Epitope Prediction Research Articles

Whole Proteome-Based Therapeutic Targets Annotation and Designing of Multi-Epitope-Based Vaccines against the Gram-Negative XDR-Alcaligenes faecalis Bacterium.

This study involved therapeutic targets mining for the extremely drug-resistant bacterial species called Alcaligenes faecalis, which is known to infect humans. The infections caused by this species in different parts of the human body have been linked with a higher degree of resistance to several classes of antibiotics. Meanwhile, alternate therapeutic options are needed to treat these bacterial infections in clinical settings. In the current study, a subtractive proteomics approach was adapted to annotate the whole proteome of Alcaligenes faecalis and prioritize target proteins for vaccine-related therapeutics design. This was followed by targeted protein-specific immune epitope prediction and prioritization. The shortlisted epitopes were further subjected to structural design and in silico validation of putative vaccines against Alcaligenes faecalis. The final vaccine designs were also evaluated for potential interaction analysis with human TLR-2 through molecular docking. Finally, the putative vaccines were subjected to in silico cloning and immune simulation approaches to ensure the feasibility of the target-specific vaccine constructs in further experimental designs.

Computational analysis of proteome of Foot-and-mouth disease Virus for the prediction of immunogenic epitopes

ObjectiveFoot and Mouth Disease virus (FMDV) commonly affects cloven hoofed animals. Infection with one serotype does not confer immunity to others and currently available vaccines do not provide effective T-cell immunity. The objective of this work is to predict the Cytotoxic T lymphocyte (CTL) epitopes which could be used as vaccine components against FMD virus. Material and methodsProteomes of three Indian FMDV vaccine strains, Type A IND40/00, Type A IND17/82 and Asia-1 IND63/72 were retrieved. BoLa-T2b allele restricted CTL epitopes were predicted using Immune Epitope Data Base Analysis Resource (IEDB-AR) and NetMHCpan 2.8 tool. The predicted overlapping epitopes were further analysed for antigenicity prediction, binding to other BoLA allele and consensus epitope analysis. ResultsA total of 207 epitopes were predicted by IEDB-AR tool. Among this, 72, 68, 67, epitopes were predicted in Asia 1 IND 63/72, Type A IND40/00 and Type A IND17/82 respectively. Totally 489 epitopes were predicted by NetMHCpan 2.0 tool. Of which, 164, 163 and 162 epitopes were predicted in Type A IND17/82, Asia 1 IND63/72 and Type A IND40/00 respectively. In total 29 consensus epitopes were predicted during this analysis, among this, 28 epitopes are present to occur in all the three Indian strains that were tested. ConclusionsThe 29 consensus epitopes predicted in this study could be used for developing a potent vaccine for both Asia I and Type A strains of FMD. Further in vitro and in vivo studies are needed to confirm the CTL efficacy of this peptide against FMDV.

LBCEPred: a machine learning model to predict linear B-cell epitopes.

B-cell epitopes have the capability to recognize and attach to the surface of antigen receptors to stimulate the immune system against pathogens. Identification of B-cell epitopes from antigens has a great significance in several biomedical and biotechnological applications, provides support in the development of therapeutics, design and development of an epitope-based vaccine and antibody production. However, the identification of epitopes with experimental mapping approaches is a challenging job and usually requires extensive laboratory efforts. However, considerable efforts have been placed for the identification of epitopes using computational methods in the recent past but deprived of considerable achievements. In this study, we present LBCEPred, a python-based web-tool (http://lbcepred.pythonanywhere.com/), build with random forest classifier and statistical moment-based descriptors to predict the B-cell epitopes from the protein sequences. LBECPred outperforms all sequence-based available models that are currently in use for the B-cell epitopes prediction, with 0.868 accuracy value and 0.934 area under the curve. Moreover, the prediction performance of proposed models compared to other state-of-the-art models is 56.3% higher on average for Mathews Correlation Coefficient. LBCEPred is easy to use tool even for novice users and has also shown the models stability and reliability, thus we believe in its significant contribution to the research community and the area of bioinformatics.

