NetCTLpan: pan-specific MHC class I pathway epitope predictions

Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review)

Cancer immunotherapy stimulates and enhances antitumor immune responses to eliminate cancer cells. Neoantigens, which originate from specific mutations within tumor cells, are key targets in cancer immunotherapy. Neoantigens manifest as abnormal peptide fragments or protein segments that are uniquely expressed in tumor cells, making them highly immunogenic. As a result, they activate the immune system, particularly T cell‑mediated immune responses, effectively identifying and eliminating tumor cells. Certain tumor‑associated antigens that are abnormally expressed in normal host proteins in cancer cells are promising targets for immunotherapy. Neoantigens derived from mutated proteins in cancer cells offer true cancer specificity and are often highly immunogenic. Furthermore, most neoantigens are unique to each patient, highlighting the need for personalized treatment strategies. The precise identification and screening of neoantigens are key for improving treatment efficacy and developing individualized therapeutic plans. The neoantigen prediction process involves somatic mutation identification, human leukocyte antigen (HLA) typing, peptide processing and peptide‑HLA binding prediction. The present review summarizes the major current methods used for neoantigen screening, available computational tools and the advantages and limitations of various techniques. Additionally, the present review aimed to summarize experimental strategies for validating the immunogenicity of the predicted neoantigens, which will determine whether these neoantigens can effectively trigger immune responses, as well as challenges encountered during neoantigen screening, providing relevant recommendations for the optimization of neoantigen‑based immunotherapy.

Impact of mutations in immunodominant regions of SARS-CoV-2 variants on recognition by CD8+ T cell: An in silico analysis

BACKGROUND

This study aimed to investigate whether mutations in the immunodominant regions of the S, M, and N proteins of the Gamma, Delta, and Omicron SARS-CoV-2 variants that circulated in Brazil affect the recognition of viral antigens by Brazilian HLA-I-restricted CD8+ T cell epitopes, using an in silico approach.

METHODS

Sequences of the Gamma (n = 36,174), Delta (n = 35,129), and Omicron (n = 336) variants were retrieved using GISAID. Consensus sequences were generated using Geneious software with NC045512 as a reference. Epitopes for the S, M, and N proteins of both the original and variant sequences were predicted using NetCTLpan 1.1, NetMHCpan 4.0, and VaxiJen v2.0. The positions occupied by these epitopes, with high probability of presentation, affinity to HLA molecules, and antigenicity, were identified as potentially immunodominant regions.

RESULTS

The S protein of the reference sequence (NC045512) and its variants contained 17 immunodominant regions. Delta showed the highest conservation (94.1 %, 16), followed by Gamma (82.3 %, 14) and Omicron (70.5 %, 12). Omicron exhibited the greatest mutational variability and had regions of increased antigenicity and two novel immunodominant regions with broader human leukocyte antigen (HLA) recognition. Additionally, Omicron lost two previously identified immunodominant regions and had one region of reduced antigenicity that did not affect HLA recognition. Gamma had mutations in three regions that increased both antigenicity and HLA recognition. Delta had only one mutated region with lower antigenicity, which did not affect HLA recognition. Notably, new immunodominant regions for the M and N proteins appeared in the Omicron variant.

CONCLUSIONS

Brazilian HLA-I-restricted CD8+ T cell epitopes from SARS-CoV-2 immunodominant regions are partially conserved in the Gamma, Delta, and Omicron variants circulating in Brazil, suggesting effective a cross-protective immune response that may help reduce COVID-19 severity and mortality.

Neoantigen-based immunotherapy: advancing precision medicine in cancer and glioblastoma treatment through discovery and innovation

Neoantigen-based immunotherapy has emerged as a transformative approach in cancer treatment, offering precision medicine strategies that target tumor-specific antigens derived from genetic, transcriptomic, and proteomic alterations unique to cancer cells. These neoantigens serve as highly specific targets for personalized therapies, promising more effective and tailored treatments. The aim of this article is to explore the advances in neoantigen-based therapies, highlighting successful treatments such as vaccines, tumor-infiltrating lymphocyte (TIL) therapy, T-cell receptor-engineered T cells therapy (TCR-T), and chimeric antigen receptor T cells therapy (CAR-T), particularly in cancer types like glioblastoma (GBM). Advances in technologies such as next-generation sequencing, RNA-based platforms, and CRISPR gene editing have accelerated the identification and validation of neoantigens, moving them closer to clinical application. Despite promising results, challenges such as tumor heterogeneity, immune evasion, and resistance mechanisms persist. The integration of AI-driven tools and multi-omic data has refined neoantigen discovery, while combination therapies are being developed to address issues like immune suppression and scalability. Additionally, the article discusses the ongoing development of personalized immunotherapies targeting tumor mutations, emphasizing the need for continued collaboration between computational and experimental approaches. Ultimately, the integration of cutting-edge technologies in neoantigen research holds the potential to revolutionize cancer care, offering hope for more effective and targeted treatments.

Clinical microbiology and artificial intelligence: Different applications, challenges, and future prospects

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training different AI algorithms. Recently, a number of machine learning (ML) and deep learning (DL) algorithms are developed and evaluated using diverse microbiological datasets. These datasets included spectral analysis (Raman and MALDI-TOF spectroscopy), microscopic images (Gram and acid fast stains), and genomic and protein sequences (whole genome sequencing (WGS) and protein data banks (PDBs)). The primary objective of these algorithms is to minimize the time, effort, and expenses linked to conventional analytical methods. Furthermore, AI algorithms are incorporated with quantitative structure-activity relationship (QSAR) models to predict novel antimicrobial agents that address the continuing surge of antimicrobial resistance. During the COVID-19 pandemic, AI algorithms played a crucial role in vaccine developments and the discovery of new antiviral agents, and introduced potential drug candidates via drug repurposing. However, despite their significant benefits, the implementation of AI encounters various challenges, including ethical considerations, the potential for bias, and errors related to data training. This review seeks to provide an overview of the most recent applications of artificial intelligence in clinical microbiology, with the intention of educating a wider audience of clinical practitioners regarding the current uses of machine learning algorithms and encouraging their implementation. Furthermore, it will discuss the challenges related to the incorporation of AI into clinical microbiology laboratories and examine future opportunities for AI within the realm of infectious disease epidemiology.

Immunogenicity of Trypanosoma cruzi Multi-Epitope Recombinant Protein as an Antigen Candidate for Chagas Disease Vaccine in Humans

Chagas disease, caused by the protozoan Trypanosoma cruzi (T. cruzi), is the most significant neglected tropical disease affecting individuals in the Americas. Currently, available drugs, such as nifurtimox and benznidazole (BZN), are both toxic and ineffective in the chronic phase of the disease. A promising alternative is the development of a Chagas disease vaccine, although this effort is hampered by the complexity of the parasite and HLA polymorphisms. In addition, the activation of epitope-specific CD8+ T cells is critical to conferring a robust cell-mediated immune response and protection by producing IFN-γ and perforin. Thus, the antigen (s) for the development of a Chagas vaccine or immunotherapy must include CD8+ T cell epitopes. In this study, we aimed to develop a multi-epitope recombinant protein as a novel human vaccine for Chagas disease. Sixteen database programs were used to predict de novo 40 potential epitopes for the HLA-A*02:01 allele. Nine out of the 40 predicted epitopes were able to elicit IFN-γ production in Peripheral Blood Mononuclear Cells (PBMCs) from Chagas patients. Molecular docking revealed a good binding affinity among the epitopes with diverse HLA molecules. Therefore, a recombinant multi-epitope protein including these nine T. cruzi CD8+ epitopes was expressed and demonstrated to recall an antigen-specific immune response in ex-vivo assays using PBMCs from Chagas patients with the HLA-A*02 allele. These findings support the development of this multi-epitope protein as a promising candidate human vaccine against Chagas disease.

