Immunogenicity Score Research Articles

Immunogenic cell death signatures from on-treatment tumor specimens predict immune checkpoint therapy response in metastatic melanoma

Melanoma is a highly malignant form of skin cancer that typically originates from abnormal melanocytes. Despite significant advances in treating metastatic melanoma with immune checkpoint blockade (ICB) therapy, a substantial number of patients do not respond to this treatment and face risks of recurrence and metastasis. This study collected data from multiple datasets, including cohorts from Riaz et al., Gide et al., MGH, and Abril-Rodriguez et al., focusing on on-treatment samples during ICB therapy. We used the single-sample gene set enrichment analysis (ssGSEA) method to calculate immunogenic cell death scores (ICDS) and employed an elastic network algorithm to construct a model predicting ICB efficacy. By analyzing 18 ICD gene signatures, we identified 9 key ICD gene signatures that effectively predict ICB treatment response for on-treatment metastatic melanoma specimens. Results showed that patients with high ICD scores had significantly higher response rates to ICB therapy compared to those with low ICD scores. ROC analysis demonstrated that the AUC values for both the training and validation sets were around 0.8, indicating good predictive performance. Additionally, survival analysis revealed that patients with high ICD scores had longer progression-free survival (PFS). This study used an elastic network algorithm to identify 9 ICD gene signatures related to the immune response in metastatic melanoma. These gene features can not only predict the efficacy of ICB therapy but also provide references for clinical decision-making. The results indicate that ICD plays an important role in metastatic melanoma immunotherapy and that expressing ICD signatures can more accurately predict ICB treatment response and prognosis for on-treatment metastatic melanoma specimens, thus providing a basis for personalized treatment.

Neoantigen immunogenicity landscapes and evolution of tumor ecosystems during immunotherapy with nivolumab.

Neoantigen immunoediting drives immune checkpoint blockade efficacy, yet the molecular features of neoantigens and how neoantigen immunogenicity shapes treatment response remain poorly understood. To address these questions, 80 patients with non-small cell lung cancer were enrolled in the biomarker cohort of CheckMate 153 (CA209-153), which collected radiographic guided biopsy samples before treatment and during treatment with nivolumab. Early loss of mutations and neoantigens during therapy are both associated with clinical benefit. We examined 1,453 candidate neoantigens, including many of which that had reduced cancer cell fraction after treatment with nivolumab, and identified 196 neopeptides that were recognized by T cells. Mapping these neoantigens to clonal dynamics, evolutionary trajectories and clinical response revealed a strong selection against immunogenic neoantigen-harboring clones. We identified position-specific amino acid and physiochemical features related to immunogenicity and developed an immunogenicity score. Nivolumab-induced microenvironmental evolution in non-small cell lung cancer shared some similarities with melanoma, yet critical differences were apparent. This study provides unprecedented molecular portraits of neoantigen landscapes underlying nivolumab's mechanism of action.

Reverse vaccinology approach for identification of epitopes from E1 protein as peptide vaccine against HCV: A proof of concept

The development of effective vaccines against Hepatitis C Virus (HCV) remains a global health priority and challenge. In this study, we employed an integrative approach combining computational epitope prediction with experimental validation to identify immunogenic peptides targeting the E1 glycoprotein of HCV. In the present report, computational data from various epitope prediction algorithms such as IEDB and SYFPEITHI, followed by molecular dynamics (MD) simulations and immuno-informatics analysis is presented. Through computational screening, we identified potential epitope candidates, with QVRNSSGLY (P3) and QLFTFSPRH (P7) emerging as promising candidates. MD simulations revealed stable interactions between these epitopes and MHC molecule, further validated by free energy estimations using MMPBSA method. Immuno-informatics analysis supported these findings, showing high binding potential and immunogenicity scores for the selected peptides. Subsequent synthesis and characterization of epitope peptides confirmed their structural integrity and purity required for conducting immune activation assays. Experimental immunological assays carried out in this study involved epitope peptide induced activation of CD8 + and CD4 + T cells from healthy human subjects and HCV- recovered patients. Data from experimental validation revealed significant cytokine release upon exposure to epitope peptides, particularly TNF-a, IL-6, and GM-CSF, indicative of robust immune responses. Notably, peptides P3 and P7 exhibited the most pronounced cytokine induction profiles, underscoring their potential as vaccine candidates. Further investigations addressing the mechanism of action of these epitope peptides under preclinical and clinical settings may help in developing effective vaccine against HCV.