Design of Vaccine Candidate Based on Ebola Virus Epitop With In Silico Approach

ABSTRACT The World Health Organization (WHO) recorded as many as 2,299 cases of death with a case percentage of 66% during 2018 to 2020 in the Democratic Republic of the Congo due to the Ebola virus. Ebola virus is a member of the Filoviridae family. One of the viro cores consists of glycoprotein (GP). Messenger RNA (mRNA) generates long GP chains for the attachment protein (GP 1) and the fusion protein (GP 2). Epitope-based vaccines are a promising approach because epitopes represent immunogenic regions that elicit immunity specifically. Epitope prediction was performed based on the GP EBOV sequence available in the Data Bank. The designed vaccine could be one of the candidates for the Ebola virus vaccine. The design of the virus with access to NCBI AAB81004 was carried out by testing such as B cell epitope, T cell, and their antigenicity using the VaxiJen v2.0 server and IEDB. The T cell epitope prediction results showed that 20 T cell epitopes interacted with the Major Histocompatibility Complex (MHC) with the highest score of 2.8069. B cell epitope by linear BepiPred assay had 77 candidate epitope peptides from sequence 401-477. Karplus and Schulz's flexibility predictions showed a predictive value of 1.119 with a threshold of 1.008, with the analyzed area having an antigenic tendency where the threshold area was yellow. Keywords: Ebolavirus; Vaccine; Epitope B Cell; Epitope T Cell; In Silico

HAMdetector: a Bayesian regression model that integrates information to detect HLA-associated mutations.

A key process in anti-viral adaptive immunity is that the human leukocyte antigen (HLA) system presents epitopes as major histocompatibility complex I (MHC I) protein-peptide complexes on cell surfaces and in this way alerts CD8+ cytotoxic T-lymphocytes (CTLs). This pathway exerts strong selection pressure on viruses, favoring viral mutants that escape recognition by the HLA/CTL system. Naturally, such immune escape mutations often emerge in highly variable viruses, e.g. HIV or HBV, as HLA-associated mutations (HAMs), specific to the hosts MHC I proteins. The reliable identification of HAMs is not only important for understanding viral genomes and their evolution, but it also impacts the development of broadly effective anti-viral treatments and vaccines against variable viruses. By their very nature, HAMs are amenable to detection by statistical methods in paired sequence/HLA data. However, HLA alleles are very polymorphic in the human host population which makes the available data relatively sparse and noisy. Under these circumstances, one way to optimize HAM detection is to integrate all relevant information in a coherent model. Bayesian inference offers a principled approach to achieve this. We present a new Bayesian regression model for the detection of HAMs that integrates a sparsity-inducing prior, epitope predictions and phylogenetic bias assessment, and that yields easily interpretable quantitative information on HAM candidates. The model predicts experimentally confirmed HAMs as having high posterior probabilities, and it performs well in comparison to state-of-the-art models for several datasets from individuals infected with HBV, HDV and HIV. The source code of this software is available at https://github.com/HAMdetector/Escape.jl under a permissive MIT license. The data underlying this article were provided by permission. Data will be shared on request to the corresponding author with permission of the respective co-authors. Supplementary data are available at Bioinformatics online.

In silico analyses and experimental validation of the MHC class-I restricted epitopes of Ebolavirus GP.

Ebolavirus (EBOV) causes an extremely high mortality and prevalence disease called Ebola virus disease (EVD). There is only one glycoprotein (GP) on the virus particle surface, which mediates entry into the host cell. Major histocompatibility complex (MHC) class-I restricted cluster of differentiation 8 (CD8+) T cell responses are important antiviral immune responses. Therefore, it is of great importance to understand EBOV GP-specific MHC class-I restricted epitopes within immunogenicity. In this study, computational approaches were employed to predict the dominant MHC class-I molecule epitopes of EBOV GP for mouse H2 and major alleles of human leukocyte antigen (HLA) class-I supertypes. Our results yielded 42 dominant epitopes in H2 haplotypes and 301 dominant epitopes in HLA class-I haplotypes. After validation by enzyme-linked immunospot (ELISpot) assay, in-depth analyses to ascertain their nature of conservation, immunogenicity, and docking with the corresponding MHC class-I molecules were undertaken. Our study predicted MHC class-I restricted epitopes that may aid the advancement of anti-EBOV immune responses. An integrated strategy of epitope prediction, validation and comparative analyses was postulated, which is promising for epitope-based immunotherapy development and application to viral epidemics.

