vaccine's design, and its molecular docking was done with immuno-receptor the TLR-3. The docking interaction showed a stronger binding affinity with a minimum energy of -1153.2 kcal/mol and established 23 hydrogen bonds, 3 salt bridges, 1 disulfide bond, and 340 non-binding contacts. Further validation was done using In-silico cloning which shows the highest CAI score of 0.98 with higher GC contents of 72.25% which represents a vaccine construct with a high value of expression in E. coli. Immune Simulation shows that the antibodies (IgM, IgG1, and IgG2) production exceeded 650,000 in 2 to 3 days but the response was completely neutralized in the 5th day. In conclusion, the study provides the effective, safe and stable vaccine construct against Klebsiella pneumonia, which further needs in vitro and in vivo validations.Communicated by Ramaswamy H. Sarma.

Carbohydrate Structure Database: current state and recent developments

Carbohydrate Structure Database (CSDB) is a curated glycan data collection and a glycoinformatic platform. In this report, its database, analytical, and other components that have appeared for the recent years are reviewed. The major improvements were achieving close-to-full coverage on glycans from microorganisms, launching modules for glycosyltransferases and saccharide conformations, online glycan builder and 3D modeler, NMR simulator, NMR-based structure predictor, and other tools.

Navigating the landscape: A comprehensive overview of computational approaches in therapeutic antibody design and analysis

Immunotherapy, harnessing components like antibodies, cells, and cytokines, has become a cornerstone in treating diseases such as cancer and autoimmune disorders. Therapeutic antibodies, in particular, have transformed modern medicine, providing a targeted approach that destroys disease-causing cells while sparing healthy tissues, thereby reducing the side effects commonly associated with chemotherapy. Beyond oncology, these antibodies also hold promise in addressing chronic infections where conventional therapeutics may fall short. However, antibodies identified through in vivo or in vitro methods often require extensive engineering to enhance their therapeutic potential. This optimization process, aimed at improving affinity, specificity, and reducing immunogenicity, is both challenging and costly, often involving trade-offs between critical properties. Traditional methods of antibody development, such as hybridoma technology and display techniques, are resource-intensive and time-consuming. In contrast, computational approaches offer a faster, more efficient alternative, enabling the precise design and analysis of therapeutic antibodies. These methods include sequence and structural bioinformatics approaches, next-generation sequencing-based data mining, machine learning algorithms, systems biology, immuno-informatics, and integrative approaches. These approaches are advancing the field by providing new insights and enhancing the accuracy of antibody design and analysis. In conclusion, computational approaches are essential in the development of therapeutic antibodies, significantly improving the precision and speed of discovery, optimization, and validation. Integrating these methods with experimental approaches accelerates therapeutic antibody development, paving the way for innovative strategies and treatments for various diseases ranging from cancers to autoimmune and infectious diseases.

A study on loading multiple epitopes with a single peptide

Epitopes, the basic functional units of antigens, hold great significance in the field of immunology. However, the structure and composition of epitopes and their interactions with antibodies remain unclear, which limits in-depth studies on epitopes and the development of subunit vaccines. In a previous study on the localization of anti-influenza HA monoclonal antibodies (mAbs), three strains with different characteristics reacted with the same peptide. In this study, by conventional immunological assays, computer homology modeling, and molecular docking simulations, we found that (1) the peptide could bind to three strains of mAbs with different reaction characteristics utilizing different combinations of immunodominant groups. (2) By computer molecular docking and simulation methods, the immunodominant groups on the two peptides could be combined into a multi-epitope peptide bound to six strains of mAbs. We established a method for multi-epitope peptide recombination from these immunodominant groups. (3) The immune effect of the recombinant multi-epitope peptide was better than that of a single peptide. Our findings facilitate the understanding of the composition of antigen epitopes and provide a theoretical and experimental basis for developing polyvalent vaccines and understanding immune responses at the molecular level.

Structural and biochemical analysis of highly similar HLA-B allotypes differentially associated with type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease involving T cell-mediated destruction of the insulin-producing beta cells in the pancreatic islets of Langerhans. CD8+ T cells, responding to beta cell peptides presented by class I major histocompatibility complex (MHC) molecules, are important effectors leading to beta cell elimination. Human leukocyte antigen (HLA) B∗39:06, B∗39:01, and B∗38:01 are closely related class I MHC allotypes that nonetheless show differential association with T1D. HLA-B∗39:06 is the most predisposing of all HLA class I molecules and is associated with early age at disease onset. B∗39:01 is also associated with susceptibility to T1D, but to a lesser extent, though differing from B∗39:06 by only two amino acids. HLA-B∗38:01, in contrast, is associated with protection from the disease. Upon identifying a peptide that binds to both HLA-B∗39:06 and B∗39:01, we determined the respective X-ray structures of the two allotypes presenting this peptide to 1.7 Å resolution. The peptide residues available for T cell receptor contact and those serving as anchors were identified. Analysis of the F pocket of HLA-B∗39:06 and B∗39:01 provided an explanation for the distinct peptide C terminus preferences of the two allotypes. Structure-based modeling of the protective HLA-B∗38:01 suggested a potential reason for its peptide preferences and its reduced propensity to present 8-mer peptides compared to B∗39:06. Notably, the three allotypes showed differential binding to peptides derived from beta cell autoantigens. Taken together, our findings should facilitate identification of disease-relevant candidate T cell epitopes and structure-guided therapeutics to interfere with peptide binding.