Exploring the S Protein of SARS-CoV-2 to Design a Novel Multi-Epitope Vaccine against COVID-19 Based on Immunoinformatics Approaches.

Developing a novel COVID-19 multi-epitope vaccine (CoVMEV) is essential to containing the SARS-CoV-2 pandemic. The virus's immunodominant B and T cell epitopes from the S protein were found and joined to create the CoVMEV. Bioinformatics techniques were used to investigate the secondary and tertiary structures, as well as the physical and chemical properties of CoVMEV. CoVMEV exhibited high antigenicity and immunogenicity scores, together with good water solubility and stability. Toll-like receptor 2 (TLR2) and toll-like receptor4 (TLR4), which are critical in triggering immunological responses, were also strongly favoured by CoVMEV. Molecular dynamics simulation and immune stimulation studies revealed that CoVMEV effectively activated T and B lymphocytes, and increased the number of active CD8+ T cells than similar vaccines. CoVMEV holds promise as a potential vaccine candidate for COVID-19, given its robust immunogenicity, stability, antigenicity, and capacity to stimulate a strong immune response. This study presents a significant design concept for the development of peptidyl vaccines targeting SARS-CoV-2. Further investigation and clinical trials will be crucial in assessing the efficacy and safety of CoVMEV as a potential vaccine for COVID-19.

Multi-epitope vaccine against SARS-CoV-2 targeting the spike RBD: an immunoinformatics approach.

Aim: We designed a SARS-CoV-2 epitope vaccine based on the receptor-binding domain (RBD) in virus spike protein. Methods: RT-PCR performed on nasopharyngeal swab COVID-19 patients. After registering RBD region in the GenBank, physicochemical parameters, secondary structure, homology modeling, 3D structure of RBD region and antigenicity were determined using ProtParam ExPASy, PSIPRED, MolProbity, IEDB and Vaxijen online tools, respectively. Results: B and T cell epitopes were predicted in terms of non-allergenicity and antigenicity. MolProbity analysis provided a qualitative model for RBD. The homology model showed that most of the residues are in optimal district of energy. Conclusion: High immunogenicity score of epitopes indicates promising candidates for the development of multi-epitope vaccines. It may help to develop an effective vaccine.

Association between brain cancer immunogenetic profile and in silico immunogenicities of 11 viruses

Several viruses including human herpes viruses (HHVs), human polyomavirus JCV, and human papilloma virus (HPV) have been implicated in brain cancer, albeit inconsistently. Since human leukocyte antigen (HLA) is centrally involved in the human immune response to viruses and has been implicated in brain cancer, we evaluated in silico the immunogenicity between 69 Class I HLA alleles with epitopes of proteins of 9 HHVs, JCV, and HPV with respect to a population-based HLA-brain cancer profile. We found that immunogenicity varied widely across HLA alleles with HLA-C alleles exhibiting the highest immunogenicity, and that immunogenicity scores were negatively associated with the population-based HLA-brain cancer profile, particularly for JCV, HHV6A, HHV5, HHV3, HHV8, and HHV7. Consistent with the role of HLA in foreign antigen elimination, the findings suggest that viruses with proteins of high HLA immunogenicity are eliminated more effectively and, consequently, less likely to cause brain cancer; conversely, the absence of highly immunogenic HLA may allow the viral antigens to persist, contributing to cancer.