Towards the prediction of non-peptidic epitopes.

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69–0.96 depending on the molecule cluster.

Computational Design of a Multi-Epitope Vaccine Against Porphyromonas gingivalis.

Porphyromonas gingivalis is a Gram-negative pathogenic bacterium associated with chronic periodontitis. The development of a chimeric peptide-based vaccine targeting this pathogen could be highly beneficial in preventing oral bone loss as well as other severe gum diseases. We applied a computational framework to design a multi-epitope-based vaccine candidate against P. gingivalis. The vaccine comprises epitopes from subunit proteins prioritized from the P. gingivalis reference strain (P. gingivalis ATCC 33277) using several reported vaccine properties. Protein-based subunit vaccines were prioritized through genomics techniques. Epitope prediction was performed using immunoinformatic servers and tools. Molecular modeling approaches were used to build a putative three-dimensional structure of the vaccine to understand its interactions with host immune cells through biophysical techniques such as molecular docking simulation studies and binding free energy methods. Genome subtraction identified 18 vaccine targets: six outer-membrane, nine cytoplasmic membrane-, one periplasmic, and two extracellular proteins. These proteins passed different vaccine checks required for the successful development of a vaccine candidate. The shortlisted proteins were subjected to immunoinformatic analysis to map B-cell derived T-cell epitopes, and antigenic, water-soluble, non-toxic, and good binders of DRB1*0101 were selected. The epitopes were then modeled into a multi-epitope peptide vaccine construct (linked epitopes plus adjuvant) to enhance immunogenicity and effectively engage both innate and adaptive immunity. Further, the molecular docking approach was used to determine the binding conformation of the vaccine to TLR2 innate immune receptor. Molecular dynamics simulations and binding free energy calculations of the vaccine–TLR2 complex were performed to highlight key intermolecular binding energies. Findings of this study will be useful for vaccine developers to design an effective vaccine for chronic periodontitis pathogens, specifically P. gingivalis.

Insights into the mutation T1117I in the spike and the lineage B.1.1.389 of SARS-CoV-2 circulating in Costa Rica

Emerging mutations and genotypes of the SARS-CoV-2 virus, responsible for the COVID-19 pandemic, have been reported globally. In Costa Rica during the year 2020, a predominant genotype carrying the mutation T1117I in the spike (S:T1117I) was previously identified. To investigate the possible effects of this mutation on the function of the spike, i.e. the biology of the virus, different bioinformatic pipelines based on phylogeny, natural selection, and co-evolutionary models, molecular docking, and epitopes prediction were implemented.Results of the phylogeny of sequences carrying the S:T1117I worldwide showed a polyphyletic group, with the emergence of local lineages. In Costa Rica, the mutation is found in the lineage B.1.1.389 and it is suggested to be a product of positive/adaptive selection. Different changes in the function of the spike protein and more stable interaction with a ligand (nelfinavir drug) were found. Only one epitope out 742 in the spike was affected by the mutation, with some different properties, but suggesting scarce changes in the immune response and no influence on the vaccine effectiveness.Jointly, these results suggest a partial benefit of the mutation for the spread of the virus with this genotype during the year 2020 in Costa Rica, although possibly not strong enough with the introduction of new lineages during early 2021 which became predominant later. In addition, the bioinformatic analyses used here can be applied as an in silico strategy to eventually study other mutations of interest for the SARS-CoV-2 virus and other pathogens.