An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus

The zoonotic viruses pose significant threats to public health. Nipah virus (NiV) is an emerging virus transmitted from bats to humans. The NiV causes severe encephalitis and acute respiratory distress syndrome, leading to high mortality rates, with fatality rates ranging from 40% to 75%. The first emergence of the disease was found in Malaysia in 1998-1999 and later in Bangladesh, Cambodia, Timor-Leste, Indonesia, Singapore, Papua New Guinea, Vietnam, Thailand, India, and other South and Southeast Asian nations. Currently, no specific vaccines or antiviral drugs are available. The potential advantages of epitope-based vaccines include their ability to elicit specific immune responses while minimizing potential side effects. The epitopes have been identified from the conserved region of viral proteins obtained from the UniProt database. The selection of conserved epitopes involves analyzing the genetic sequences of various viral strains. The present study identified two B cell epitopes, seven cytotoxic T lymphocyte (CTL) epitopes, and seven helper T lymphocyte (HTL) epitope interactions from the NiV proteomic inventory. The antigenic and physiological properties of retrieved protein were analyzed using online servers ToxinPred, VaxiJen v2.0, and AllerTOP. The final vaccine candidate has a total combined coverage range of 80.53%. The tertiary structure of the constructed vaccine was optimized, and its stability was confirmed with the help of molecular simulation. Molecular docking was performed to check the binding affinity and binding energy of the constructed vaccine with TLR-3 and TLR-5. Codon optimization was performed in the constructed vaccine within the Escherichia coli K12 strain, to eliminate the danger of codon bias. However, these findings must require further validation to assess their effectiveness and safety. The development of vaccines and therapeutic approaches for virus infection is an ongoing area of research, and it may take time before effective interventions are available for clinical use.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

In Silico and In Vitro Evaluation of the Molecular Mimicry of the SARS-CoV-2 Spike Protein by Common Short Constituent Sequences (cSCSs) in the Human Proteome: Toward Safer Epitope Design for Vaccine Development

Spike protein sequences in SARS-CoV-2 have been employed for vaccine epitopes, but many short constituent sequences (SCSs) in the spike protein are present in the human proteome, suggesting that some anti-spike antibodies induced by infection or vaccination may be autoantibodies against human proteins. To evaluate this possibility of "molecular mimicry" in silico and in vitro, we exhaustively identified common SCSs (cSCSs) found both in spike and human proteins bioinformatically. The commonality of SCSs between the two systems seemed to be coincidental, and only some cSCSs were likely to be relevant to potential self-epitopes based on three-dimensional information. Among three antibodies raised against cSCS-containing spike peptides, only the antibody against EPLDVL showed high affinity for the spike protein and reacted with an EPLDVL-containing peptide from the human unc-80 homolog protein. Western blot analysis revealed that this antibody also reacted with several human proteins expressed mainly in the small intestine, ovary, and stomach. Taken together, these results showed that most cSCSs are likely incapable of inducing autoantibodies but that at least EPLDVL functions as a self-epitope, suggesting a serious possibility of infection-induced or vaccine-induced autoantibodies in humans. High-risk cSCSs, including EPLDVL, should be excluded from vaccine epitopes to prevent potential autoimmune disorders.

An Aedes-Anopheles Vaccine Candidate Supplemented with BCG Epitopes Against the Aedes and Anopheles Genera to Overcome Hypersensitivity to Mosquito Bites

BACKGROUND

Skeeter syndrome is a severe local allergic response to mosquito bites that is accompanied by considerable inflammation and, in some cases, a systemic response like fever. People with the syndrome develop serious allergies, ranging from rashes to anaphylaxis or shock. The few available studies on mosquito venom immunotherapy have utilized whole-body preparations and small sample sizes. Still, owing to their little success, vaccination remains a promising alternative as well as a permanent solution for infections like Skeeter's.

METHODS

This study, therefore, illustrated the construction of an epitope-based vaccine candidate against Skeeter Syndrome using established immunoinformatic techniques. We selected three species of mosquitoes, Anopheles melas, Anopheles funestus, and Aedes aegypti, to derive salivary antigens usually found in mosquito bites. Our construct was also supplemented with bacterial epitopes known to elicit a strong TH1 response and suppress TH2 stimulation that is predicted to reduce hypersensitivity against the bites.

RESULTS

A quality factor of 98.9496, instability index of 38.55, aliphatic index of 79.42, solubility of 0.934747, and GRAVY score of -0.02 indicated the structural (tertiary and secondary) stability, thermostability, solubility, and hydrophilicity of the construct, respectively. The designed Aedes-Anopheles vaccine (AAV) candidate was predicted to be flexible and less prone to deformability with an eigenvalue of 1.5911e-9 and perfected the human immune response against Skeeter (hypersensitivity) and many mosquito-associated diseases as we noted the production of 30,000 Th1 cells per mm3 with little (insignificant production of Th2 cells. The designed vaccine also revealed stable interactions with the pattern recognition receptors of the host. The TLR2/vaccine complex interacted with a free energy of - 1069.2 kcal/mol with 26 interactions, whereas the NLRP3/vaccine complex interacted with a free energy of - 1081.2 kcal/mol with 16 molecular interactions.

CONCLUSION

Although being a pure in-silico study, the in-depth analysis performed herein speaks volumes of the potency of the designed vaccine candidate predicting that the proposition can withstand rigorous in-vitro and in-vivo clinical trials and may proceed to become the first preventative immunotherapy against mosquito bite allergy.

Rational immunosilencing of a promiscuous T-cell epitope in the capsid of an adeno-associated virus

Owing to the immunogenicity of adeno-associated viruses (AAVs), gene therapies using AAVs face considerable obstacles. Here, by leveraging ex vivo T-cell assays, the prediction of epitope binding to major histocompatibility complex class-II alleles, sequence-conservation analysis in AAV phylogeny and site-directed mutagenesis, we show that the replacement of amino acid residues in a promiscuous and most immunodominant T-cell epitope in the AAV9 capsid with AAV5 sequences abrogates the immune responses of peripheral blood mononuclear cells to the chimaeric vector while preserving its functions, potency, cellular specificity, transduction efficacy and biodistribution. This rational approach to the immunosilencing of capsid epitopes promiscuously binding to T cells may be applied to other AAV vectors and epitope regions.

MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.

Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.