Design and development of a multi‐epitope vaccine for the prevention of latent tuberculosis infection

AbstractBackgroundLatent tuberculosis infection (LTBI) often progresses to active tuberculosis, necessitating the development of novel vaccine to prevent LTBI. In this study, we aimed to design a Mycobacterium tuberculosis (M. tuberculosis) vaccine that could elicit a potent immune response to prevent LTBI.MethodsWe used bioinformatics and immunoinformatics techniques to develop a multi‐epitope vaccine (MEV) called C624P. The vaccine contained six cytotoxic T lymphocytes (CTL), two helper T lymphocytes (HTL), and four B‐cell epitopes derived from six antigens associated with LTBI and the Mycobacterium tuberculosis region of difference. We added Toll‐like receptor (TLR) agonists and PADRE peptide to the MEV to enhance its immunogenicity. We then analyzed the C624P vaccine's physical and chemical properties, spatial structure, molecular docking with TLRs, and immunological features.ResultsThe C624P vaccine displayed good antigenicity and immunogenicity scores of 0.901398 and 3.65609, respectively. The vaccine structure was stable, with 42.82% α‐helix content, a Z‐value of −7.84, and a favored Ramachandran plot area of 85.84% after majorization. Molecular docking analysis showed that the C624P vaccine could bind tightly to TLR2 (−1011.0 kcal/mol) and TLR4 (−941.4 kcal/mol). Furthermore, the C624P vaccine effectively stimulated T and B lymphocytes, resulting in high levels of Th1 cytokines such as IFN‐γ and IL‐2.ConclusionsThe C624P vaccine represents a promising MEV for preventing LTBI. The vaccine's good antigenicity, immunogenicity, stability, and ability to activate immune responses suggest its effectiveness in preventing LTBI. Our study demonstrated the utility of bioinformatics and immunoinformatics techniques in designing safe and effective tuberculosis vaccines.

A novel approach to design a multiepitope peptide as a vaccine candidate for Bordetella pertussis

Bordetella pertussis is a very contagious pathogen in humans, causing pertussis disease. Pertussis is one of the 10 leading causes of death due to infectious diseases, especially among infants and children. Antibiotic-resistant strains have recently emerged in this bacterium, and despite the high vaccination coverage, the prevalence of this disease has been increasing recently in both developed and developing countries. The objective of this study is to introduce a novel in silico vaccine candidate aimed at countering B. pertussis effectively. Differing from other comparable studies, this research employed a computational screening methodology to assess the genome of ‘Bordetella pertussis 18323.’ The purpose was to identify an innovative antigen for the development of a vaccine against B. pertussis. Notably, our investigation introduces an innovative antigen distinguished by its elevated immunogenicity score. Importantly, this antigen lacks toxicity and allergenicity, making it recognizable to the immune system and thus capable of inducing a robust immune response. In the subsequent phase, our antigen was utilized to identify potential epitopes conducive to the construction of a B. pertussis vaccine. These epitopes, alongside linkers, his-tag and adjuvants, were amalgamated to form the vaccine candidate. Subsequently, a comprehensive evaluation of the vaccine was conducted, encompassing various computational tests such as secondary and tertiary structure analysis, physicochemical examination, and structural analysis involving docking and molecular dynamics simulations. Importantly, our vaccine successfully passed all in silico tests. Communicated by Ramaswamy H. Sarma

Immunoinformatics-guided selection of immunogenic peptides from betanodavirus induce antigen-specific antibody production in hybrid grouper, Epinephelus fuscoguttatus x Epinephelus lanceolatus

Betanodavirus is a significant pathogen that causes viral nervous necrosis (VNN) in a wide range of fish species. It could cause up to 100% mortality in the infected farmed fish, especially at the larval stage. Red-Spotted Grouper Nervous Necrosis Virus (RGNNV), one of the betanodavirus genotypes, has been proven to have a wide range of host-compatibility across warm-water fish species. To date, there is no viable strategy to combat this virus infection, but vaccination approach is known to be effective against infectious diseases. Thus, current study aimed to identify immunogenic peptides as potential subunit vaccine candidates against grouper betanodavirus. Nucleotide sequences of the grouper nervous necrosis virus were retrieved from the National Center for Biotechnology Information database. B- and T-cell epitopes were predicted using the Immune Epitope Database analysis resource and the NetCTL 1.2 server, respectively. The predicted B- and T-cell epitopes were analysed for its respective antigenicity, immunogenicity, physico-chemical properties, and surface accessibility. Selected epitopes were then synthesized for immunization assay to examine antigen-specific antibody production in the immunized grouper. Five epitopes (one B-cell and four T-cell epitopes) were selected based on the immunogenicity score. The B-cell epitope has the highest immunogenicity score of 0.9333. Physico-chemical properties analysis of the B-cell epitope shown highest GRAVY score of 0.516 and was predicted to possess the longest estimated half-life of 30 h. Structural analysis has shown that the B-cell epitope possesses the largest accessible surface on the virus particle. Immunization of grouper with the selected B-cell epitope induced the highest antigen-specific antibody production as compared to grouper that was immunized with the selected T-cell epitope. This study demonstrated the feasibility of reverse vaccinology approach in the selection and design of immunogenic peptides against NNV in grouper to serve as the potential vaccine candidate.