Identification of arginine kinase as an allergen of brown crab, Callinectes bellicosus, and in silico analysis of IgE-binding epitopes

In recent years there has been an increase in the prevalence of allergic reactions to contact with/or consumption of crustaceans by immune responses mediated by IgE antibodies. Arginine kinase (AK) is considered one of the main allergens present in marine invertebrates. Currently, the allergenic potential of the brown crab (Callinectes bellicosus), which is a crustacean of great economic importance, has not been studied. Therefore, the aim of this work was to identify C. bellicosus AK as an allergen and to predict IgE-binding epitopes through immunobioinformatic analysis. AK was purified by precipitation with ammonium sulfate and ion- exchange chromatography. AK allergenicity was evaluated by IgE reactivity against sera from crustacean-allergic and non-allergic patients in both native and denaturing conditions. Additionally, a homology model was built based on the deduced amino acid sequence. A single band (~40 kDa) was found in SDS-PAGE, which was identified as an AK by mass spectrometry. AK showed immunoreactivity against crab-allergenic sera in both native and denaturing conditions with 70% and 80% positive reactions, respectively. Additionally, a 1073 bp ORF was obtained which codes for a deduced sequence of 357 amino acids corresponding to AK with > 90% identity with other AKs. Structural homology model of AK showed two main domains with conserved / folding of phospho-guanidine kinases. BediPred and Discotope were used for epitope prediction analysis, which suggests eight possible linear epitopes and seven conformational epitopes, respectively; and shows to be similar to other crustaceans AKs. C. bellicosus AK was identified as an allergenic protein by IgE reactivity and immunobioinformatic analysis indicates that both linear and conformational epitopes could be located in the surface of C. bellicosus AK structure.

Computational discovery of binding mode of anti-TRBC1 antibody and predicted key amino acids of TRBC1

Peripheral T-cell lymphoma (PTCL) is a type of non-Hodgkin lymphoma that progresses aggressively with poor survival rate. CAR T cell targeting T-cell receptor β-chain constant domains 1 (TRBC1) of malignant T cells has been developed recently by using JOVI.1 monoclonal antibody as a template. However, the mode of JOVI.1 binding is still unknown. This study aimed to investigate the molecular interaction between JOVI.1 antibody and TRBC1 by using computational methods and molecular docking. Therefore, the TRBC protein crystal structures (TRBC1 and TRBC2) as well as the sequences of JOVI.1 CDR were chosen as the starting materials. TRBC1 and TRBC2 epitopes were predicted, and molecular dynamic (MD) simulation was used to visualize the protein dynamic behavior. The structure of JOVI.1 antibody was also generated before the binding mode was predicted using molecular docking with an antibody mode. Epitope prediction suggested that the N3K4 region of TRBC1 may be a key to distinguish TRBC1 from TCBC2. MD simulation showed the major different surface conformation in this area between two TRBCs. The JOVI.1-TRBC1 structures with three binding modes demonstrated JOVI.1 interacted TRBC1 at N3K4 residues, with the predicted dissociation constant (Kd) ranging from 1.5 × 108 to 1.1 × 1010 M. The analysis demonstrated JOVI.1 needed D1 residues of TRBC1 for the interaction formation to N3K4 in all binding modes. In conclusion, we proposed the three binding modes of the JOVI.1 antibody to TRBC1 with the new key residue (D1) necessary for N3K4 interaction. This data was useful for JOVI.1 redesign to improve the PTCL-targeting CAR T cell.

Combined assessment of MHC binding and antigen abundance improves T cell epitope predictions

SummaryMany steps of the MHC class I antigen processing pathway can be predicted using computational methods. Here we show that epitope predictions can be further improved by considering abundance levels of peptides' source proteins. We utilized biophysical principles and existing MHC binding prediction tools in concert with abundance estimates of source proteins to derive a function that estimates the likelihood of a peptide to be an MHC class I ligand. We found that this combination improved predictions for both naturally eluted ligands and cancer neoantigen epitopes. We compared the use of different measures of antigen abundance, including mRNA expression by RNA-Seq, gene translation by Ribo-Seq, and protein abundance by proteomics on a dataset of SARS-CoV-2 epitopes. Epitope predictions were improved above binding predictions alone in all cases and gave the highest performance when using proteomic data. Our results highlight the value of incorporating antigen abundance levels to improve epitope predictions.