Re-Evaluating Human Cytomegalovirus Vaccine Design: Prediction of T Cell Epitopes

HCMV vaccine development has traditionally focused on viral antigens identified as key targets of neutralizing antibody (NAb) and/or T cell responses in healthy adults with chronic HCMV infection, such as glycoprotein B (gB), the glycoprotein H-anchored pentamer complex (PC), and the unique long 83 (UL83)-encoded phosphoprotein 65 (pp65). However, the protracted absence of a licensed HCMV vaccine that reduces the risk of infection in pregnancy regardless of serostatus warrants a systematic reassessment of assumptions informing vaccine design. To illustrate this imperative, we considered the hypothesis that HCMV proteins infrequently detected as targets of T cell responses may contain important vaccine antigens. Using an extant dataset from a T cell profiling study, we tested whether HCMV proteins recognized by only a small minority of participants encompass any T cell epitopes. Our analyses demonstrate a prominent skewing of T cell responses away from most viral proteins-although they contain robust predicted CD8 T cell epitopes-in favor of a more restricted set of proteins. Our findings raise the possibility that HCMV may benefit from evading the T cell recognition of certain key proteins and that, contrary to current vaccine design approaches, including them as vaccine antigens could effectively take advantage of this vulnerability.

In Silico Analyses, Experimental Verification and Application in DNA Vaccines of Ebolavirus GP-Derived pan-MHC-II-Restricted Epitopes

(1) Background and Purpose: Ebola virus (EBOV) is the causative agent of Ebola virus disease (EVD), which causes extremely high mortality and widespread epidemics. The only glycoprotein (GP) on the surface of EBOV particles is the key to mediating viral invasion into host cells. DNA vaccines for EBOV are in development, but their effectiveness is unclear. The lack of immune characteristics resides in antigenic MHC class II reactivity. (2) Methods: We selected MHC-II molecules from four human leukocyte antigen II (HLA-II) superfamilies with 98% population coverage and eight mouse H2-I alleles. IEDB, NetMHCIIpan, SYFPEITHI, and Rankpep were used to screen MHC-II-restricted epitopes with high affinity for EBOV GP. Further immunogenicity and conservation analyses were performed using VaxiJen and BLASTp, respectively. EpiDock was used to simulate molecular docking. Cluster analysis and binding affinity analysis of EBOV GP epitopes and selected MHC-II molecules were performed using data from NetMHCIIpan. The selective GP epitopes were verified by the enzyme-linked immunospot (ELISpot) assay using splenocytes of BALB/c (H2d), C3H, and C57 mice after DNA vaccine pVAX-GPEBO immunization. Subsequently, BALB/c mice were immunized with Protein-GPEBO, plasmid pVAX-GPEBO, and pVAX-LAMP/GPEBO, which encoded EBOV GP. The dominant epitopes of BALB/c (H-2-I-AdEd genotype) mice were verified by the enzyme-linked immunospot (ELISpot) assay. It is also used to evaluate and explore the advantages of pVAX-LAMP/GPEBO and the reasons behind them. (3) Results: Thirty-one HLA-II-restricted and 68 H2-I-restricted selective epitopes were confirmed to have high affinity, immunogenicity, and conservation. Nineteen selective epitopes have cross-species reactivity with good performance in MHC-II molecular docking. The ELISpot results showed that pVAX-GPEBO could induce a cellular immune response to the synthesized selective peptides. The better immunoprotection of the DNA vaccines pVAX-LAMP/GPEBO coincides with the enhancement of the MHC class II response. (4) Conclusions: Promising MHC-II-restricted candidate epitopes of EBOV GP were identified in humans and mice, which is of great significance for the development and evaluation of Ebola vaccines.

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases

T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

Antigen-specificity measurements are the key to understanding T cell responses

Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.

T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy

T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.

Structure-free antibody paratope similarity prediction for in silico epitope binning via protein language models

Antibodies are an important group of biological molecules that are used as therapeutics and diagnostic tools. Although millions of antibody sequences are available, identifying their structural and functional similarity and their antigen binding sites remains a challenge at large scale. Here, we present a fast, sequence-based computational method for antibody paratope prediction based on protein language models. The paratope information is then used to measure similarity among antibodies via protein language models. Our computational method enables binning of antibody discovery hits into groups as the function of epitope engagement. We further demonstrate the utility of the method by identifying antibodies targeting highly similar epitopes of the same antigens from a large pool of antibody sequences, using two case studies: SARS CoV2 Receptor Binding Domain (RBD) and Epidermal Growth Factor Receptor (EGFR). Our approach highlights the potential in accelerating antibody discovery by enhancing hit prioritization and diversity selection.

Immunoinformatics Approach for Epitope-Based Vaccine Design: Key Steps for Breast Cancer Vaccine

Vaccines are an upcoming medical intervention for breast cancer. By targeting the tumor antigen, cancer vaccines can be designed to train the immune system to recognize tumor cells. Therefore, along with technological advances, the vaccine design process is now starting to be carried out with more rational methods such as designing epitope-based peptide vaccines using immunoinformatics methods. Immunoinformatics methods can assist vaccine design in terms of antigenicity and safety. Common protocols used to design epitope-based peptide vaccines include tumor antigen identification, protein structure analysis, T cell epitope prediction, epitope characterization, and evaluation of protein-epitope interactions. Tumor antigen can be divided into two types: tumor associated antigen and tumor specific antigen. We will discuss the identification of tumor antigens using high-throughput technologies. Protein structure analysis comprises the physiochemical, hydrochemical, and antigenicity of the protein. T cell epitope prediction models are widely available with various prediction parameters as well as filtering tools for the prediction results. Epitope characterization such as allergenicity and toxicity can be done in silico as well using allergenicity and toxicity predictors. Evaluation of protein-epitope interactions can also be carried out in silico with molecular simulation. We will also discuss current and future developments of breast cancer vaccines using an immunoinformatics approach. Finally, although prediction models have high accuracy, the opposite can happen after being tested in vitro and in vivo. Therefore, further studies are needed to ensure the effectiveness of the vaccine to be developed. Although epitope-based peptide vaccines have the disadvantage of low immunogenicity, the addition of adjuvants can be a solution.