Transcript-targeted antigen mapping reveals the potential of POSTN splicing junction epitopes in glioblastoma immunotherapy

Tumor antigens are crucial for T-cell mediated immunotherapy, but identified antigens for gliomas remain limited. Aberrant splicing variants are commonly expressed in tumors, resulting in unique tumor isoforms with potential antigenic properties. Herein, we analyzed multi-omics data from 587 glioma patients and assembled a library of putative tumor-enriched isoform antigens (TIA) and corresponding peptides presented on each HLA-I allele. We constructed an individual-specific TIA peptide candidate repertoire for each patient based on their TIA expression and HLA-I haplotypes. TIAs were highly expressed, enriched with glioma malignancy, and demonstrated strong HLA-binding affinity. We focused on periostin isoform-203 (POSTN-203), which was associated with poor survival of patients and contained multiple predicted HLA-restricted peptide epitopes. A selected HLA-A11-restricted peptide from POSTN-203 (POSTN-203A11) induced antigen-specific T-cell responses against both peptide-pulsed and POSTN-203-expressing glioma cells in an HLA-specific manner. Our findings highlight TIAs as a promising source of immunogenic antigens and POSTN-203 as a potential promising target for glioma immunotherapy.

In silico design of Ebola virus Glycoprotein antigenic peptides as vaccine candidates

Ebola virus (EBOV) is a filovirus that causes severe hemorrhagic fever and has a fatality rate between 50 and 90%. The vaccines were developed against the Ebola Zaire species; therefore, it is necessary to develop vaccines against other species to control future outbreaks. The objective of this work was to obtain vaccine candidate peptides against different EBOV species through the use of bioinformatics programs and servers that allow glycoprotein (GP) to be analyzed. GP sequences of various EBOV species that did not present gaps or unspecified amino acids or that were repeated (same year, region and laboratory) were downloaded from the NCBI database. A consensus sequence was generated and used to determine vaccine candidate peptides, which were evaluated, through a combination of servers and molecular dynamics, for their ability to interact with B and T lymphocytes, toxicity, allergenicity, solvent exposure, glycosylation, antigenicity, and presence in mature GP. Five vaccine candidate peptides were identified, of which PEP4 had the best characteristics evaluated in this study. PEP4 may be a potential candidate for the development of an EBOV vaccine.

Vaccine Development T-cell (MHC-I) Epitopes Identification Against the Indian HCV Genotype: An Approach Based on Immunoinformatic

Hepatitis C virus (HCV) infects approximately 58 million individuals worldwide, often progressing to chronic liver disease, cirrhosis, and hepatocellular carcinoma. The viral envelope glycoproteins E1 and E2 are critical for HCV entry and serve as primary targets for neutralizing antibodies. Recent advancements in cryo-electron tomography have provided high-resolution structures (3.5 Å) of the E1E2 heterodimer, revealing interactions between the E1 and E2 ectodomains, as well as neutralizing antibody complexes (e.g., AR4A, AT1209, IGH505). This structural information facilitates the design of a synthetic peptide vaccine targeting conserved E1 and E2 regions. We suggest developing a vaccine tailored to the HLA-A*24:02 allele, the most prevalent in the Indian population. Epitope candidates will be screened using immunoinformatics tools, incorporating epitopes derived from epitope mapping with 7t6x protein structure modeling. Molecular docking studies will identify high-affinity interactions with human MHC-Class I alleles, using tools such as AutoDock and HADDOCK. GROMACS molecular dynamics simulations will assess peptide-HLA binding stability and dynamics. Among ten screened epitopes, KWEYVVLLF and QWQVLPCSF emerged as the most promising based on their toxicity profiles, conservation, and docking scores with HLA-A*24:02 (- 9.3 kcal/mol for KWEYVVLLF and - 225.34 kcal/mol for QWQVLPCSF). Molecular dynamics simulations indicated that the KWEY segment of KWEYVVLLF underwent structural changes, while the VVLLF region maintained stable binding to Chain A, suggesting immunogenic potential. These epitopes represent strong candidates for T-cell-based vaccines, and the reverse vaccinology approach, supported by computational tools, offers a population-specific strategy for HCV vaccine development, advancing precision immunotherapy.

In silico design of multi-epitope vaccine candidate based on structural proteins of porcine reproductive and respiratory syndrome virus

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most common respiratory disease-causing viral agents. Swine infected with PRRSV exhibit severe respiratory symptoms and reproductive failure, leading to significant economic losses. To address this issue, inactivated and live-attenuated vaccines have been developed. However, the current commercially available PRRSV vaccines do not confer sufficient protection or have safety issues. The use of epitope-based subunit vaccines reduce safety risks by including only specific immunogenic portions of the antigens. To enhance immune protection, this study targeted multiple structural proteins of PRRSV, including GP2, GP3, GP4, GP5, membrane (M), envelope (E), GP5a, and nucleocapsid (N), to enable the discovery of novel epitopes. Thus, a reverse vaccinology approach was utilized to design a multi-epitope subunit vaccine construct against PRRSV. Using different tools, seven linear B cell, seven cytotoxic T cell, and three helper T cell epitopes were predicted to be safe, antigenic, and immunogenic. These epitopes were linked together, and a protein adjuvant, heparin-binding hemagglutinin, was added to increase the vaccine's immunogenicity. The construct was then docked to Toll-like receptor 4 (TLR4) to assess its ability to initiate the innate immune response. The final vaccine construct was determined to be antigenic, stable, non-allergenic, and soluble. Furthermore, the vaccine demonstrated stable binding to TLR4 based on coarse-grained and atomistic molecular dynamics simulations. Finally, the immune simulation of the vaccine construct showed a robust immune response against PRRSV. In this study, a candidate vaccine construct was successfully designed as a potential strategy against PRRSV.

Leveraging mRNA technology for antigen based immuno-oncology therapies

The application of messenger RNA (mRNA) technology in antigen-based immuno-oncology therapies represents a significant advancement in cancer treatment. Cancer vaccines are an effective combinatorial partner to sensitize the host immune system to the tumor and boost the efficacy of immune therapies. Selecting suitable tumor antigens is the key step to devising effective vaccinations and amplifying the immune response. Tumor neoantigens are de novo epitopes derived from somatic mutations, avoiding T-cell central tolerance of self-epitopes and inducing immune responses to tumors. The identification and prioritization of patient-specific tumor neoantigens are based on advanced computational algorithms taking advantage of the profiling with next-generation sequencing considering factors involved in human leukocyte antigen (HLA)-peptide-T-cell receptor (TCR) complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. This review discusses the development and clinical application of mRNA vaccines in oncology, with a particular focus on recent clinical trials and the computational workflows and methodologies for identifying both shared and individual antigens. While this review centers on therapeutic mRNA vaccines targeting existing tumors, it does not cover preventative vaccines. Preclinical experimental validations are crucial in cancer vaccine development, but we emphasize the computational approaches that facilitate neoantigen selection and design, highlighting their role in advancing mRNA vaccine development. The versatility and rapid development potential of mRNA make it an ideal platform for personalized neoantigen immunotherapy. We explore various strategies for antigen target identification, including tumor-associated and tumor-specific antigens and the computational tools used to predict epitopes capable of eliciting strong immune responses. We address key design considerations for enhancing the immunogenicity and stability of mRNA vaccines, as well as emerging trends and challenges in the field. This comprehensive overview highlights the therapeutic potential of mRNA-based cancer vaccines and underscores ongoing research efforts aimed at optimizing these therapies for improved clinical outcomes.

Immunoinformatics approach: Developing a multi-epitope vaccine with novel carriers targeting monkeypox virus

Since May 2022, the global spread of monkeypox virus (MPXV) has presented a significant threat to public health. Despite this, there are limited preventive measures available. In this study, different computational tools were employed to design a multi-epitope vaccine targeting MPXV. Three key MPXV proteins, M1R, B6R, and F3L, were chosen for epitope selection, guided by bioinformatic analyses to identify immunodominant epitopes for T- and B-cell activation. To enhance immune stimulation and facilitate targeted delivery of the vaccine to specific cells, the selected epitopes were linked to novel carriers, including the extracellular domain of cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), a 12-mer Clec9a binding peptide (CBP-12), and a Toll-like receptor 2 (TLR2) peptide ligand. The designed vaccine construct exhibited strong antigenicity along with nonallergenic and nontoxic properties, with favorable physicochemical characteristics. The validated vaccine's tertiary structure underwent evaluation for interactions with CD80/86, Clec9a, and TLR2 through molecular docking and molecular dynamics simulation. The results ensured the vaccine's stability and high affinity for the aforementioned receptors. In silico immune simulations studies revealed robust innate and adaptive immune responses, including enhanced mucosal immunity essential for protection against MPXV. Ultimately, the DNA sequence of the vaccine construct was synthesized and successfully cloned into the pET-22b(+) vector. Our study, through integration of computational predictions, suggests the proposed vaccine's potential efficacy in safeguarding against MPXV; however, further in vitro and in vivo validations are imperative to assess real-world effectiveness and safety.

LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks

Introduction

The identification of peptides eluted from HLA complexes by mass spectrometry (MS) can provide critical data for deep learning models of antigen presentation prediction and promote neoantigen vaccine design. A major challenge remains in determining which HLA allele eluted peptides correspond to.

Methods

To address this, we present a tool for prediction of multiple allele (MA) presentation called LRMAHpan, which integrates LSTM network and ResNet_CA network for antigen processing and presentation prediction. We trained and tested the LRMAHpan BA (binding affinity) and the LRMAHpan AP (antigen processing) models using mass spectrometry data, subsequently combined them into the LRMAHpan PS (presentation score) model. Our approach is based on a novel pHLA encoding method that enables the integration of neoantigen prediction tasks into computer vision methods. This method aggregates MA data into a multichannel matrix and incorporates peptide sequences to efficiently capture binding signals.

Results

LRMAHpan outperforms standard predictors such as NetMHCpan 4.1, MHCflurry 2.0, and TransPHLA in terms of positive predictive value (PPV) when applied to MA data. Additionally, it can accommodate peptides of variable lengths and predict HLA class I and II presentation. We also predicted neoantigens in a cohort of metastatic melanoma patients, identifying several shared neoantigens.

Discussion

Our results demonstrate that LRMAHpan significantly improves the accuracy of antigen presentation predictions.

Unveiling cross-reactivity: implications for immune response modulation in cancer

Antigen recognition by CD8+ T-cell receptors (TCR) is crucial for immune responses to pathogens and tumors. TCRs are cross-reactive, a single TCR can recognize multiple peptide-Human Leukocyte Antigen (HLA) complexes. The study of cross-reactivity can support the development of therapies focusing on immune modulation, such as the expansion of pre-existing T-cell clones to fight pathogens and tumors. The peptide-HLA (pHLA) surface has previously been used to identify TCR cross-reactivities. In the present work, we sought to perform a comprehensive analysis of peptide-HLA by selecting thousands of human and viral epitopes. We profit from established docking models to identify features from different spatial perspectives of HLA-A*02:01, explore similarities between self and non-self epitopes, and list potential cross-reactive epitopes of therapeutic interest. A total of 2631 unique epitopes from representative viral proteins or human proteins were modeled. We were able to demonstrate that cross-reactive CDR3 sequences from public databases recognize epitopes with similar electrostatic potential, charge, and spatial location. Using data from published studies that measured T-cell reactivity to mutated epitopes, we observed a negative correlation between epitope dissimilarity and T-cell activation. Most analysed cancer epitopes were more similar to self epitopes, yet we identified features distinguishing those more similar to viral antigens. Finally, we enumerated potential cross-reactivities between tumoral and viral epitopes and highlighted some challenges in their identification for therapeutic use. Moreover, the thousands of peptide-HLA complexes generated in our work constitute a valuable resource to study T-cell cross-reactivity.

StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides

Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10.

Charting Peptide Shared Sequences Between 'Diabetes-Viruses' and Human Pancreatic Proteins, Their Structural and Autoimmune Implications

Diabetes mellitus (DM) is a metabolic syndrome characterized by hyperglycaemia, polydipsia, polyuria, and weight loss, among others. The pathophysiology for the disorders is complex and results in pancreatic abnormal function. Viruses have also been implicated in the metabolic syndrome. This study charted peptides to investigate and predict the autoimmune potential of shared sequences between 8 viral species proteins (which we termed 'diabetes-viruses') and the human pancreatic proteins. The structure and immunological relevance of shared sequences between viruses reported in DM onset and human pancreatic proteins were analysed. At nonapeptide mapping between human pancreatic protein and 'diabetic-viruses', reveal 1064 shared sequences distributed among 454 humans and 4288 viral protein sequences. The viral results showed herpesviruses, enterovirus (EV), human endogenous retrovirus, influenza A viruses, rotavirus, and rubivirus sequences are hosted by the human pancreatic protein. The most common shared nonapeptide was AAAAAAAAA, present in 30 human nonredundant sequences. Among the viral species, the shared sequence NSLEVLFQG occurred in 18 nonredundant EVs protein, while occurring merely in 1 human protein, whereas LGLDIEIAT occurred in 8 influenza A viruses overlapping to 1 human protein and KDELSEARE occurred in 2 rotaviruses. The prediction of the location of the shared sequences in the protein structures, showed most of the shared sequences are exposed and located either on the surface or cleft relative to the entire protein structure. Besides, the peptides in the viral protein shareome were predicted computationally for binding to MHC molecules. Here analyses showed that the entire 1064 shared sequences predicted 203 to be either HLA-A or B supertype-restricted epitopes. Fifty-one of the putative epitopes matched reported HLA ligands/T-cell epitopes majorly coming from EV B supertype representative allele restrictions. These data, shared sequences, and epitope charts provide important insight into the role of viruses on the onset of DM and its implications.

Computational strategies in Klebsiella pneumoniae vaccine design: navigating the landscape of in silico insights

The emergence of multidrug-resistant Klebsiella pneumoniae poses a grave threat to global public health, necessitating urgent strategies for vaccine development. In this context, computational tools have emerged as indispensable assets, offering unprecedented insights into klebsiellal biology and facilitating the design of effective vaccines. Here, a review of the application of computational methods in the development of K. pneumoniae vaccines is presented, elucidating the transformative impact of in silico approaches. Through a systematic exploration of bioinformatics, structural biology, and immunoinformatics techniques, the complex landscape of K. pneumoniae pathogenesis and antigenicity was unravelled. Key insights into virulence factors, antigen discovery, and immune response mechanisms are discussed, highlighting the pivotal role of computational tools in accelerating vaccine development efforts. Advancements in epitope prediction, antigen selection, and vaccine design optimisation are examined, highlighting the potential of in silico approaches to update vaccine development pipelines. Furthermore, challenges and future directions in leveraging computational tools to combat K. pneumoniae are discussed, emphasizing the importance of multidisciplinary collaboration and data integration. This review provides a comprehensive overview of the current state of computational contributions to K. pneumoniae vaccine development, offering insights into innovative strategies for addressing this urgent global health challenge.

A modular protein language modelling approach to immunogenicity prediction

Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe ImmugenX, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. ImmugenX comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, ImmugenX's encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. ImmugenX outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve = 0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. ImmugenX shows further improved performance on immunogenicity prediction with the integration of TCR context information. ImmugenX performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets.

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

A multi-epitope self-amplifying mRNA SARS-CoV-2 vaccine design using a reverse vaccinology approach

Background and purpose

Massive vaccine distribution is a crucial step to prevent the spread of SARS-CoV2 as the causative agent of COVID-19. This research aimed to design the multi-epitope self-amplifying mRNA (saRNA) vaccine from the spike and nucleocapsid proteins of SARS-CoV2.

Experimental approach

Commonly distributed constructions class I and II alleles of the Indonesian population were used to determine peptide sequences that trigger this population's high specificity T-cell response. The best vaccine candidate was selected through the analysis of tertiary structure validation and molecular docking of each candidate with TLR-4, TLR-8, HLA-A*24:02, and HLA-DRB1*04:05. The selected multi-epitope vaccine combined with the gene encoding the replication machinery that allows the RNA amplification in the host cell.

Findings/Results

Seven B-cell and four T-cell epitopes from the protein target were highly antigenic and conserved, non-allergen, non-toxic, and hydrophilic. Tertiary structure validation then determined the best multi-epitope construction with 269 AA in length containing hBD-2 adjuvant and PADRE. Most residues are predicted to be accessible by solvent and show high population coverage (99,26%). Molecular docking analysis demonstrated a stable and strong binding affinity with immune receptors. A recombinant plasmid as the template for mRNA production was constructed by inserting the multi-epitope DNA and non-structural polyprotein 1-4 gene of VEEV, which encodes the RNA replication complex to the cloning site of pcDNA3.1(+).