Design of a multi-epitope-based peptide vaccine against the SARS-CoV-2 Omicron variant using bioinformatics approach

SARS-CoV-2, a member of the coronavirus family, is an RNA virus characterized by a single-stranded genome and is responsible for the development of COVID-19. The emergence of the Omicron variant of SARS-CoV-2 in 2021 marked a significant variation recognized by the World Health Organization. The primary objective of this study is to investigate the spike glycoprotein of the Omicron variant of SARS-CoV-2 and identify potential immunogenic epitopes in order to design multi-epitope vaccine constructs. Among the other major structural proteins of the coronavirus, the spike glycoprotein stands out as the largest. Importantly, individuals who have recovered from SARS-CoV-2 and COVID-19 were found to possess antibodies that target the spike glycoprotein. This article asserts that the vaccine presented in this study has the potential to elicit immune responses against previous variants, including the Omicron variant, as well as future variations. This is attributed to the utilization of a Java-based tool, which facilitated the identification of conserved epitopes with high immunogenicity scores, ensuring their non-toxic and non-allergenic properties. Our analysis provides strong evidence for the conservation of these epitopes across all coronavirus sequences detected in various countries since the beginning of the pandemic. The vaccine was subsequently constructed by integrating the identified conserved epitopes with linkers and adjuvants. The vaccine was subsequently evaluated through computational tests to assess their efficacy and performance. Communicated by Ramaswamy H. Sarma

Bioinformatics-based prediction and screening of immunogenic epitopes of Toxoplasma gondii rhoptry proteins 7, 21 and 22 as candidate vaccine target

IntroductionToxoplasmosis is a well-known zoonotic disease caused by Toxoplasma gondii. The main causes of the disease range from eating undercooked or contaminated meat and shellfish to cleaning litter trays into which cats that excreted toxoplasma via faeces. This pathogen can live for a very long time, possibly a lifetime, within the bodies of humans and other animals. Aims and objectivesThis study aimed to predict and analyse candidate immunogenic epitopes for vaccine development by evaluating the physio-chemical properties, multiple sequence alignment, secondary and tertiary structures, phosphorylation sites, transmembrane domains, and signal peptides, of T. gondii rhoptry proteins ROP7, ROP21, and ROP22 using bioinformatics tools. MethodsTo find immunogenic epitopes of rhoptry proteins, numerous bioinformatics web servers were used containing multiple sequence alignment, physiochemical properties, antigenicity and allergenicity, post-translational modification sites (PTMs), signal peptides, transmembrane domains, secondary and tertiary structures, and screening of predicted epitopes. We evaluated immunogenic linear B-cell epitopes as candidate proteins for vaccine development. ResultsNine epitopes were identified for each protein, and analysis of immunogenicity, revealed three candidate epitopes for ROP7, one for ROP21, and four for ROP22. Among all candidate epitopes, ROP22 contained the most immunogenic epitopes with immunogenicity score of 0.50575. ConclusionWe acquired detailed information on predicted immunogenic epitopes using in-silico methods. The results provide a foundation for further experimental analysis of toxoplasmosis, and potential vaccine development.

Molecular and immune profiling of TP53-mutated ovarian cancers with non-BRCA1/2 homologous recombination gene alterations.