Viral Shedding 1 Year Following First-Episode Genital HSV-1 Infection

IMPORTANCE

Herpes simplex virus type 1 (HSV-1) is the leading cause of first-episode genital herpes in many countries.

OBJECTIVE

To inform counseling messages regarding genital HSV-1 transmission, oral and genital viral shedding patterns among persons with first-episode genital HSV-1 infection were assessed. The trajectory of the development of HSV-specific antibody and T-cell responses was also characterized.

DESIGN, SETTING, AND PARTICIPANTS

Prospective cohort followed up for up to 2 years, with 82 participants followed up between 2013 and 2018. Participants were recruited from sexual health and primary care clinics in Seattle, Washington. Persons with laboratory-documented first-episode genital HSV-1 infection, without HIV infection or current pregnancy, were referred for enrollment.

EXPOSURES

First-episode genital HSV-1 infection.

MAIN OUTCOMES AND MEASURES

Genital and oral HSV-1 shedding and lesion rates at 2 months, 11 months, and up to 2 years after initial genital HSV-1 infection. Participants self-collected oral and genital swabs for HSV polymerase chain reaction testing for 30 days at 2 and 11 months and up to 2 years after diagnosis of genital HSV-1. Blood samples were collected at serial time points to assess immune responses to HSV-1. Primary HSV-1 infection was defined as absent HSV antibody at baseline or evolving antibody profile using the University of Washington HSV Western Blot. HSV-specific T-cell responses were detected using interferon γ enzyme-linked immunospot.

RESULTS

Among the 82 participants, the median (range) age was 26 (16-64) years, 54 (65.9%) were women, and 42 (51.2%) had primary HSV-1 infection. At 2 months, HSV-1 was detected from the genital tract in 53 participants (64.6%) and in the mouth in 24 participants (29.3%). Genital HSV-1 shedding was detected on 275 of 2264 days (12.1%) at 2 months and declined significantly to 122 of 1719 days (7.1%) at 11 months (model-predicted rate, 6.2% [95% CI, 4.3%-8.9%] at 2 months vs 3.2% [95% CI, 1.8%-5.7%] at 11 months; relative risk, 0.52 [95% CI, 0.29-0.93]). Genital lesions were rare, reported on 65 of 2497 days (2.6%) at 2 months and 72 of 1872 days (3.8%) at 11 months. Oral HSV-1 shedding was detected on 88 of 2247 days (3.9%) at 2 months. Persons with primary HSV-1 infection had a higher risk of genital shedding compared with those with nonprimary infection (model-predicted rate, 7.9% [95% CI, 5.4%-11.7%] vs 2.9% [95% CI, 1.7%-5.0%]; relative risk, 2.75 [95% CI, 1.40-5.44]). Polyfunctional HSV-specific CD4+ and CD8+ T-cell responses were maintained during the follow-up period.

CONCLUSIONS AND RELEVANCE

Genital HSV-1 shedding was frequent after first-episode genital HSV-1, particularly among those with primary infection, and declined rapidly during the first year after infection.

In silico designing of a recombinant multi-epitope antigen for leprosy diagnosis

BACKGROUND

Leprosy is caused by Mycobacterium leprae and Mycobacterium lepromatosis. Most of the affected population lives in low-income countries and may take up to 10 years to show any clinical signs, which is how physicians diagnose it. However, due to progressive cell damage, early diagnosis is very important. The best way to confirm leprosy is through bacilloscopic, which only confirms the diagnosis and has low accuracy or PCR, that requires specialized operators and is expensive. Since the bacteria are fastidious and do not grow in any culture media, therefore, diagnosing leprosy in the lab is still a challenge. In this concern, a recombinant multi-epitope protein can be a beneficial strategy in the management of the diagnosis, as diverse immunogenic epitopes are precisely selected to detect specific antibodies. Therefore, the purposes of the present study were to select immunogenic epitopes from different relevant proteins, with immunogenic properties, and then to construct a recombinant multi-epitope protein that accuses the presence of the antibodies in the early stages of the disease, making it more than appropriate to be applied as a diagnostic tool.

RESULTS

We selected 22 common proteins from both species and, using bioinformatics tools, predicted B and T cell epitopes. After multiple filtering and analyzing, we ended up with 29 epitopes {MHC-I (total 18) and MHC-II (total 11)} from 10 proteins, which were then merged into one construct. Its secondary and tertiary structures were also predicted and refined to comprise the amino acid residues in the best conformation possible. The multi-epitope protein construct was stable, non-host homologous, non-allergic, non-toxic, and elicit humoral and cellular responses. It has conformational B cell epitopes and potential to elicit IFN-γ, IL-4, and IL-10 secretion.

CONCLUSIONS

This novel recombinant multi-epitope protein constructed using the common epitopes from M. leprae and M. lepromatosis has a huge immunological potential, is stable, and can be lyophilized to be used in ELISA plates or even in biosensors, which are user-friendly diagnosis tools, facilitating translation into human sample tests.

Robust tolerogenic dendritic cells via push/pull pairing of toll-like-receptor agonists and immunomodulators reduces EAE

A failure of central immune tolerance driven by autoantigen specific T regulatory (Treg) cells is a major cause of many autoimmune diseases. Restoration of proper autoantigen Treg specific response holds promise as a highly effective, long-term therapy for a wide variety of autoimmune diseases. Generating autoantigen specific Tregs remains a challenge due to the non-specific nature of most tolerizing agents and the complexities of generating Tregs in vivo. Here we show a new push/pull method for inducing antigen-specific Treg tolerance via induction of tolerogenic dendritic cells (tolDCs). We identified a combination of three tolerogenic drugs, dexamethasone, simvastatin and SC-514, which when used in combination with toll-like-receptor (TLR) agonists induces an active tolDC phenotype. When the tolerogenic combination was packaged into a liposome with a model antigen such as ovalbumin (OVA), these tolDCs induce differentiation of OVA specific Tregs both ex vivo and in vivo. We examined the tolerizing potential of the combination in an experimental autoimmune encephalomyelitis (EAE) disease model. Given the antigen specificity of this technique, this paper presents an attractive preclinical autoimmune therapy.