Conclusion and implication

In silico, design of self-amplifying mRNA could be a potential COVID-19 vaccine candidate since its ability to be amplified in the host cell can efficiently reduce the intake doses.

Allergies to Allergens from Cats and Dogs: A Review and Update on Sources, Pathogenesis, and Strategies

Inhalation allergies caused by cats and dogs can lead to a range of discomforting symptoms, such as rhinitis and asthma, in humans. With the increasing popularity of and care provided to these companion animals, the allergens they produce pose a growing threat to susceptible patients' health. Allergens from cats and dogs have emerged as significant risk factors for triggering asthma and allergic rhinitis worldwide; however, there remains a lack of systematic measures aimed at assisting individuals in recognizing and preventing allergies caused by these animals. This review provides comprehensive insights into the classification of cat and dog allergens, along with their pathogenic mechanisms. This study also discusses implementation strategies for prevention and control measures, including physical methods, gene-editing technology, and immunological approaches, as well as potential strategies for enhancing allergen immunotherapy combined with immunoinformatics. Finally, it presents future prospects for the prevention and treatment of human allergies caused by cats and dogs. This review will improve knowledge regarding allergies to cats and dogs while providing insights into potential targets for the development of next-generation treatments.

Advancing one health vaccination: In silico design and evaluation of a multi-epitope subunit vaccine against Nipah virus for cross-species immunization using immunoinformatics and molecular modeling

The resurgence of the Nipah virus (NiV) in 2023 has raised concerns for another potentially severe pandemic, given its history of high mortality from previous outbreaks. Unfortunately, no therapeutics and vaccines have been available for the virus. This study used immunoinformatics and molecular modeling to design and evaluate a multi-epitope subunit vaccine targeting NiV. The designed vaccine construct aims to stimulate immune responses in humans and two other intermediate animal hosts of the virus-swine and equine. Using several epitope prediction tools, ten peptides that induced B-lymphocyte responses, 17 peptides that induced cytotoxic T-lymphocyte (CTL) responses, and 12 peptides that induced helper T-lymphocyte (HTL) responses were mapped from nine NiV protein sequences. However, the CTL and HTL-inducing peptides were reduced to ten and eight, respectively, following molecular docking and dynamics. These screened peptides exhibited stability with 30 common major histocompatibility complex (MHC) receptors found in humans, swine, and equine. All peptides were linked using peptide linkers to form the multi-epitope construct and various adjuvants were tested to enhance its immunogenicity. The vaccine construct with resuscitation-promoting factor E (RpfE) adjuvant was selected as the final design based on its favorable physicochemical properties and superior immune response profile. Molecular docking was used to visualize the interaction of the vaccine to toll-like receptor 4 (TLR4), while molecular dynamics confirmed the structural stability of this interaction. Physicochemical property evaluation and computational simulations showed that the designed vaccine construct exhibited favorable properties and elicited higher antibody titers than the six multi-epitope NiV vaccine designs available in the literature. Further in vivo and in vitro experiments are necessary to validate the immunogenicity conferred by the designed vaccine construct and its epitope components. This study demonstrates the capability of computational methodologies in rational vaccine design and highlights the potential of cross-species vaccination strategies for mitigating potential NiV threats.

T Cell Peptide Prediction, Immune Response, and Host-Pathogen Relationship in Vaccinated and Recovered from Mild COVID-19 Subjects

COVID-19 remains a significant threat, particularly to vulnerable populations. The emergence of new variants necessitates the development of treatments and vaccines that induce both humoral and cellular immunity. This study aimed to identify potentially immunogenic SARS-CoV-2 peptides and to explore the intricate host-pathogen interactions involving peripheral immune responses, memory profiles, and various demographic, clinical, and lifestyle factors. Using in silico and experimental methods, we identified several CD8-restricted SARS-CoV-2 peptides that are either poorly studied or have previously unreported immunogenicity: fifteen from the Spike and three each from non-structural proteins Nsp1-2-3-16. A Spike peptide, LA-9, demonstrated a 57% response rate in ELISpot assays using PBMCs from 14 HLA-A*02:01 positive, vaccinated, and mild-COVID-19 recovered subjects, indicating its potential for diagnostics, research, and multi-epitope vaccine platforms. We also found that younger individuals, with fewer vaccine doses and longer intervals since infection, showed lower anti-Spike (ELISA) and anti-Wuhan neutralizing antibodies (pseudovirus assay), higher naïve T cells, and lower central memory, effector memory, and CD4hiCD8low T cells (flow cytometry) compared to older subjects. In our cohort, a higher prevalence of Vδ2-γδ and DN T cells, and fewer naïve CD8 T cells, seemed to correlate with strong cellular and lower anti-NP antibody responses and to associate with Omicron infection, absence of confusional state, and habitual sporting activity.

Mesothelin- and nucleolin-specific T cells from combined short peptides effectively kill triple-negative breast cancer cells

BACKGROUND

Triple-negative breast cancer (TNBC), known for its aggressiveness and limited treatment options, presents a significant challenge. Adoptive cell transfer, involving the ex vivo generation of antigen-specific T cells from peripheral blood mononuclear cells (PBMCs), emerges as a promising approach. The overexpression of mesothelin (MSLN) and nucleolin (NCL) in TNBC samples underscores their potential as targets for T cell therapy. This study explored the efficacy of multi-peptide pulsing of PBMCs to generate MSLN/NCL-specific T cells targeting MSLN+/NCL+ TNBC cells.

METHODS

TNBC patient samples were confirmed for both MSLN and NCL expression via immunohistochemistry. Synthesized MSLN and NCL peptides were combined and administered to activate PBMCs from healthy donors. The cancer-killing ability of the resultant T cells was assessed using crystal violet staining, and their subtypes and cytotoxic cytokines were characterized through flow cytometry and cytokine bead array.

RESULTS

Findings showed that 85.3% (127/149) of TNBC cases were positive for either MSLN or NCL, or both; with single positivity rates for MSLN and NCL of 14.1% and 28.9%, respectively. MSLN and NCL peptides, with high binding affinity for HLA-A*02, were combined and introduced to activated PBMCs from healthy donors. The co-pulsed PBMCs significantly induced TEM and TEMRA CD3+/CD8+ T cells and IFN-γ production, compared to single-peptide pulsed or unpulsed conditions. Notably, MSLN/NCL-specific T cells successfully induced cell death in MSLN+/NCL+ MDA-MB-231 cells, releasing key cytotoxic factors such as perforin, granzymes A and B, Fas ligand, IFN-γ, and granulysin.

CONCLUSIONS

These findings serve as a proof-of-concept for using multiple immunogenic peptides as a novel therapeutic approach in TNBC patients.

Design of a novel EmTSP-3 and EmTIP based multi-epitope vaccine against Echinococcus multilocularis infection

Background

Current treatments and prevention strategies for echinococcosis are inadequate. Recent advancements in molecular vaccine development show promise against Echinococcus granulosus; however, Echinococcus multilocularis remains a challenge. A Multi-epitope Vaccine could potentially induce specific B and T lymphocyte responses, thereby offering protection against Echinococcus multilocularis infection.

Methods

This study aimed to develop a MEV against alveolar echinococcosis. Key epitopes from the Echinococcus multilocularis proteins EmTSP3 and EmTIP were identified using immunoinformatics analyses. These analyses were conducted to assess the MEV feasibility, structural characteristics, molecular docking, molecular dynamics simulations, and immune simulations. The immunogenicity and antigenicity of the vaccine were evaluated through in vitro and in vivo experiments, employing ELISA, Western blotting, FCM, challenge infection experiments, and ELISPOT.