Identification and characterization of recurrent neoantigens in upper gastrointestinal (GI) cancers.

246 Background: Neoantigens are short peptides derived from tumor-specific somatic mutations that can bind to HLA molecules and be presented on the cell surface to activate the immune system. Recognition of neoantigens by autologous T cells promotes sensitivity to immune checkpoint blockade of mismatch repair deficient (MMRd)/microsatellite instability high (MSI-H) tumors. Neoantigen-targeted reactivity by autologous T cells has also been reported in microsatellite stable (MSS) tumors. We aimed to comprehensively assess the spectrum of neoantigens in upper GI cancers and identify recurrent immunogenic candidate neoantigens. Methods: 600 tumor specimens including 268 esophageal (EC), 211 gastric (GC), and 121 gastroesophageal junction (GEJ) cancers tested at Caris Life Sciences (Phoenix, AZ) with NextGen Sequencing (NGS) on DNA (whole exome) and RNA (whole transcriptome) were analyzed. MSI status was determined by immunohistochemistry of MMR protein and/or NGS. Immune epitope prediction was performed on translated peptide sequences harboring detected mutations using the NetMHCpan v4.0 method in the Immune Epitope Database, with HLA genotyping performed using arcasHLA. Immune/stromal cell abundance was quantified using the Microenvironment Cell Populations Counter method. Gene expression profiles were analyzed for a transcriptional signature predictive of response to immunotherapy (T cell-inflamed signature, TIS). Results: Overall, the median patient age was 66 years (range 22-90), 72.8% were male, and 33.7% of samples were metastatic site biopsies. MMRd/MSI-H rate was 2.2% in EC, 12.4% in GC, and 2.5% in GEJ. A total of 1172 unique recurrent neoantigens with predicted binding-level affinity for patient specific HLA alleles were identified (317 in EC, 786 in GC, 157 in GEJ), with 442 and 552 neoantigens exclusively associated with MSS and MSI-H tumors, respectively. Across each cancer type, a higher positive TIS score correlated with both immune and stromal cell population abundance in the tumor microenvironment, most notably cytotoxic lymphocyte and monocytic cell types (r > 0.80, P < 0.0001). Recurrent peptides associated with highest average TIS scores resulted from mutation of TP53 (R248W, 2 peptides: 2.8%/3.2% of samples, respectively) and CUX1 (L162F, 1.6%) in MSS EC; REL (D318A, 2.3%) and ERBB2 (V842I, 1.2%) in MSS GC; MSH3 (K383fs, 36%) and ASXL1 (G645fs, 24%) in MMRd/MSI-H GC; and HOXD12 (A70-A71dup , 2.8%) and TP53 (R248Q, 3.7%) in MSS GEJ. Conclusions: This is one of the largest studies to investigate the landscape of recurrent neoantigens in upper GI cancers. We were able to identify candidate recurrent peptides with high HLA binding affinity and an association with a positive TIS signature in both MSI and MSS tumors, supporting the role of recurrent neoantigens as potential cancer immunotherapy targets.

Development of a Novel Multi-Epitope Vaccine Candidate against Streptococcus Iniae Infection in Fish: An Immunoinformatics Study.

Streptococcus Iniae infection is recognized as a disease with substantial economic losses, infecting a wide range of fish species. The limitations of current vaccines and strategies have led to the identification of new methods to control this disease. Multi-epitope vaccines which employ various immunogenic proteins can be promising. The current research project aimed to design an efficient multi-epitope vaccine against Streptococcus Iniae infection in fish. To this end, six immunogenic proteins of Streptococcus Iniae, including FBA, ENO, Sip11, GAPDH, MtsB, and SCPI proteins, were applied for epitope prediction. The best B cell, T cell, and IFNγ epitopes of the immunogenic proteins, as well as interleukin-8, were used to construct a multi-epitope vaccine. Thereafter, different parameters of the designed vaccine, including physicochemical features, antigenicity, secondary structure, and tertiary structure, were evaluated. Moreover, the interaction of the interleukin-8 domain of the designed vaccine and its receptor was investigated by molecular docking strategy. Finally, nucleotide sequence of the vaccine was adapted to express in Escherichia coli. The results of the present study pointed out that the designed vaccine was a stable vaccine with molecular weight and antigenicity score of 45 kDa and 0.936, respectively. Furthermore, the structure analysis results revealed that the designed vaccine contained 23.49% alpha helix, with 90.5% residues in favored region. Finally, it was demonstrated that the interleukin-8 domain of the designed vaccine could be successfully docked to its receptor with the lowest energy of -1020.9. Based on the obtained results, it seems that the designed vaccine can be an efficient candidate to prevent Streptococcus Iniae infection in fish.