In Silico Designed Multi-Epitope Immunogen “Tpme-VAC/LGCM-2022” May Induce Both Cellular and Humoral Immunity against Treponema pallidum Infection

Syphilis, a sexually transmitted infection caused by the spirochete Treponema pallidum, has seen a resurgence over the past years. T. pallidum is capable of early dissemination and immune evasion, and the disease continues to be a global healthcare burden. The purpose of this study was to design a multi-epitope immunogen through an immunoinformatics-based approach. Multi-epitope immunogens constitute carefully selected epitopes belonging to conserved and essential bacterial proteins. Several physico-chemical characteristics, such as antigenicity, allergenicity, and stability, were determined. Further, molecular docking and dynamics simulations were performed, ensuring binding affinity and stability between the immunogen and TLR-2. An in silico cloning was performed using the pET-28a(+) vector and codon adaptation for E. coli. Finally, an in silico immune simulation was performed. The in silico predictions obtained in this work indicate that this construct would be capable of inducing the requisite immune response to elicit protection against T. pallidum. Through this methodology we have designed a promising potential vaccine candidate for syphilis, namely Tpme-VAC/LGCM-2022. However, it is necessary to validate these findings in in vitro and in vivo assays.

CAVES: A Novel Tool for Comparative Analysis of Variant Epitope Sequences

In silico methods for immune epitope prediction have become essential for vaccine and therapeutic design, but manual intra-species comparison of putative epitopes remains challenging and subject to human error. Created initially for analyzing SARS-CoV-2 variants of concern, comparative analysis of variant epitope sequences (CAVES) is a novel tool designed to carry out rapid comparative analyses of epitopes amongst closely related pathogens, substantially reducing the required time and user workload. CAVES applies a two-level analysis approach. The Level-one (L1) analysis compares two epitope prediction files, and the Level-two (L2) analysis incorporates search results from the IEDB database of experimentally confirmed epitopes. Both L1 and L2 analyses sort epitopes into categories of exact matches, partial matches, or novel epitopes based on the degree to which they match with peptides from the compared file. Furthermore, CAVES uses positional sequence data to improve its accuracy and speed, taking only a fraction of the time required by manual analyses and minimizing human error. CAVES is widely applicable for evolutionary analyses and antigenic comparisons of any closely related pathogen species. CAVES is open-source software that runs through a graphical user interface on Windows operating systems, making it widely accessible regardless of coding expertise. The CAVES source code and test dataset presented here are publicly available on the CAVES GitHub page.

Proteomics for Development of Food Allergy Vaccines

Food allergy is a hypersensitivity reaction to food products initiated by immunologic mechanisms, which represents one of the major concerns in food safety. New therapies for food allergies including oral and epicutaneous allergen-specific immunotherapy are required, and B cell epitope-based allergy vaccines are a good promise to improve this field. In this chapter, we describe a workflow for the design of food allergy vaccines using proteomic tools. The strategy is defined based on the characterization of B cell epitopes for a particular food allergen. For that, the workflow comprises five consecutive steps: (1) shotgun proteomics analysis of different protein isoforms for a particular food allergen, (2) downloading all protein sequences for the specific allergen included in UniProtKB database, (3) analysis by protein-based bioinformatics of B cell epitopes, (4) synthesizing of the selected B cell peptide epitopes, and (5) performing of immunoassays using sera from healthy and allergic patients. The results from this method provide a rationale repository of B cell epitopes for the design of new specific immunotherapies for a particular food allergen. The strategy was optimized for all the beta-parvalbumins (β-PRVBs), which are considered as the main fish allergens. Using this workflow, a total of 35 peptides were identified as B cell epitopes, among them the top 4 B cell peptide epitopes that may induce protective immune response were selected as potential peptide vaccine candidates for fish allergy.

Therapeutic Antibodies: An Overview

Polyclonal immunoglobulin (Ig) preparations have been used for several decades for treatment of primary and secondary immunodeficiencies and for treatment of some infections and intoxications. This has demonstrated the importance of Igs, also called antibodies (Abs) for prevention and elimination of infections. Moreover, elucidation of the structure and functions of Abs has suggested that they might be useful for targeted treatment of several diseases, including cancers and autoimmune diseases. The development of technologies for production of specific monoclonal Abs (MAbs) in large amounts has led to the production of highly effective therapeutic antibodies (TAbs), a collective term for MAbs (MAbs) with demonstrated clinical efficacy in one or more diseases. The number of approved TAbs is currently around hundred, and an even larger number is under development, including several engineered and modified Ab formats. The use of TAbs has provided new treatment options for many severe diseases, but prediction of clinical effect is difficult, and many patients eventually lose effect, possibly due to development of Abs to the TAbs or to other reasons. The therapeutic efficacy of TAbs can be ascribed to one or more effects, including binding and neutralization of targets, direct cytotoxicity, Ab-dependent complement-dependent cytotoxicity, Ab-dependent cellular cytotoxicity or others. The therapeutic options for TAbs have been expanded by development of several new formats of TAbs, including bispecific Abs, single domain Abs, TAb-drug conjugates, and the use of TAbs for targeted activation of immune cells. Most promisingly, current research and development can be expected to increase the number of clinical conditions, which may benefit from TAbs.