5579 Background: Molecular alterations in homologous recombination (HR) pathway genes lead to HR deficiency (HRD) in ~50% of high grade serous ovarian carcinomas (HGSOC). The immune microenvironment of BRCA1/2-mutated HGSOC has been extensively characterized, in contrast to tumors with mutations in other HR genes. We evaluated a cohort of epithelial ovarian cancers (EOC) enriched for HGSOC and compared molecular and immune signatures of tumors with BRCA1/2 mutations ( BRCAm) to those with non-BRCA1/2 HR gene mutations (NBHRD). Methods: We characterized 173 EOC by comprehensive genomic and immune profiling (CGIP) using panel-based next-generation DNA (523 genes) and RNA sequencing (384 genes), respectively. We enriched for HGSOC by selecting for TP53 mutations and excluding tumors with non-serous histology or any mutation in POLE, MLH1, PMS2, MSH2, MSH6, or nucleotide excision repair genes. Tumors lacking BRCA1/2-mutations were classified as NBHRD if a reportable mutation was present in canonical HR genes. Mutational burden (TMB, mut/Mb) was estimated from detected SNV/indels. We calculated immune-related expression signatures, including tumor immunogenicity score (TIGS), cellular proliferation (CP), and cancer testis antigen burden (CTAB). PD-L1 expression was assessed by tumor proportion score (TPS) following 22C3 immunohistochemistry. Differences in TMB, TIGS, CP, CTAB, PD-L1 and gene expression rank were tested for using Wilcoxon rank-sum test. Results: Of 173 EOC, 84 cases had complete CGIP, including 11 (13%) BRCAm, 7 (8%) NBHRD (Table), and 66 (79%) lacking HR gene alterations. On average, NBHRD tumors harbored lower TMB (4.1±2.3 vs. 6.6±2.3 vs. p=0.03) and elevated TIGS (59.1±16.2 vs. 41.7±16.2 p=0.044) compared to BRCAm tumors. We found no difference in PD-L1 (p=0.21), CP (p=0.26), or CTAB (p=0.48). Expression changes were identified in 32 immune genes, of which 31 (97%) were substantially up-regulated (1.3-3.3 fold) in NBHRD compared to BRCAm tumors. Conclusions: Our findings showed our cohort of NBHRD tumors were more immunogenic but less mutagenic than BRCAm tumors, implying enhanced immunogenicity unrelated to neoantigen load. Given that immune checkpoint inhibitors have shown limited efficacy in ovarian cancer, our data suggest NBHRD tumors may have stronger and possibly distinct immune escape mechanisms compared to BRCAm tumors. [Table: see text]

Harnessing immunoinformatics for developing a multiple-epitope peptide-based vaccination approach against SARS-CoV-2 spike protein

COVID-19 has spread to over 200 countries with variable severity and mortality rates. Computational analysis is a valuable tool for developing B-cell and T-cell epitope-based vaccines. In this study, by harnessing immunoinformatics tools, we designed a multiple-epitope vaccine to protect against COVID-19. The candidate epitopes were designed from highly conserved regions of the SARS-CoV-2 spike (S) glycoprotein. The consensus amino acids sequence of ten SARS-CoV-2 variants including Gamma, Beta, Epsilon, Delta, Alpha, Kappa, Iota, Lambda, Mu, and Omicron was involved. Applying the multiple sequence alignment plugin and the antigenic prediction tools of Geneious prime 2021, ten predicted variants were identified and consensus S-protein sequences were used to predict the antigenic part. According to ElliPro analysis of S-protein B-cell prediction, we explored 22 continuous linear epitopes with high scores ranging from 0.879 to 0.522. First, we reported five promising epitopes: BE1 1115-1192, BE2 481-563, BE3 287-313, BE4 62-75, and BE5 112-131 with antigenicity scores of 0.879, 0.86, 0.813, 0.779, and 0.765, respectively, while only nine discontinuous epitopes scored between 0.971 and 0.511. Next, we identified 194 Major Histocompatibility Complex (MHC) ‐ I and 156 MHC ‐ II epitopes with antigenic characteristics. These spike-specific peptide-epitopes with characteristically high immunogenic and antigenic scores have the potential as a SARS-CoV-2 multiple-epitope peptide-based vaccination strategy. Nevertheless, further experimental investigations are needed to test for the vaccine efficacy and efficiency.