High-specificity targets in SARS-CoV-2 N protein for serological detection and distinction from SARS-CoV

Numerous serological detection kits are being rapidly developed and approved for screening and diagnosing suspected coronavirus disease 2019 (COVID-19) cases. However, cross-reactivity between pre-existing antibodies against other coronaviruses and the captured antigens in these kits can affect detection accuracy, emphasizing the necessity for identifying highly specific antigen fragments for antibody detection. Thus, we performed a conservation and specificity analysis of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein. We also integrated various B-cell epitope prediction methods to obtain possible dominant epitope regions for the N protein, analyzed the differences in serological antibody levels for different epitopes using ELISA, and identified N protein epitopes for IgG and IgM with high-specificity. The SARS-CoV-2 N protein showed low mutation rates and shared the highest amino acid similarity with SARS-CoV; however, it differed substantially from other coronaviruses. Tests targeting the SARS-CoV-2 N protein produce strong positive results in patients recovering from SARS-CoV. The N18–39 and N183-197 epitopes for IgG and IgM detection, respectively, can effectively overcome cross-reactivity, and even exhibit good specificity between SARS-CoV-2 and SARS-CoV. The antibody levels detected with these were consistent with those detected using the complete N protein. These findings provide a basis for serological diagnosis and determining the kinetics of SARS-CoV-2 antibody detection in patients.

Designing a Recombinant Vaccine against Providencia rettgeri Using Immunoinformatics Approach.

Antibiotic resistance (AR) is the resistance mechanism pattern in bacteria that evolves over some time, thus protecting the bacteria against antibiotics. AR is due to bacterial evolution to make itself fit to changing environmental conditions in a quest for survival of the fittest. AR has emerged due to the misuse and overuse of antimicrobial drugs, and few antibiotics are now left to deal with these superbug infections. To combat AR, vaccination is an effective method, used either therapeutically or prophylactically. In the current study, an in silico approach was applied for the design of multi-epitope-based vaccines against Providencia rettgeri, a major cause of traveler’s diarrhea. A total of six proteins: fimbrial protein, flagellar hook protein (FlgE), flagellar basal body L-ring protein (FlgH), flagellar hook-basal body complex protein (FliE), flagellar basal body P-ring formation protein (FlgA), and Gram-negative pili assembly chaperone domain proteins, were considered as vaccine targets and were utilized for B- and T-cell epitope prediction. The predicted epitopes were assessed for allergenicity, antigenicity, virulence, toxicity, and solubility. Moreover, filtered epitopes were utilized in multi-epitope vaccine construction. The predicted epitopes were joined with each other through specific GPGPG linkers and were joined with cholera toxin B subunit adjuvant via another EAAAK linker in order to enhance the efficacy of the designed vaccine. Docking studies of the designed vaccine construct were performed with MHC-I (PDB ID: 1I1Y), MHC-II (1KG0), and TLR-4 (4G8A). Findings of the docking study were validated through molecular dynamic simulations, which confirmed that the designed vaccine showed strong interactions with the immune receptors, and that the epitopes were exposed to the host immune system for proper recognition and processing. Additionally, binding free energies were estimated, which highlighted both electrostatic energy and van der Waals forces to make the complexes stable. Briefly, findings of the current study are promising and may help experimental vaccinologists to formulate a novel multi-epitope vaccine against P. rettgeri.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review.