An immunoinformatics-based designed multi-epitope candidate vaccine (mpme-VAC/STV-1) against Mycoplasma pneumoniae

Pneumonia is a serious global health problem that accounts for over one million deaths annually. Among the main microorganisms causing pneumonia, Mycoplasma pneumoniae is one of the most common ones for which a vaccine is immediately required. In this context, a multi-epitope vaccine against this pathogen could be the best option that can induce effective immune response avoiding any serious adverse reactions. In this study, using an immunoinformatics approach we have designed a multi-epitope vaccine (mpme-VAC/STV-1) against M. pneumoniae. Our designed mpme-VAC/STV-1 is constructed using CTL (cytotoxic T lymphocyte), HTL (Helper T lymphocyte), and B-cell epitopes. These epitopes are selected from the core proteins of 88 M. pneumoniae genomes that were previously identified through reverse vaccinology approaches. The epitopes were filtered according to their immunogenicity, population coverage, and several other criteria. Sixteen CTL/B- and thirteen HTL/B- epitopes that belong to 5 core proteins were combined together through peptide linkers to develop the mpme-VAC/STV-1. The heat-labile enterotoxin from E. coli was used as an adjuvant. The designed mpme-VAC/STV-1 is predicted to be stable, non-toxic, non-allergenic, non-host homologous, and with required antigenic and immunogenic properties. Docking and molecular dynamic simulation of mpme-VAC/STV-1 shows that it can stimulate TLR2 pathway mediated immunogenic reactions. In silico cloning of mpme-VAC/STV-1 in an expression vector also shows positive results. Finally, the mpme-VAC/STV-1 also shows promising efficacy in immune simulation tests. Therefore, our constructed mpme-VAC/STV-1 could be a safe and effective multi-epitope vaccine for immunization against pneumonia. However, it requires further experimental and clinical validations.

The Longest Persistence of Viable SARS-CoV-2 With Recurrence of Viremia and Relapsing Symptomatic COVID-19 in an Immunocompromised Patient-A Case Study

BACKGROUND

Immunocompromised patients show prolonged shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in nasopharyngeal swabs. We report a case of prolonged persistence of viable SARS-CoV-2 associated with clinical relapses of coronavirus disease 2019 (COVID-19) in a patient with mantle cell lymphoma who underwent treatment with rituximab, bendamustine, cytarabine with consequent lymphopenia and hypogammaglobulinemia.

METHODS

Nasopharyngeal swabs and blood samples were tested for SARS-CoV-2 by real-time polymerase chain reaction (RT-PCR). On 5 positive nasopharyngeal swabs, we performed viral culture and next-generation sequencing. We analyzed the patient's adaptive and innate immunity to characterize T- and NK-cell subsets.

RESULTS

SARS-CoV-2 RT-PCR on nasopharyngeal swabs samples remained positive for 268 days. All 5 performed viral cultures were positive, and genomic analysis confirmed a persistent infection with the same strain. Viremia resulted positive in 3 out of 4 COVID-19 clinical relapses and cleared each time after remdesivir treatment. The T- and NK-cell dynamic was different in aviremic and viremic samples, and no SARS-CoV-2-specific antibodies were detected throughout the disease course.

CONCLUSIONS

In our patient, SARS-CoV-2 persisted with proven infectivity for >8 months. Viremia was associated with COVID-19 relapses, and remdesivir treatment was effective in viremia clearance and symptom remission, although it was unable to clear the virus from the upper respiratory airways. During the viremic phase, we observed a low frequency of terminal effector CD8+ T lymphocytes in peripheral blood; these are probably recruited in inflammatory tissue for viral eradication. In addition, we found a high level of NK-cell repertoire perturbation with relevant involvement during SARS-CoV-2 viremia.

APRANK: Computational Prioritization of Antigenic Proteins and Peptides From Complete Pathogen Proteomes

Availability of highly parallelized immunoassays has renewed interest in the discovery of serology biomarkers for infectious diseases. Protein and peptide microarrays now provide a rapid, high-throughput platform for immunological testing and validation of potential antigens and B-cell epitopes. However, there is still a need for tools to prioritize and select relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize potentially antigenic proteins and peptides in a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We trained and tested this method with a number of bacteria and protozoa causing human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). We have evaluated this integrative method using non-parametric ROC-curves and made an unbiased validation using Onchocerca volvulus as an independent data set. We found that APRANK is successful in predicting antigenicity for all pathogen species tested, facilitating the production of antigen-enriched protein subsets. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Noncontiguous T cell epitopes in autoimmune diabetes: From mice to men and back again

Type 1 diabetes (T1D) is a T cell-mediated autoimmune disease that affects the insulin-producing beta cells of the pancreatic islets. The nonobese diabetic mouse is a widely studied spontaneous model of the disease that has contributed greatly to our understanding of T1D pathogenesis. This is especially true in the case of antigen discovery. Upon review of existing knowledge concerning the antigens and peptide epitopes that are recognized by T cells in this model, good concordance is observed between mouse and human antigens. A fascinating recent illustration of the contribution of the nonobese diabetic mouse in the area of epitope identification is the discovery of noncontiguous CD4+ T cell epitopes. This novel epitope class is characterized by the linkage of an insulin-derived peptide to, most commonly, a fragment of a natural cleavage product of another beta cell secretory granule constituent. These so-called hybrid insulin peptides are also recognized by T cells in patients with T1D, although the precise mechanism for their generation has yet to be defined and is the subject of active investigation. Although evidence from the tumor immunology arena documented the existence of noncontiguous CD8+ T cell epitopes, generated by proteasome-mediated peptide splicing involving transpeptidation, such CD8+ T cell epitopes were thought to be a rare immunological curiosity. However, recent advances in bioinformatics and mass spectrometry have challenged this view. These developments, coupled with the discovery of hybrid insulin peptides, have spurred a search for noncontiguous CD8+ T cell epitopes in T1D, an exciting frontier area still in its infancy.