Developing a multiepitope vaccine for the prevention of SARS-CoV-2 and monkeypox virus co-infection: A reverse vaccinology analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and monkeypox virus (MPXV) severely threaten human health; however, currently, no vaccine can prevent a co-infection with both viruses. Five antigens were selected to predict dominant T and B cell epitopes screened for immunogenicity, antigenicity, toxicity, and sensitization. After screening, all antigens joined in the construction of a novel multiepitope vaccine. The physicochemical and immunological characteristics, and secondary and tertiary structures of the vaccine were predicted and analyzed using bio- and immunoinformatics. Finally, codon optimization and cloning in-silico were performed. A new multiepitope vaccine, named S7M8, was constructed based on four helper T lymphocyte (HTL) epitopes, six cytotoxic T lymphocyte (CTL) epitopes, five B cell epitopes, as well as Toll-like receptor (TLR) agonists. The antigenicity and immunogenicity scores of the S7M8 vaccine were 0.907374 and 0.6552, respectively. The S7M8 vaccine was comprised of 26.96% α-helices, the optimized Z-value of the tertiary structure was -5.92, and the favored area after majorization in the Ramachandran plot was 84.54%. Molecular docking showed that the S7M8 vaccine could tightly bind to TLR2 (-1100.6kcal/mol) and TLR4 (-950.3kcal/mol). In addition, the immune stimulation prediction indicated that the S7M8 vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies. S7M8 is a promising biomarker with good antigenicity, immunogenicity, non-toxicity, and non-sensitization. The S7M8 vaccine can trigger significantly high levels of Th1 cytokines and antibodies and may be a potentially powerful tool in preventing SARS-CoV-2 and MPXV.

In-Silico Designing of a Multi-Epitope Vaccine against SARS-CoV2 and Studying the Interaction of the Vaccine with Alpha, Beta, Delta and Omicron Variants of Concern.

The sudden appearance of the SARS-CoV2 virus has almost changed the future of vaccine development. There have been many different approaches to vaccination; among them, computational vaccinology in the form of multi-epitope vaccines with excellent immunological properties and minimal contamination or other adverse reactions has emerged as a promising strategy with a lot of room for further study in this area. Designing a multi-epitope vaccine from the spike protein of SARS-CoV2 based on immunoinformatics and in-silico techniques. Evaluating the binding affinity of the constructed vaccine against the major variants of concern (alpha, beta, delta, and omicron) using docking studies. The potential antigenic, immunogenic, and non-allergic T-cell epitopes were thoroughly explored using IEDB, NetCTL1.2, and NetMHCII pan 3.2 servers. The best suitable linker was identified using the ExPASy Protparam tool and VERIFY 3D. The 3D model of the vaccine was developed by RaptorX and the model was validated using ERRAT, Z-score, and Ramachandran Plot. Docking studies of the vaccine with TLR-2, 3, 4, and 7 and alpha, beta, delta, and omicron variants were performed using HADDOCK 2.4. The vaccine construct showed good antigenic and immunogenic scores and was non-allergic as well. The model was capable of binding to all four selected Toll-like receptors. Docking scores with variants were also promising. All the variants showed good binding ability with the vaccine construct. Interaction with the alpha variant was found to be the most intense, followed by delta, beta, and omicron.

A systems approach evaluating the impact of SARS-CoV-2 variant of concern mutations on CD8+ T cell responses.

T cell recognition of SARS-CoV-2 antigens after vaccination and/or natural infection has played a central role in resolving SARS-CoV-2 infections and generating adaptive immune memory. However, the clinical impact of SARS-CoV-2-specific T cell responses is variable and the mechanisms underlying T cell interaction with target antigens are not fully understood. This is especially true given the virus' rapid evolution, which leads to new variants with immune escape capacity. In this study, we used the Omicron variant as a model organism and took a systems approach to evaluate the impact of mutations on CD8+ T cell immunogenicity. We computed an immunogenicity potential score for each SARS-CoV-2 peptide antigen from the ancestral strain and Omicron, capturing both antigen presentation and T cell recognition probabilities. By comparing ancestral vs. Omicron immunogenicity scores, we reveal a divergent and heterogeneous landscape of impact for CD8+ T cell recognition of mutated targets in Omicron variants. While T cell recognition of Omicron peptides is broadly preserved, we observed mutated peptides with deteriorated immunogenicity that may assist breakthrough infection in some individuals. We then combined our scoring scheme with an in silico mutagenesis, to characterise the position- and residue-specific theoretical mutational impact on immunogenicity. While we predict many escape trajectories from the theoretical landscape of substitutions, our study suggests that Omicron mutations in T cell epitopes did not develop under cell-mediated pressure. Our study provides a generalisable platform for fostering a deeper understanding of existing and novel variant impact on antigen-specific vaccine- and/or infection-induced T cell immunity.

IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method.

In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, ‘IntegralVac,’ by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac with 4500 human cancer MHC I peptides obtained from the Immune Epitope Database (IEDB) and with cancer and COVID epitopes previously selected in our laboratory. The other data referenced from existing deep learning tools served as a positive control to ensure successful prediction was possible. As evidenced by increased accuracy and AUC, IntegralVac improved the prediction rate of top-ranked epitopes. We also examined the compatibility between other servers’ clinical checkpoint filters and IntegralVac. This was to ensure that the other servers had a means for predicting additional checkpoint filters that we wanted to implement in IntegralVac. The clinical checkpoint filters, including allergenicity, antigenicity, and toxicity, were used as additional predictors to improve IntegralVac’s prediction accuracy. We generated immunogenicity scores by cross-comparing sequence inputs with each other and determining the overlap between each individual peptide sequence. The IntegralVac increased the immunogenicity prediction accuracy to 90.1% AUC and the binding affinity accuracy to 95.4% compared to the control NetMHCPan server. The IntegralVac opens new avenues for future in silico methods, by building upon established models for continued prediction accuracy improvement.

Bioinformatics pipeline unveils genetic variability to synthetic vaccine design for Indian SARS-CoV-2 genomes

In the worrisome scenarios of various waves of SARS-CoV-2 pandemic, a comprehensive bioinformatics pipeline is essential to analyse the virus genomes in order to understand its evolution, thereby identifying mutations as signature SNPs, conserved regions and subsequently to design epitope based synthetic vaccine. We have thus performed multiple sequence alignment of 4996 Indian SARS-CoV-2 genomes as a case study using MAFFT followed by phylogenetic analysis using Nextstrain to identify virus clades. Furthermore, based on the entropy of each genomic coordinate of the aligned sequences, conserved regions are identified. After refinement of the conserved regions, based on its length, one conserved region is identified for which the primers and probes are reported for virus detection. The refined conserved regions are also used to identify T-cell and B-cell epitopes along with their immunogenic and antigenic scores. Such scores are used for selecting the most immunogenic and antigenic epitopes. By executing this pipeline, 40 unique signature SNPs are identified resulting in 23 non-synonymous signature SNPs which provide 28 amino acid changes in protein. On the other hand, 12 conserved regions are selected based on refinement criteria out of which one is selected as the potential target for virus detection. Additionally, 22 MHC-I and 21 MHC-II restricted T-cell epitopes with 10 unique HLA alleles each and 17 B-cell epitopes are obtained for 12 conserved regions. All the results are validated both quantitatively and qualitatively which show that from genetic variability to synthetic vaccine design, the proposed pipeline can be used effectively to combat SARS-CoV-2.

In silico Analysis of Peptide-Based Biomarkers for the Diagnosis and Prevention of Latent Tuberculosis Infection.