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety—they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

P696 Genetic predisposition to infliximab immunogenicity in patients with immune-mediated inflammatory diseases – secondary analyses from a randomised clinical trial

Abstract Background Immunogenicity is a leading cause of treatment failure to TNF inhibitors. Genetic variations in HLA class II genes have been suggested to predispose to anti-drug antibody formation, but studies using high-resolution HLA typing as well as characterisation of biologically relevant haplotypes are currently lacking. The aim of this study was to assess associations between HLA loci and immunogenicity to infliximab. Methods Patients with immune-mediated inflammatory diseases (IMID) treated with infliximab (N=612) (120 rheumatoid arthritis, 181 spondyloarthritis, 72 psoriatic arthritis, 114 ulcerative colitis, 80 Crohn disease, and 45 psoriasis) were included in the randomised, Norwegian Drug Monitoring (NOR-DRUM) trial. Neutralising anti-drug antibodies were detected with an automated fluorescence assay. Next generation HLA sequencing at G group resolution were performed and trimmed to 2nd field. Haplotype analyses including the most significant HLA loci were performed, and epitope predictions for the infliximab light- and heavy-chain were assessed. Results Anti-drug antibodies to infliximab were detected in 147 (24 %) of patients. The most significant associations with immunogenicity were with the genes HLA-DQB1 (P=1.4x10-6), -DRB1 (P= 2.7x10-5), -DQA1 (P=0.00017) and -B (P=0.0009). Conditional analyses indicated that HLA-DQB1 represents the primary association(Table 1). Haplotype analyses showed that there was significantly increased risk of immnogenicity in patients carrying the HLA-DQ2 haplotypes DQB1*02:01~DQA1*05:01 (P=1.1x10-6) and DQB1*02:02~DQA1*02:01 (P=0.008), while one protective haplotype DQB1*05:01~DQA1*01:01 (P=0.0005) was identified (Figure 1a-c). Presence of HLA-DQ2 more than doubled the risk of anti-drug antibody formation (HR 2.54, 95%CI 1.82–3.56) (Figure 1d), in a model including the covariates age, sex, diagnosis, and concomitant immunosuppressive therapy. Association tests of each level of HLA analyses showed that HLA-DQ2 represents the strongest association(Figure 2). Epitope predictions revealed that the HLA-DQ2 haplotypes DQB1*02:01-DQA1*05:01 and DQB1*02:02-DQA1*02:01 share nine strong binder peptides, originating from the infliximab light chain, encompassing the amino acid core-peptide sequences INTVESEDI and VYACEVTHQ. Conclusion Using high-resolution HLA sequencing, we here demonstrate a genetic association between HLA-DQ2 and infliximab immunogenicity in IMID patients. The presence of either HLA-DQ2 risk haplotypes more than doubled the risk of anti-drug antibody formation across all diagnoses. Testing for HLA-DQ2 could be of clinical value in identifying patients at risk for immunogenicity and facilitate treatment decisions. Whether these findings apply to other TNF inhibitors should be further explored.

Recombinantly Expressed Chimeric Fibers Demonstrate Discrete Type-Specific Neutralizing Epitopes in the Fowl Aviadenovirus E (FAdV-E) Fiber, Promoting the Optimization of FAdV Fiber Subunit Vaccines towards Cross-Protection in vivo.