Designing Multi-Antigen Vaccines Against Acinetobacter baumannii Using Systemic Approaches

Vaccines and monoclonal antibodies are promising approaches for preventing and treating infections caused by multidrug resistant Acinetobacter baumannii. However, only partial protection has been achieved with many previously tested protein antigens, which suggests that vaccines incorporating multiple antigens may be necessary in order to obtain high levels of protection. Several aspects that use the wealth of omic data available for A. baumannii have not been fully exploited for antigen identification. In this study, the use of fractionated proteomic and computational data from ~4,200 genomes increased the number of proteins potentially accessible to the humoral response to 8,824 non-redundant proteins in the A. baumannii panproteome. Among them, 59% carried predicted B-cell epitopes and T-cell epitopes recognized by two or more alleles of the HLA class II DP supertype. Potential cross-reactivity with human proteins was detected for 8.9% of antigens at the protein level and 2.7% at the B-cell epitope level. Individual antigens were associated with different infection types by genomic, transcriptomic or functional analyses. High intra-clonal genome density permitted the identification of international clone II as a “vaccitype”, in which 20% of identified antigens were specific to this clone. Network-based centrality measurements were used to identify multiple immunologic nodes. Data were formatted, unified and stored in a data warehouse database, which was subsequently used to identify synergistic antigen combinations for different vaccination strategies. This study supports the idea that integration of multi-omic data and fundamental knowledge of the pathobiology of drug-resistant bacteria can facilitate the development of effective multi-antigen vaccines against these challenging infections.

Epitope similarity cannot explain the pre-formed T cell immunity towards structural SARS-CoV-2 proteins

The current pandemic is caused by the SARS-CoV-2 virus and large progress in understanding the pathology of the virus has been made since its emergence in late 2019. Several reports indicate short lasting immunity against endemic coronaviruses, which contrasts studies showing that biobanked venous blood contains T cells reactive to SARS-CoV-2 S-protein even before the outbreak in Wuhan. This suggests a preformed T cell memory towards structural proteins in individuals not exposed to SARS-CoV-2. Given the similarity of SARS-CoV-2 to other members of the Coronaviridae family, the endemic coronaviruses appear likely candidates to generate this T cell memory. However, given the apparent poor immunological memory created by the endemic coronaviruses, immunity against other common pathogens might offer an alternative explanation. Here, we utilize a combination of epitope prediction and similarity to common human pathogens to identify potential sources of the SARS-CoV-2 T cell memory. Although beta-coronaviruses are the most likely candidates to explain the pre-existing SARS-CoV-2 reactive T cells in uninfected individuals, the SARS-CoV-2 epitopes with the highest similarity to those from beta-coronaviruses are confined to replication associated proteins-not the host interacting S-protein. Thus, our study suggests that the observed SARS-CoV-2 pre-formed immunity to structural proteins is not driven by near-identical epitopes.

Epitope prediction and identification- adaptive T cell responses in humans

Epitopes, in the context of T cell recognition, are short peptides typically derived by antigen processing, and presented on the cell surface bound to MHC molecules (HLA molecules in humans) for TCR scrutiny. The identification of epitopes is a context-dependent process, with consideration given to, for example, the source pathogen and protein, the host organism, and state of the immune reaction (e.g., following natural infection, vaccination, etc.). In the following review, we consider the various approaches used to define T cell epitopes, including both bioinformatic and experimental approaches, and discuss the concepts of immunodominance and immunoprevalence. We also discuss HLA polymorphism and epitope restriction, and the resulting impact on the identification of, and potential population coverage afforded by, epitopes or epitope-based vaccines. Finally, some examples of the practical application of T cell epitope identification are provided, showing how epitopes have been valuable for deriving novel immunological insights in the context of the immune response to various pathogens and allergens.

Pancreatlas: Applying an Adaptable Framework to Map the Human Pancreas in Health and Disease

Human tissue phenotyping generates complex spatial information from numerous imaging modalities, yet images typically become static figures for publication, and original data and metadata are rarely available. While comprehensive image maps exist for some organs, most resources have limited support for multiplexed imaging or have non-intuitive user interfaces. Therefore, we built a Pancreatlas resource that integrates several technologies into a unique interface, allowing users to access richly annotated web pages, drill down to individual images, and deeply explore data online. The current version of Pancreatlas contains over 800 unique images acquired by whole-slide scanning, confocal microscopy, and imaging mass cytometry, and is available at https://www.pancreatlas.org. To create this human pancreas-specific biological imaging resource, we developed a React-based web application and Python-based application programming interface, collectively called Flexible Framework for Integrating and Navigating Data (FFIND), which can be adapted beyond Pancreatlas to meet countless imaging or other structured data-management needs.

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Human organ phenotyping databases benefit from intuitive user interfaces

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Pancreatlas resource enables exploration of bioimaging data from human pancreas

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The front-end framework of Pancreatlas, FFIND, is modular and easily adaptable

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FFIND provides structured data-exploration capabilities across countless domains

Scientists need cost-effective yet fully featured database solutions that facilitate large dataset sharing in a structured and easily digestible manner. Flexible Framework for Integrating and Navigating Data (FFIND) is a data-agnostic web application that is designed to easily connect existing databases with data-browsing clients. We used FFIND to build Pancreatlas, an online imaging resource containing datasets linking imaging data with clinical data to facilitate advances in the understanding of diabetes, pancreatitis, and pancreatic cancer. FFIND architecture, which is available as open-source software, can be easily adapted to meet other field- or project-specific needs; we hope it will help data scientists reach a broader audience by reducing the development life cycle and providing familiar interactivity in communicating data and underlying stories.

A Note on the Potential BCG Vaccination – COVID-19 Molecular Link

Objective:

Our goal was to elucidate a potential molecular link between the past and current tuberculosis vaccine Bacillus Calmette-Guérin (BCG; a live attenuated strain of Mycobacterium bovis) immunization policies and COVID-19.

Methods:

Our sequence homology analyses have demonstrated that there is an intriguing level of sequence homology between a few of the BCG and Sars-CoV-2 proteins.

Results:

The data suggest that the BCG-specific memory B-cells that are preserved in BCG-vaccinated patients cross-recognize SARS-CoV-2 and that this cross-recognition may affect the virus proliferation and COVID-19 severity.

Conclusion:

Our results can stimulate the sharply focused follow-up experimental studies.