BackgroundLatent tuberculosis infection (LTBI) is the primary source of active tuberculosis (ATB), but there are no specific methods for diagnosing and preventing LTBI.MethodsDominant T and B cell epitopes predicted from five antigens related to LTBI and Mycobacterium tuberculosis region of difference (LTBI-RD) were used to construct a novel polypeptide molecule (PPM). Then, the physicochemical properties, secondary structure, tertiary structure of the PPM, and its binding ability to toll-like receptor 2 (TLR2) and TLR4 were analyzed by bioinformatics tools. Finally, immune stimulation and expression optimization of the PPM were carried out.ResultsFour helper T lymphocytes (HTL) epitopes, five cytotoxic T lymphocytes (CTL) epitopes, and three B cell epitopes were predicted and screened from five LTBI-RD related antigens. These epitopes were connected in series with linkers and adjuvants to construct a novel PPM termed C543P. The results indicated that antigenicity and immunogenicity scores of the C543P candidate were 0.936399 and 1.36469, respectively. The structural analysis results showed that the C543P candidate had good stability. Its secondary structure contained 43.6% α-helix, the Z-score after tertiary structure optimization was −7.9, and the Ramachandran diagram showed that 88.77% amino acid residues of the C543P candidate were in the allowable region. Furthermore, the C543P candidate showed an excellent affinity to TLR2 (−1091.7kcal/mol) and TLR4 (−1102.7kcal/mol). In addition, we also analyzed the immunological characteristics of the C543P candidate. Immune stimulation prediction showed that the C543P candidate could effectively activate T and B lymphocytes and produce high levels of Th1 cytokines such as IFN-γ and IL-2.ConclusionWe constructed a novel PPM with acceptable antigenicity, immunogenicity, stability, and ability to induce robust immune responses. This study provides a new diagnostic biomarker or peptides-based vaccine for LTBI diagnosis and prevention.

Abstract 6389: A novel computational pipeline supported with in vivo vaccination models for identification and validation of neoantigens

Abstract Background: Personalized neoantigen-based therapies are being actively developed with several clinical trials currently ongoing. The standard methodology to identify patient specific neoantigens relies on computational algorithms for analyzing mutation, expression, and HLA profiles from patients’ NGS data. However, clinical reports have thus far showed that the number of actionable neoantigens was not as high as originally expected. One possible reason is due to a lack of appropriate prediction models, especially for immunogenicity, which is largely hindered by a scarcity of training data based on in vivo vaccination models. In addition, HLA allele status and/or impurity and heterogeneity in tumor samples are other factors affecting the selection efficiency. Methods: We have developed a computational pipeline to identify neoantigens from patients’ WES and RNA-Seq data. Our HLA assessment not only determines HLA types but also detects HLA LOH events from WES and down-regulation of HLA gene expression from RNA-Seq taking into account tumor purity. The neoantigen candidates were ranked based on a presentation score calculated by a regression model optimized with publicly available immunopeptidome data. We tested our top-ranked neoantigen peptides by immunizing them into HLA-A02:01, A24:02, B07:02, and B35:01-transgenic mice and performed ELISPOT to determine positive or negative in vivo immune responses. Furthermore, we trained a neural network to define an immunogenicity score based on the results from the transgenic mouse experiments. Results: We generated NGS data from over 100 clinical samples and analyzed the data using our computational pipeline. We observed a wide range in the tumor mutational burden and tumor purity of our samples, and HLA LOH was frequently observed. From 27 patient cases, 275 of HLA-A02:01-, A24:02-, B07:02-, or B35:01-predicted neoantigen epitopes were tested in transgenic mice and confirmed that the rate of positively reacted epitopes increased in a correlated manner to the presentation score, achieving 82% positive rate in top scored 35 epitopes and 47% in overall 275 epitopes. We further developed an immunogenicity prediction model and tested its performance in additional clinical samples. By integrating it into the selection criteria, we achieved higher positive rate than using the presentation score solely. Conclusions: We have developed a robust computational pipeline to identify putatively actionable neoantigens from patients’ WES and RNA-Seq data. We detected frequent HLA LOH events indicating that this is a common mechanism of immune evasion. We also showed the feasibility of improving prediction algorithms using HLA-transgenic mice. Accurate prediction performance enables us to decrease the number of vaccine antigens to be immunized per patient, leading to a decrease in the manufacturing cost and complexity in quality control. Citation Format: Kazushi Hiranuka, Keigo Takahashi, Dave Tang, Noriko Watanabe, Takashi Yamada, Yuji Mishima, Norihiro Fujinami, Manami Shimomura, Toshihiro Suzuki, Tetsuya Nakatsura, Norihiro Nakamura. A novel computational pipeline supported with in vivo vaccination models for identification and validation of neoantigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6389.