A platform for the rapid screening of equine immunoglobins F (ab)2 derived from single equine memory B cells able to cross-neutralize to influenza virus.

Single B cells-based antibody platforms offer an effective approach for the discovery of useful antibodies for therapeutic or research purposes. Here we present a method for screening equine immunoglobins F(ab)2, which offers the potential advantage of reacting with multiple epitopes on the virus. Using equine influenza virus (EIV) as model, a hemagglutinin (HA) trimer was constructed to bait B cells in vaccinated horses. We screened 370 HA-specific B cells from 1 × 106 PBMCs and identified a diverse set of equine variable region gene sequences of heavy and light chains and then recombined with humanized Ig Fc. Recombinant equine Ig was then self-assembled in co-transfected 293 T cells, and subsequently optimized to obtain HA binding B-cell receptor (s). The recombinant antibodies exhibited a high binding affinity to the HA protein. Antibody H81 exhibited the highest cross neutralizing activities against EIV strains in vitro. Furthermore, it effectively protected EIV-challenged mice, resulting in significantly improved survival, reduced pulmonary inflammation and decreased viral titers. In silico predication identified a functional region of H81 comprising 27 key amino acids cross the main circulating EIV strains. The 12 amino acid residues in this region with the highest binding affinities were screened. Notably, the predicted epitopes of H81 encompassed the documented equine HA receptor binding site, validating its cross-neutralization. In summary, a rapid platform was successfully established to investigate the profiling of equine antigen-recognizing receptors (BCRs) following infection. This platform has the potential to optimize the screening of virus-neutralizing antibodies and aid in vaccine design.

Immunological Considerations for the Development of an Effective Herpes Vaccine.

Research is underway to develop a vaccine to prevent and cure infection from herpes simplex virus (HSV). It emphasizes the critical need for immunization to address public health issues and the shortcomings of existing treatment options. Furthermore, studies on the HSV vaccine advance the field of immunology and vaccine creation, which may help in the battle against other viral illnesses. The current lack of such a vaccine is, in part, due to herpes viral latency in sensory ganglions. Current vaccines rely on tissue-resident memory CD8+ T cells, which are known to provide protection against subsequent HSV reinfection and reactivation without correlating with other immune subsets. For that reason, there is no effective vaccine that can provide protection against latent or recurrent herpes infection. This review focuses on conventional methods for evaluating the efficacy of a herpes vaccine using differential CD8+ T cells and important unaccounted immune aspects for designing an effective vaccine against herpes.

Conversion of vaccines from low to high immunogenicity by antibodies with epitope complementarity.

Existing antibodies (Abs) have varied effects on humoral immunity during subsequent infections. Here, we leveraged in vivo systems that allow precise control of antigen-specific Abs and B cells to examine the impact of Ab dose, affinity, and specificity in directing B cell activation and differentiation. Abs competing with the B cell receptor (BCR) epitope showed affinity-dependent suppression. By contrast, Abs targeting a complementary epitope, not overlapping with the BCR, shifted B cell differentiation toward Ab-secreting cells. Such Abs allowed for potent germinal center (GC) responses to otherwise poorly immunogenic sites by promoting antigen capture and presentation by low-affinity B cells. These mechanisms jointly diversified the B cell repertoire by facilitating the recruitment of high- and low-affinity B cells into Ab-secreting cell, GC, and memory B cell fates. Incorporation of small amounts of monoclonal Abs into protein- or mRNA-based vaccines enhanced immunogenicity and facilitated sustained immune responses, with implications for vaccine design and our understanding of protective immunity.

Identification and biophysical characterization of epitope atlas of Porcine Reproductive and Respiratory Syndrome Virus.

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) have been a critical threat to swine health since 1987 due to its high mutation rate and substantial economic loss over half a billion dollar in USA. The rapid mutation rate of PRRSV presents a significant challenge in developing an effective vaccine. Even though surveillance and intervention studies have recently (2019) unveiled utilization of PRRSV glycoprotein 5 (GP5; encoded by ORF5 gene) to induce immunogenic reaction and production of neutralizing antibodies in porcine populations, the future viral generations can accrue escape mutations. In this study we identify 63 porcine-PRRSV protein-protein interactions which play primary or ancillary roles in viral entry and infection. Using genome-proteome annotation, protein structure prediction, multiple docking experiments, and binding energy calculations, we identified a list of 75 epitope locations on PRRSV proteins crucial for infection. Additionally, using machine learning-based diffusion model, we designed 56 stable immunogen peptides that contain one or more of these epitopes with their native tertiary structures stabilized through optimized N- and C-terminus flank sequences and interspersed with appropriate linker regions. Our workflow successfully identified numerous known interactions and predicted several novel PRRSV-porcine interactions. By leveraging the structural and sequence insights, this study paves the way for more effective, high-avidity, multi-valent PRRSV vaccines, and leveraging neural networks for immunogen design.

Deciphering the HLA-E immunopeptidome with mass spectrometry: an opportunity for universal mRNA vaccines and T-cell-directed immunotherapies.

Advances in immunotherapy rely on targeting novel cell surface antigens, including therapeutically relevant peptide fragments presented by HLA molecules, collectively known as the actionable immunopeptidome. Although the immunopeptidome of classical HLA molecules is extensively studied, exploration of the peptide repertoire presented by non-classical HLA-E remains limited. Growing evidence suggests that HLA-E molecules present pathogen-derived and tumor-associated peptides to CD8+ T cells, positioning them as promising targets for universal immunotherapies due to their minimal polymorphism. This mini-review highlights recent developments in mass spectrometry (MS) technologies for profiling the HLA-E immunopeptidome in various diseases. We discuss the unique features of HLA-E, its expression patterns, stability, and the potential for identifying new therapeutic targets. Understanding the broad repertoire of actionable peptides presented by HLA-E can lead to innovative treatments for viral and pathogen infections and cancer, leveraging its monomorphic nature for broad therapeutic efficacy.