Identification of Novel Candidate Epitopes on SARS-CoV-2 Proteins for South America: A Review of HLA Frequencies by Country

Coronavirus disease (COVID-19), caused by the virus SARS-CoV-2, is already responsible for more than 4.3 million confirmed cases and 295,000 deaths worldwide as of May 15, 2020. Ongoing efforts to control the pandemic include the development of peptide-based vaccines and diagnostic tests. In these approaches, HLA allelic diversity plays a crucial role. Despite its importance, current knowledge of HLA allele frequencies in South America is very limited. In this study, we have performed a literature review of datasets reporting HLA frequencies of South American populations, available in scientific literature and/or in the Allele Frequency Net Database. This allowed us to enrich the current scenario with more than 12.8 million data points. As a result, we are presenting updated HLA allelic frequencies based on country, including 91 alleles that were previously thought to have frequencies either under 5% or of an unknown value. Using alleles with an updated frequency of at least ≥5% in any South American country, we predicted epitopes in SARS-CoV-2 proteins using NetMHCpan (I and II) and MHC flurry. Then, the best predicted epitopes (class-I and -II) were selected based on their binding to South American alleles (Coverage Score). Class II predicted epitopes were also filtered based on their three-dimensional exposure. We obtained 14 class-I and four class-II candidate epitopes with experimental evidence (reported in the Immune Epitope Database and Analysis Resource), having good coverage scores for South America. Additionally, we are presenting 13 HLA-I and 30 HLA-II novel candidate epitopes without experimental evidence, including 16 class-II candidates in highly exposed conserved areas of the NTD and RBD regions of the Spike protein. These novel candidates have even better coverage scores for South America than those with experimental evidence. Finally, we show that recent similar studies presenting candidate epitopes also predicted some of our candidates but discarded them in the selection process, resulting in candidates with suboptimal coverage for South America. In conclusion, the candidate epitopes presented provide valuable information for the development of epitope-based strategies against SARS-CoV-2, such as peptide vaccines and diagnostic tests. Additionally, the updated HLA allelic frequencies provide a better representation of South America and may impact different immunogenetic studies.

A Systematic, Unbiased Mapping of CD8+ and CD4+ T Cell Epitopes in Yellow Fever Vaccinees

Examining CD8+ and CD4+ T cell responses after primary Yellow Fever vaccination in a cohort of 210 volunteers, we have identified and tetramer-validated 92 CD8+ and 50 CD4+ T cell epitopes, many inducing strong and prevalent (i.e., immunodominant) T cell responses. Restricted by 40 and 14 HLA-class I and II allotypes, respectively, these responses have wide population coverage and might be of considerable academic, diagnostic and therapeutic interest. The broad coverage of epitopes and HLA overcame the otherwise confounding effects of HLA diversity and non-HLA background providing the first evidence of T cell immunodomination in humans. Also, double-staining of CD4+ T cells with tetramers representing the same HLA-binding core, albeit with different flanking regions, demonstrated an extensive diversification of the specificities of many CD4+ T cell responses. We suggest that this could reduce the risk of pathogen escape, and that multi-tetramer staining is required to reveal the true magnitude and diversity of CD4+ T cell responses. Our T cell epitope discovery approach uses a combination of (1) overlapping peptides representing the entire Yellow Fever virus proteome to search for peptides containing CD4+ and/or CD8+ T cell epitopes, (2) predictors of peptide-HLA binding to suggest epitopes and their restricting HLA allotypes, (3) generation of peptide-HLA tetramers to identify T cell epitopes, and (4) analysis of ex vivo T cell responses to validate the same. This approach is systematic, exhaustive, and can be done in any individual of any HLA haplotype. It is all-inclusive in the sense that it includes all protein antigens and peptide epitopes, and encompasses both CD4+ and CD8+ T cell epitopes. It is efficient and, importantly, reduces the false discovery rate. The unbiased nature of the T cell epitope discovery approach presented here should support the refinement of future peptide-HLA class I and II predictors and tetramer technologies, which eventually should cover all HLA class I and II isotypes. We believe that future investigations of emerging pathogens (e.g., SARS-CoV-2) should include population-wide T cell epitope discovery using blood samples from patients, convalescents and/or long-term survivors, who might all hold important information on T cell epitopes and responses.

Energetics and IC50 based epitope screening in SARS CoV-2 (COVID 19) spike protein by immunoinformatic analysis implicating for a suitable vaccine development

BACKGROUND

The recent outbreak by SARS-CoV-2 has generated a chaos in global health and economy and claimed/infected a large number of lives. Closely resembling with SARS CoV, the present strain has manifested exceptionally higher degree of spreadability, virulence and stability possibly due to some unidentified mutations. The viral spike glycoprotein is very likely to interact with host Angiotensin-Converting Enzyme 2 (ACE2) and transmits its genetic materials and hijacks host machinery with extreme fidelity for self propagation. Few attempts have been made to develop a suitable vaccine or ACE2 blocker or virus-receptor inhibitor within this short period of time.

METHODS

Here, attempt was taken to develop some therapeutic and vaccination strategies with a comparison of spike glycoproteins among SARS-CoV, MERS-CoV and the SARS-CoV-2. We verified their structure quality (SWISS-MODEL, Phyre2, and Pymol) topology (ProFunc), motifs (MEME Suite, GLAM2Scan), gene ontology based conserved domain (InterPro database) and screened several epitopes (SVMTrip) of SARS CoV-2 based on their energetics, IC50 and antigenicity with regard to their possible glycosylation and MHC/paratope binding (Vaxigen v2.0, HawkDock, ZDOCK Server) effects.

RESULTS

We screened here few pairs of spike protein epitopic regions and selected their energetic, Inhibitory Concentration50 (IC50), MHC II reactivity and found some of those to be very good target for vaccination. A possible role of glycosylation on epitopic region showed profound effects on epitopic recognition.

CONCLUSION

The present work might be helpful for the urgent development of a suitable vaccination regimen against SARS CoV-2.