Results

The effective antigenicity and immunogenicity of MEV were demonstrated through immunoinformatics, as well as in vitro and in vivo experiments. In vitro experiments revealed that MEV increased the secretion of IFN-γ and IL-4 in PBMC and successfully bound to specific antibodies in patient serum. Furthermore, mice immunized with MEV developed a robust immune response, characterized by elevated levels of CD4+ and CD8+ T-cells, increased secretion of IFN-γ and IL-4 by specific Th1 and Th2 cells, and heightened serum antibody levels. Importantly, MEV reduced the weight of cysts by conferring resistance against echinococcosis. These findings suggest that MEV is a promising candidate for the prevention of Echinococcus multilocularis infection.

Conclusion

A total of 7 CTL, 7 HTL, 5 linear B-cell, and 2 conformational B-cell epitopes were identified. The vaccine has demonstrated effective antigenicity and immunogenicity against AE through molecular docking, immune simulation, molecular dynamics studies, and both in vitro and in vivo experiments. It provides effective protection against Echinococcus multilocularis infection, thereby laying a foundation for further development.

Development of innovative multi-epitope mRNA vaccine against central nervous system tuberculosis using in silico approaches

Tuberculosis(TB) of the Central nervous system (CNS) is a rare and highly destructive disease. The emergence of drug resistance has increased treatment difficulty, leaving the Bacillus Calmette-Guérin (BCG) vaccine as the only licensed preventative immunization available. This study focused on identifying the epitopes of PknD (Rv0931c) and Rv0986 from Mycobacterium tuberculosis(Mtb) strain H37Rv using an in silico method. The goal was to develop a therapeutic mRNA vaccine for preventing CNS TB. The vaccine was designed to be non-allergenic, non-toxic, and highly antigenic. Codon optimization was performed to ensure effective translation in the human host. Additionally, the secondary and tertiary structures of the vaccine were predicted, and molecular docking with TLR-4 was carried out. A molecular dynamics simulation confirmed the stability of the complex. The results indicate that the vaccine structure shows effectiveness. Overall, the constructed vaccine exhibits ideal physicochemical properties, immune response, and stability, laying a theoretical foundation for future laboratory experiments.

Reverse Vaccinology and Immunoinformatic Approach for Designing a Bivalent Vaccine Candidate Against Hepatitis A and Hepatitis B Viruses

Hepatitis A and B are two crucial viral infections that still dramatically affect public health worldwide. Hepatitis A Virus (HAV) is the main cause of acute hepatitis, whereas Hepatitis B Virus (HBV) leads to the chronic form of the disease, possibly cirrhosis or liver failure. Therefore, vaccination has always been considered the most effective preventive method against pathogens. At this moment, we aimed at the immunoinformatic analysis of HAV-Viral Protein 1 (VP1) as the major capsid protein to come up with the most conserved immunogenic truncated protein to be fused by HBV surface antigen (HBs Ag) to achieve a bivalent vaccine against HAV and HBV using an AAY linker. Various computational approaches were employed to predict highly conserved regions and the most immunogenic B-cell and T-cell epitopes of HAV-VP1 capsid protein in both humans and BALB/c. Moreover, the predicted fusion protein was analyzed regarding primary and secondary structures and also homology validation. Afterward, the three-dimensional structure of vaccine constructs docked with various toll-like receptors (TLR) 2, 4 and 7. According to the bioinformatics tools, the region of 99-259 amino acids of VP1 was selected with high immunogenicity and conserved epitopes. T-cell epitope prediction showed that this region contains 32 antigenic peptides for Human leukocyte antigen (HLA) class I and 20 antigenic peptides in terms of HLA class II which are almost fully conserved in the Iranian population. The vaccine design includes 5 linear and 4 conformational B-cell lymphocyte (BCL) epitopes to induce humoral immune responses. The designed VP1-AAY-HBsAg fusion protein has the potency to be constructed and expressed to achieve a bivalent vaccine candidate, especially in the Iranian population. These findings led us to claim that the designed vaccine candidate provides potential pathways for creating an exploratory vaccine against Hepatitis A and Hepatitis B Viruses with high confidence for the identified strains.

CD4+ and CD8+ T-cell multi-epitope chimeric protein associated with an MPLA adjuvant induce protective efficacy and long-term immunological memory for the immunoprophylaxis of American Tegumentary Leishmaniasis

American Tegumentary Leishmaniasis (ATL) is a disease of high severity and incidence in Brazil, in addition to being a worldwide concern in public health. Leishmania amazonensis is one of the etiological agents of ATL, and the inefficiency of control measures, associated with the high toxicity of the treatment and the lack of effective immunoprophylactic strategies, makes the development of vaccines indispensable and imminent. In this light, the present study proposes to elaborate a chimeric protein (rChiP), based on the fusion of multiple epitopes of CD4+/CD8+ T cells, identified in the immunoproteome of the parasites L. amazonensis and L. braziliensis. The designed chimeric protein was tested in the L. amazonensis murine model of infection using the following formulations: 25 μg of the rChiP in saline (rChiP group) and 25 μg of the rChiP plus 25 μg of MPLA-PHAD® (rChiP+MPLA group). After completing immunization, CD4+ and CD8+ T cells, stimulated with SLa-Antigen or rChiP, showed an increased production of nitric oxide and intracytoplasmic pro-inflammatory cytokines, in addition to the generation of central and effector memory T cells. rChiP and rChiP+MPLA formulations were able to promote an effective protection against L. amazonensis infection determined by a reduction in the development of skin lesions and lower parasitic burden. Reduction in the development of skin lesions and lower parasitic burden in the vaccinated groups were associated with an increase of nitrite, CD4+/CD8+IFN-γ+TNF-α+ and CD4+/CD8+CD44highCD62Lhigh/low T cells, IgGTotal, IgG2a, and lower rates of IgG1 and CD4+/CD8+IL-10+. This data suggests that proposed formulations could be considered potential tools to prevent ATL.

Engineering receptor-binding domain and heptad repeat domains towards the development of multi-epitopes oral vaccines against SARS-CoV-2 variants

The inability of existing vaccines to cope with the mutation rate has highlighted the need for effective preventative strategies for COVID-19. Through the secretion of immunoglobulin A, mucosal delivery of vaccines can effectively stimulate mucosal immunity for better protection against SARS-CoV-2 infection. In this study, various immunoinformatic tools were used to design a multi-epitope oral vaccine against SARS-CoV-2 based on its receptor-binding domain (RBD) and heptad repeat (HR) domains. T and B lymphocyte epitopes were initially predicted from the RBD and HR domains of SARS-CoV-2, and potential antigenic, immunogenic, non-allergenic, and non-toxic epitopes were identified. Epitopes that are highly conserved and have no significant similarity to human proteome were selected. The epitopes were joined with appropriate linkers, and an adjuvant was added to enhance the vaccine efficacy. The vaccine 3D structure constructs were docked with toll-like receptor 4 (TLR-4) and TLR1-TLR2, and the binding affinity was calculated. The designed multi-epitope vaccine construct (MEVC) consisted of 33 antigenic T and B lymphocyte epitopes. The results of molecular dockings and free binding energies confirmed that the MEVC effectively binds to TLR molecules, and the complexes were stable. The results suggested that the designed MEVC is a potentially safe and effective oral vaccine against SARS-CoV-2. This in silico study presents a novel approach for creating an oral multi-epitope vaccine against the rapidly evolving SARS-CoV-2 variants. These findings offer valuable insights for developing an effective strategy to combat COVID-19. Further preclinical and clinical studies are required to confirm the efficacy of the MEVC vaccine.

Bioinformatic elucidation of conserved epitopes to design a potential vaccine candidate against existing and emerging SARS-CoV-2 variants of concern

The COVID-19 pandemic caused by SARS-CoV-2 poses a significant adverse effects on health and economy globally. Due to mutations in genome, COVID-19 vaccine efficacy decreases. We used immuno-informatics to design a Multi epitope vaccine (MEV) candidate for SARS-CoV-2 variants of concern (VOCs). Hence, we predicted binders/epitopes MHC-I, CD8+, MHC-II, CD4+, and CTLs from spike, membrane and envelope proteins of VOCs. In addition, we assessed the conservation of these binders and epitopes across different VOCs. Subsequently, we designed MEV by combining the predicted CTL and CD4+ epitopes from spike protein, peptide linkers, and an adjuvant. Further, we evaluated the binding of MEV candidate against immune receptors namely HLA class I histocompatibility antigen, HLA class II histocompatibility antigen, and TLR4, achieving binding scores of -1265.3, -1330.7, and -1337.9. Molecular dynamics and normal mode analysis revealed stable docking complexes. Moreover, immune simulation suggested MEV candidate elicits both innate and adaptive immune response. We anticipate that this conserved MEV candidate will provide protection from VOCs and emerging strains.