ABSTRACTVaccines against inclusion body hepatitis in chickens are complicated by the involvement of antigenically diverse fowl adenovirus types. Though immunization with fiber protein confers robust protection, type specificity of fiber antibodies is an obstacle for the desired broad coverage. In this study, we utilized information on multiple linear epitopes predicted in the Fowl Aviadenovirus E (FAdV-E) fiber head (knob) to develop chimeric fibers with an exchange between two serotypes’ sequences, each containing proposed epitopes. Two consecutive segments pertaining to amino acid positions 1 to 441 and 442 to 525/523 in the fibers of FAdV-8a and -8b, types of Fowl Aviadenovirus E that cause inclusion body hepatitis, were swapped reciprocally to result in novel chimeras, crecFib-8a/8b and crecFib-8b/8a. crecFib was indistinguishable from monospecific recombinant fibers in its eactivity with different FAdV antisera in Western blotting. However, contrary to the results for monospecific fibers, crecFib induced cross-neutralizing antibodies against both serotypes in chickens. This demonstrates three nonidentical epitopes in the FAdV-E fiber, the conserved epitope detected in Western blotting and at least two epitopes participating in neutralization, being type specific and located opposite residue position 441-442. Furthermore, we supply conformational evidence for a site in the fiber knob with accessibility critical for neutralization. With such an extended neutralization spectrum compared to those of individual fibers, crecFib was anticipated to fulfill and even extend the mechanistic basis of fiber-mediated protection toward bivalent coverage. Accordingly, crecFib, administered as a single-antigen component, protected chickens simultaneously against challenge with FAdV-8a or -8b, demonstrated by up-to-complete resistance to clinical disease, prevention of target organ-related changes, and significant reduction of viral load.IMPORTANCE The control of inclusion body hepatitis, a disease of economic importance for chicken production worldwide, is complicated by an etiology involving multiple divergent fowl adenovirus types. The fiber protein is principally efficacious in inducing neutralizing and protective antibodies in vaccinated chickens; however, it faces limitations due to its intrinsic type specificity for neutralization. In this study, based on an in silico-guided prediction of multiple epitopes in the fowl adenovirus fiber head’s loops, we designed chimeric proteins, swapping N- and C-distal fiber portions, each containing putative epitopes, between divergent types FAdV-8a and -8b. In in vitro and in vivo studies, the chimeric fiber displayed extended properties compared to those of individual monotype-specific fibers, allowing the number, distribution, functionality, and conformational bearings of epitopes of the fowl adenovirus fiber to be characterized in more detail. Importantly, the chimeric fiber induced cross-neutralizing antibodies and protective responses in chickens against infections by both serotypes, promoting the advancement of broadly protective subunit vaccination strategies against FAdV.

Identification, Mapping, and Genetic Diversity of Novel Conserved Cross-Species Epitopes of RhopH2 in Plasmodium knowlesi With Plasmodium vivax.

Malaria is a major public health concern, and any tangible intervention during the pre-elimination phase can result in a significant reduction in infection rates. Recent studies have reported that antigens producing cross-protective immunity can play an important role as vaccines and halt malaria transmission in different endemic regions. In this study, we studied the genetic diversity, natural selection, and discovered novel conserved epitopes of a high molecular weight rhoptry protein 2 (RhopH2) in clinical samples of Plasmodium knowlesi and Plasmodium vivax cross-protective domains, which has been proven to produce cross-protective immunity in both species. We found low levels of nucleotide diversity (P. knowlesi; π ~ 0.0093, SNPs = 49 and P. vivax π ~ 0.0014, SNPs = 23) in P. knowlesi (n = 40) and P. vivax (n = 65) samples in the PkRhopH2 cross-protective domain. Strong purifying selection was observed for both species (P. knowlesi; dS - dN = 2.41, p < 0.009, P. vivax; dS - dN = 1.58, p < 0.050). In silico epitope prediction in P. knowlesi identified 10 potential epitopes, of which 7 epitopes were 100% conserved within clinical samples. Of these epitopes, an epitope with 10 amino acids (QNSKHFKKEK) was found to be fully conserved within all P. knowlesi and P. vivax clinical samples and 80%–90% conservation within simian malaria ortholog species, i.e., P. coatneyi and P. cynomolgi. Phylogenetic analysis of the PkRhopH2 cross-protective domain showed geographical clustering, and three subpopulations of P. knowlesi were identified of which two subpopulations originated from Sarawak, Malaysian Borneo, and one comprised only the laboratory lines from Peninsular Malaysia. This study suggests that RhopH2 could be an excellent target for cross-protective vaccine development with potential for outwitting strain as well as species-specific immunity. However, more detailed studies on genetic diversity using more clinical samples from both species as well as the functional role of antibodies specific to the novel conserved epitope identified in this study can be explored for protection against infection.