Artificial intelligence-driven reverse vaccinology for Neisseria gonorrhoeae vaccine: Prioritizing epitope-based candidates

Neisseria gonorrhoeae is the causative agent of the sexually transmitted disease gonorrhea. The increasing prevalence of this disease worldwide, the rise of antibiotic-resistant strains, and the difficulties in treatment necessitate the development of a vaccine, highlighting the significance of preventative measures to control and eradicate the infection. Currently, there is no widely available vaccine, partly due to the bacterium's ability to evade natural immunity and the limited research investment in gonorrhea compared to other diseases. To identify distinct vaccine candidates, we chose to focus on the uncharacterized, hypothetical proteins (HPs) as our initial approach. Using the in silico method, we first carried out a comprehensive assessment of hypothetical proteins of Neisseria gonorrhoeae, encompassing assessments of physicochemical properties, cellular localization, secretary pathways, transmembrane regions, antigenicity, toxicity, and prediction of B-cell and T-cell epitopes, among other analyses. Detailed analysis of all HPs resulted in the functional annotation of twenty proteins with a great degree of confidence. Further, using the immuno-informatics approach, the prediction pipeline identified one CD8+ restricted T-cell epitope, seven linear B-cell epitopes, and seven conformational B-cell epitopes as putative epitope-based peptide vaccine candidates which certainly require further validation in laboratory settings. The study accentuates the promise of functional annotation and immuno-informatics in the systematic design of epitope-based peptide vaccines targeting Neisseria gonorrhoeae.

Development of a novel multi-epitope vaccine for brucellosis prevention

Brucellosis, a zoonotic disease caused by Brucella, presents a significant threat to both animal and human health. In animals, the disease can lead to infertility, miscarriage, and high fever, while in humans, symptoms may include recurrent fever, fatigue, sweating, hepatosplenomegaly, and joint and muscle pain following infection. Treatment often involves long-term antibiotic therapy, placing a substantial psychological and financial burden on patients. While vaccination is crucial for prevention, current animal vaccines have drawbacks such as residual virulence, and a safe and effective human vaccine is lacking. Hence, the development of a vaccine for brucellosis is imperative. In this study, we utilized bioinformatics methods to design a multi-epitope vaccine targeting Brucella. Targeting Heme transporter BhuA and polysaccharide export protein, we identified antigenic epitopes, including six cytotoxic T lymphocyte (CTL) dominant epitopes, six helper T lymphocyte (HTL) dominant epitopes, one conformation B cell dominant epitope, and three linear B cell dominant epitopes. By linking these epitopes with appropriate linkers and incorporating a Toll-like receptor (TLR) agonist (human beta-defensin-2) and an auxiliary peptide (Pan HLA-DR epitopes), we constructed the multi-epitope vaccine (MEV). The MEV demonstrated high antigenicity, non-toxicity, non-allergenicity, non-human homology, stability, and solubility. Molecular docking analysis and molecular dynamics simulations confirmed the interaction and stability of the MEV with receptors (MHCI, MHCII, TLR4). Codon optimization and in silico cloning validated the translation efficiency and successful expression of MEV in Escherichia coli. Immunological simulations further demonstrated the efficacy of MEV in inducing robust immune responses. In conclusion, our findings suggest that the engineered MEVs have the potential to stimulate both humoral and cellular immune responses, offering valuable insights for the future development of safe and efficient Brucella vaccines.

Design of a novel multiepitope vaccine with CTLA-4 extracellular domain against Mycoplasma pneumoniae: A vaccine-immunoinformatics approach

BACKGROUND

Community-acquired pneumonia often stems from the macrolide-resistant strain of Mycoplasma pneumoniae, yet no effective vaccine exists against it.

METHODS

This study proposes a vaccine-immunoinformatics strategy for Mycoplasma pneumoniae and other pathogenic microbes. Specifically, dominant B and T cell epitopes of the Mycoplasma pneumoniae P30 adhesion protein were identified through immunoinformatics method. The vaccine sequence was then constructed by coupling with CTLA-4 extracellular region, a novel molecular adjuvant for antigen-presenting cells. Subsequently, the vaccine's physicochemical properties, antigenicity, and allergenicity were verified. Molecular dynamics modeling was employed to confirm interaction with TLR-2, TLR-4, B7-1, and B7-2. Finally, the vaccine underwent in silico cloning for expression.

RESULTS

The vaccine exhibited both antigenicity and non-allergenicity. Molecular dynamics simulation, post-docking with TLR-2, TLR-4, B7-1, and B7-2, demonstrated stable interaction between the vaccine and these molecules. In silico cloning confirmed effective expression of the vaccine gene in insect baculovirus vectors.

CONCLUSION

This vaccine-immunoinformatics approach holds promise for the development of vaccines against Mycoplasma pneumoniae and other pathogenic non-viral and non-bacterial microbes.

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

RNA vaccines for cancer: Principles to practice

Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.

Next-generation IEDB tools: a platform for epitope prediction and analysis

The Next-Generation (NG) IEDB Tools website (https://nextgen-tools.iedb.org) provides users with a redesigned interface to many of the algorithms for epitope prediction and analysis that were originally released on the legacy IEDB Tools website. The initial release focuses on consolidation of all tools related to HLA class I epitopes (MHC binding, elution, immunogenicity, and processing), making all of these predictions accessible from a single application and allowing for their simultaneous execution with minimal user inputs. Additionally, the PEPMatch tool for identifying highly similar epitopes in a set of curated proteomes, as well as a tool for epitope clustering, are available on the site. The NG Tools site allows users to build data pipelines by sending the output of one tool as input for the next. Over the next several years, all pre-existing IEDB Tools, and any newly developed tools, will be integrated into this new site. Here we describe the philosophy behind the redesign and demonstrate the utility and productivity enhancements that are enabled by the new interface.

The receiver operating characteristic curve accurately assesses imbalanced datasets

Many problems in biology require looking for a "needle in a haystack," corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance. The receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) have been reported as ill-suited to evaluate prediction performance on imbalanced problems where there is more interest in performance on the positive minority class, while the precision-recall (PR) curve is preferable. We show via simulation and a real case study that this is a misinterpretation of the difference between the ROC and PR spaces, showing that the ROC curve is robust to class imbalance, while the PR curve is highly sensitive to class imbalance. Furthermore, we show that class imbalance cannot be easily disentangled from classifier performance measured via PR-AUC.

Proteogenomic analysis identifies neoantigens and bacterial peptides as immunotherapy targets in colorectal cancer

Considerable progress has recently been made in cancer immunotherapy, including immune checkpoint blockade, cancer vaccine, and adoptive T cell methods. The lack of effective targets is a major cause of the low immunotherapy response rate in colorectal cancer (CRC). Here, we used a proteogenomic strategy comprising immunopeptidomics, whole exome sequencing, and 16 S ribosomal DNA sequencing analyses of 8 patients with CRC to identify neoantigens and bacterial peptides that can serve as antitumor targets. This study directly identified several personalized neoantigens and bacterial immunopeptides. Immunoassays showed that all neoantigens and 5 of 8 bacterial immunopeptides could be recognized by autologous T cells. Additionally, T cell receptor (TCR) αβ sequencing revealed the TCR repertoire of epitope-reactive CD8+ T cells. Functional studies showed that T cell receptor-T (TCR-T) could be activated by epitope pulsed lymphoblastoid cells. Overall, this study comprehensively profiled the CRC immunopeptidome, revealing several neoantigens and bacterial peptides with potential to serve as immunotherapy targets in CRC.

Polymorphism in F pocket affects peptide selection and stability of type 1 diabetes-associated HLA-B39 allotypes

HLA-B*39:06, HLA-B*39:01, and HLA-B*38:01 are closely related HLA allotypes differentially associated with type 1 diabetes (T1D) risk and progression. B*39:06 is highly predisposing, while B*39:01 and B*38:01 are weakly predisposing and protective allotypes, respectively. Here, we aimed to decipher molecular mechanisms underlying the differential association of these allotypes with T1D pathogenesis. We addressed peptide binding and conformational stability of HLA-B allotypes using computational and experimental approaches. Computationally, we found that B*39:06 and B*39:01 allotypes had more rigid peptide-binding grooves and were more promiscuous in binding peptides than B*38:01. Peptidomes of B*39:06 and B*39:01 contained fewer strong binders and were of lower affinity than that of B*38:01. Experimentally, we demonstrated that B*39:06 and B*39:01 had a higher capacity to bind peptides and exit to the cell surface but lower surface levels and were degraded faster than B*38:01. In summary, we propose that promiscuous B*39:06 and B*39:01 may bind suboptimal peptides and transport them the cell surface, where such unstable complexes may contribute to the pathogenesis of T1D.

Comprehensive analysis of early T cell responses to acute Zika Virus infection during the first epidemic in Bahia, Brazil

BACKGROUND

In most cases, Zika virus (ZIKV) causes a self-limited acute illness in adults, characterized by mild clinical symptoms that resolve within a few days. Immune responses, both innate and adaptive, play a central role in controlling and eliminating virus-infected cells during the early stages of infection.

AIM

To test the hypothesis that circulating T cells exhibit phenotypic and functional activation characteristics during the viremic phase of ZIKV infection.

METHODS

A comprehensive analysis using mass cytometry was performed on peripheral blood mononuclear cells obtained from patients with acute ZIKV infection (as confirmed by RT-PCR) and compared with that from healthy donors (HD). The frequency of IFN-γ-producing T cells in response to peptide pools covering immunogenic regions of structural and nonstructural ZIKV proteins was quantified using an ELISpot assay.

RESULTS

Circulating CD4+ and CD8+ T lymphocytes from ZIKV-infected patients expressed higher levels of IFN-γ and pSTAT-5, as well as cell surface markers associated with proliferation (Ki-67), activation ((HLA-DR, CD38) or exhaustion (PD1 and CTLA-4), compared to those from HD. Activation of CD4+ and CD8+ memory T cell subsets, including Transitional Memory T Cells (TTM), Effector Memory T cells (TEM), and Effector Memory T cells Re-expressing CD45RA (TEMRA), was prominent among CD4+ T cell subset of ZIKV-infected patients and was associated with increased levels of IFN-γ, pSTAT-5, Ki-67, CTLA-4, and PD1, as compared to HD. Additionally, approximately 30% of ZIKV-infected patients exhibited a T cell response primarily directed against the ZIKV NS5 protein.

CONCLUSION

Circulating T lymphocytes spontaneously produce IFN-γ and express elevated levels of pSTAT-5 during the early phase of ZIKV infection whereas recognition of ZIKV antigen results in the generation of virus-specific IFN-γ-producing T cells.

Immunoinformatics approach for designing a multiepitope subunit vaccine against porcine epidemic diarrhea virus genotype IIA spike protein

Background

Porcine epidemic diarrhea (PED), caused by the porcine epidemic diarrhea virus (PEDV), is associated with high mortality and morbidity rates, especially in neonatal pigs. This has resulted in significant economic losses for the pig industry. PEDV genotype II-based vaccines were found to confer better immunity against both heterologous and homologous challenges; specifically, spike (S) proteins, which are known to play a significant role during infection, are ideal for vaccine development.

Aim

This study aims to design a multi-epitope subunit vaccine targeting the S protein of the PEDV GIIa strain using an immunoinformatics approach.

Methods

Various bioinformatics tools were used to predict HTL, CTL, and B-cell epitopes. The epitopes were connected using appropriate linkers and conjugated with the CTB adjuvant and M-ligand. The final multiepitope vaccine construct (fMEVc) was then docked to toll-like receptor 4 (TLR4). The stability of the fMEVc-TLR4 complex was then simulated using GROMACS. C-immsim was then used to predict the in vitro immune response of the fMEVc.

Results

Six epitopes were predicted to induce antibody production, ten epitopes were predicted to induce CTL responses, and four epitopes were predicted to induce HTL responses. The assembled epitopes conjugated with the CTB adjuvant and M-ligand, fMEVc, is antigenic, non-allergenic, stable, and soluble. The construct showed a favorable binding affinity for TLR4, and the protein complex was shown to be stable through molecular dynamics simulations. A robust immune response was induced after immunization, as demonstrated through immune stimulation.

Conclusion

In conclusion, the multi-epitope subunit vaccine construct for PEDV designed in this study exhibits promising antigenicity, stability, and immunogenicity, eliciting robust immune responses and suggesting its potential as a candidate for further vaccine development.

Design and validation of a novel multi-epitopes vaccine against hantavirus

Hantavirus is a member of the order Bunyavirales and an emerging global pathogen. Hantavirus infections have affected millions of people globally based on available epidemiological data and research studies. Hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) are the two main human diseases associated with hantavirus infections. Hence, efforts are required to develop a potent vaccine against the pathogen. The only vaccine that is in use for hantavirus is an inactivated virus vaccine, "Hantavax", but it failed to produce neutralizing antibodies. Vaccine development is of much importance in dealing with the surge of hantavirus globally. In this study, hantavirus five proteins (N protein, G1 and G2, L protein, and non-structural proteins) were used in NetCTL 1.2 program to predict T-cell epitopes. To predict major histocompatibility complex (MHC) binding alleles, an immune epitope database (IEDB) was used. All predicted epitopes were then investigated for different immunoinformatics analyses such as antigenicity and toxicity analyses. The good water-soluble, non-toxic, probable antigenic, and DRB*0101 binder was selected. A multi-epitopes-based vaccine designing was then done where linkers were used to connect the shortlisted epitopes. In addition, an adjuvant molecule was supplementary to the multi-epitopes peptide to improve the vaccine's immunogenic potential. The final vaccine construct's three-dimensional structure was modeled by ab initio method. The vaccine molecule was then evaluated for its binding potential with TLR-3 immune receptor, which is key for its recognition and processing by the host immune system. Docking studies were performed using HADDOCK software. The best-docked complex was selected and visualized for intermolecular binding and interactions using UCSF Chimera 1.16 software. The findings revealed that the designed vaccine might be a potential vaccine against hantavirus and can be used in experimental animal model testings.Communicated by Ramaswamy H. Sarma.

Dual T cell receptor/chimeric antigen receptor engineered NK-92 cells targeting the HPV16 E6 oncoprotein and the tumor-associated antigen L1CAM exhibit enhanced cytotoxicity and specificity against tumor cells

The human papillomavirus type 16 (HPV16) causes a large fraction of genital and oropharyngeal carcinomas. To maintain the transformed state, the tumor cells must continuously synthesize the E6 and E7 viral oncoproteins, which makes them tumor-specific antigens. Indeed, specific T cell responses against them have been well documented and CD8+ T cells engineered to express T cell receptors (TCRs) that recognize epitopes of E6 or E7 have been tested in clinical studies with promising results, yet with limited clinical success. Using CD8+ T cells from peripheral blood of healthy donors, we have identified two novel TCRs reactive to an unexplored E618-26 epitope. These TCRs showed limited standalone cytotoxicity against E618-26-HLA-A*02:01-presenting tumor cells. However, a single-signaling domain chimeric antigen receptor (ssdCAR) targeting L1CAM, a cell adhesion protein frequently overexpressed in HPV16-induced cancer, prompted a synergistic effect that significantly enhanced the cytotoxic capacity of NK-92/CD3/CD8 cells armored with both TCR and ssdCAR when both receptors simultaneously engaged their respective targets, as shown by live microscopy of 2-D and 3-D co-cultures. Thus, virus-specific TCRs from the CD8+ T cell repertoire of healthy donors can be combined with a suitable ssdCAR to enhance the cytotoxic capacity of the effector cells and, indirectly, their specificity.

Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons

Antigen processing is critical for therapeutic vaccines to generate epitopes for priming cytotoxic T cell responses against cancer and pathogens, but insufficient processing often limits the quantity of epitopes released. We address this challenge using machine learning to ascribe a proteasomal degradation score to epitope sequences. Epitopes with varying scores were translocated into cells using nontoxic anthrax proteins. Epitopes with a low score show pronounced immunogenicity due to antigen processing, but epitopes with a high score show limited immunogenicity. This work sheds light on the sequence-activity relationships between proteasomal degradation and epitope immunogenicity. We anticipate that future efforts to incorporate proteasomal degradation signals into vaccine designs will lead to enhanced cytotoxic T cell priming by these vaccines in clinical settings.

Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy

Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from  laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect's vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.

Differences in F pocket impact on HLA I genetic associations with autoimmune diabetes

Introduction

Human leukocyte antigen (HLA) I molecules present antigenic peptides to activate CD8+ T cells. Type 1 Diabetes (T1D) is an auto-immune disease caused by aberrant activation of the CD8+ T cells that destroy insulin-producing pancreatic β cells. Some HLA I alleles were shown to increase the risk of T1D (T1D-predisposing alleles), while some reduce this risk (T1D-protective alleles).

Methods

Here, we compared the T1D-predisposing and T1D-protective allotypes concerning peptide binding, maturation, localization and surface expression and correlated it with their sequences and energetic profiles using experimental and computational methods.

Results

T1D-predisposing allotypes had more peptide-bound forms and higher plasma membrane levels than T1D-protective allotypes. This was related to the fact that position 116 within the F pocket was more conserved and made more optimal contacts with the neighboring residues in T1D-predisposing allotypes than in protective allotypes.

Conclusion

Our work uncovers that specific polymorphisms in HLA I molecules potentially influence their susceptibility to T1D.

Tumor neoantigens derived from RNA editing events show significant clinical relevance in melanoma patients treated with immunotherapy

This study aimed to investigate the clinical significance of RNA editing (RE) and RNA editing derived (RED-) neoantigens in melanoma patients treated with immunotherapy. Vardict and VEP were used to identify the somatic mutations. RE events were identified by Reditools2 and filtered by the custom pipeline. miRTar2GO was implemented to predict the RE whether located in miRNA targets within the 3' UTR region. NetMHCpan and NetCTLpan were used to identify and characterize RED-neoantigens. In total, 7116 RE events were identified, most of which were A-to-I events. Using our custom pipeline, 631 RED-neoantigens were identified that show a significantly greater peptide-MHC affinity, and facilitate epitope processing and presentation than wild-type peptides. The OS of the patients with high RED-neoantigens burden was significantly longer ( P  = 0.035), and a significantly higher RED-neoantigens burden was observed in responders ( P  = 0.048). The area under the curve of the RED-neoantigen was 0.831 of OS. Then, we validated the reliability of RED-neoantigens in predicting the prognosis in an independent cohort and found that patients with high RED-neoantigens exhibited a longer OS ( P  = 0.008). To our knowledge, this is the first study to systematically assess the clinical relevance of RED-neoantigens in melanoma patients treated with immunotherapy.

Self-adjuvant multiepitope nanovaccine based on ferritin induced long-lasting and effective mucosal immunity against H3N2 and H1N1 viruses in mice

The influenza A virus (IAV) is a ubiquitous and continuously evolving respiratory pathogen. The intranasal vaccination mimicking natural infections is an attractive strategy for controlling IAVs. Multiepitope vaccines accurately targeting multiple conserved domains have the potential to broaden the protective scope of current seasonal influenza vaccines and reduce the risk of generating escape mutants. Here, multiple linear epitopes from the matrix protein 2 ectodomain (M2e) and the hemagglutinin stem domain (HA2) are fused with the Helicobacter pylori ferritin, a self-assembled nanocarrier and mucosal adjuvant, to develop a multiepitope nanovaccine. Through intranasal delivery, the prokaryotically expressed multiepitope nanovaccine elicits long-lasting mucosal immunity, broad humoral immunity, and robust cellular immunity without any adjuvants, and confers complete protection against H3N2 and H1N1 subtypes of IAV in mice. Importantly, this intranasal multiepitope nanovaccine triggers memory B-cell responses, resulting in secretory immunoglobulin A (sIgA) and serum immunoglobulin G (IgG) levels persisting for more than five months post-immunization. Therefore, this intranasal ferritin-based multiepitope nanovaccine represents a promising approach to combating respiratory pathogens.

A multi-epitope subunit vaccine based on CU/ZN-SOD, OMP31 and BP26 against Brucella melitensis infection in BALB/C mice

Brucellosis, a zoonosis caused by Brucella, is highly detrimental to both humans and animals. Most existing vaccines are live attenuated vaccines with safety flaws for people and animals. Therefore, it is advantageous to design a multi-epitope subunit vaccine (MEV) to prevent Brucella infection. To this end, we applied a reverse vaccinology approach. Six cytotoxic T cell (CTL) epitopes, seven T helper cell (HTL) epitopes, and four linear B cell epitopes from CU/ZN-SOD, Omp31, and BP26 were obtained. We linked the CTL, HTL, B-cell epitopes, the appropriate CTB molecular adjuvant, and the universal T helper lymphocyte epitope, PADRE, with linkers AAY, GPPGG, and KK, respectively. This yielded a 412-amino acid MEV construct, which we named MEVcob. The immunogenicity, stability, safety, and feasibility of the construct were evaluated by bioinformatics tools (including the AlphaFold2 prediction tool, the AlphaFold2 tool, NetMHC-I pan 4.0 server, IEDB MHC-I server, ABCpred service, and C-ImmSim server); the physicochemical properties, secondary and tertiary structures, and binding ability of MEVocb to toll-like receptor 4 (TLR4) was analyzed. Then, codon adaptation and computer cloning studies were performed. MEVocb is highly immunogenic in immunostimulation experiments, The proteins translated by these sequences were relatively stable, exhibiting a high antigenic index. Furthermore, mouse experiments confirmed that the MEVocb construct could raise IFN-γ, IgG, IgG2a, IgG1, IL-2, TNF-α levels in mice, indicating that induced a specific humoral and cellular immune response in BALB/c mice. This vaccine induced a statistically significant level of protection in BALB/c mice when challenged with Brucella melitensis 043 in Xinjiang. Briefly, we utilized immunoinformatic tools to design a novel multi-epitope subunit candidate vaccine against Brucella. This vaccine aims to induce host immune responses and confer specific protective effects. The study results offer a theoretical foundation for the development of a novel Brucella subunit vaccine.

Immunoinformatics-guided approach for designing a pan-proteome multi-epitope subunit vaccine against African swine fever virus

Despite being identified over a hundred years ago, there is still no commercially available vaccine for the highly contagious and deadly African swine fever virus (ASFV). This study used immunoinformatics for the rapid and inexpensive designing of a safe and effective multi-epitope subunit vaccine for ASFV. A total of 18,858 proteins from 100 well-annotated ASFV proteomes were screened using various computational tools to identify potential epitopes, or peptides capable of triggering an immune response in swine. Proteins from genotypes I and II were prioritized for their involvement in the recent global ASFV outbreaks. The screened epitopes exhibited promising qualities that positioned them as effective components of the ASFV vaccine. They demonstrated antigenicity, immunogenicity, and cytokine-inducing properties indicating their ability to induce potent immune responses. They have strong binding affinities to multiple swine allele receptors suggesting a high likelihood of yielding more amplified responses. Moreover, they were non-allergenic and non-toxic, a crucial prerequisite for ensuring safety and minimizing any potential adverse effects when the vaccine is processed within the host. Integrated with an immunogenic 50S ribosomal protein adjuvant and linkers, the epitopes formed a 364-amino acid multi-epitope subunit vaccine. The ASFV vaccine construct exhibited notable immunogenicity in immune simulation and molecular docking analyses, and stable profiles in secondary and tertiary structure assessments. Moreover, this study designed an optimized codon for efficient translation of the ASFV vaccine construct into the Escherichia coli K-12 expression system using the pET28a(+) vector. Overall, both sequence and structural evaluations suggested the potential of the ASFV vaccine construct as a candidate for controlling and eradicating outbreaks caused by the pathogen.