Intestinal Microbiota and Its Effect on Vaccine-Induced Immune Amplification and Tolerance.

This review provides the potential of intestinal microbiota in vaccine design and application, exploring the current insights into the interplay between the intestinal microbiota and the immune system, with a focus on its intermediary function in vaccine efficacy. It summarizes families and genera of bacteria that are part of the intestinal microbiota that may enhance or diminish vaccine efficacy and discusses the foundational principles of vaccine sequence design and the application of gut microbial characteristics in vaccine development. Future research should further investigate the use of multi-omics technologies to elucidate the interactive mechanisms between intestinal microbiota and vaccine-induced immune responses, aiming to optimize and improve vaccine design.

Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity.

T and B cell activation are equally important in triggering and orchestrating adaptive host responses to design multi-epitope African swine fever virus (ASFV) vaccines. However, few design methods have considered the trade-off between T and B cell immunogenicity when identifying promising ASFV epitopes. This work proposed a novel Pareto front-based ASFV screening method PFAS to identify promising epitopes for designing multi-epitope vaccines utilizing five ASFV Georgia 2007/1 sequences. To accurately predict T cell immunogenicity, four scoring methods were used to estimate the T cell activation in the four stages, including proteasomal cleavage probability, transporter associated with antigen processing transport efficiency, class I binding affinity of the major histocompatibility complex, and CD8 + cytotoxic T cell immunogenicity. PFAS ranked promising epitopes using a Pareto front method considering T and B cell immunogenicity. The coefficient of determination between the Pareto ranks of multi-epitope vaccines and survival days of swine vaccinations was R2 = 0.95. Consequently, PFAS scored complete epitope profiles and identified 72 promising top-ranked epitopes, including 46 CD2v epitopes, two p30 epitopes, 10 p72 epitopes, and 14 pp220 epitopes. PFAS is the first method of using the Pareto front approach to identify promising epitopes that considers the objectives of maximizing both T and B cell immunogenicity. The top-ranked promising epitopes can be cost-effectively validated in vitro. The Pareto front approach can be adaptively applied to various epitope predictors for bacterial, viral and cancer vaccine developments. The MATLAB code of the Pareto front method was available at https://github.com/NYCU-ICLAB/PFAS .

Identification of immunity- and ferroptosis-related signature genes as potential design targets for mRNA vaccines in AML patients.

Immune-associated ferroptosis plays an important role in the progression of acute myeloid leukemia (AML); however, the targets that play key roles in this process are currently unknown. This limits the development of mRNA vaccines based on immune-associated ferroptosis for clinical therapeutic applications. In this study, based on the rich data resources of the TCGA-LAML cohort, we analyzed the tumor mutational burden (TMB), gene mutation status, and associations between immune and ferroptosis genes to reveal the disease characteristics of AML patients. To gain a deeper understanding of differentially expressed genes, we applied the Limma package for differential expression analysis and integrated data sources such as ImmPort Shared Data and FerrDb V2. Moreover, we established gene modules related to TMB according to weighted gene coexpression network analysis (WGCNA) and explored the functions of these modules in AML and their relationships with TMB. We focused on the top 30 most frequent genes through a detailed survey of missense mutations and single nucleotide polymorphisms (SNPs) and selected potentially critical gene targets for subsequent analysis. Based on the expression of these genes, we successfully subgrouped AML patients and found that the subgroups associated with TMB (C1 and C2) exhibited significant differences in survival. The differences in the tumor microenvironment and immune cells between C1 and C2 patients were investigated with the ESTIMATE and MCP-counter algorithms. A predictive model of TMB-related genes (TMBRGs) was constructed, and the validity of the model was demonstrated by categorizing patients into high-risk and low-risk groups. The differences in survival between the high-risk patients and high-TMB patients were further investigated, and potential vaccine targets were identified via immune cell-level analysis. The identification of immunity- and ferroptosis-associated signature genes is an independent predictor of survival in AML patients and provides new information on immunotherapy for AML.

miRNA-Targeted Vaccines: A Promising Approach for Viral Attenuation and Immunogenicity Enhancement.

MicroRNAs (miRNAs) have emerged as a significant tool in the realm of vaccinology, offering novel approaches to vaccine development. This study investigates the potential of miRNAs in the development of advanced vaccines, with an emphasis on how they regulate immune response and control viral replication. We go over the molecular features of miRNAs, such as their capacity to direct post-transcriptional regulation toward mRNAs, hence regulating the expression of genes in diverse tissues and cells. This property is harnessed to develop live attenuated vaccines that are tissue-specific, enhancing safety and immunogenicity. The review highlights recent advancements in using miRNA-targeted vaccines against viruses like influenza, poliovirus, and tick-borne encephalitis virus, demonstrating their attenuated replication in specific tissues while retaining immunogenicity. We also explored the function of miRNAs in the biology of cancer, highlighting their potential to develop cancer vaccines through targeting miRNAs that are overexpressed in tumor cells. The difficulties in developing miRNA vaccines are also covered in this work, including delivery, stability, off-target effects, and the requirement for individualized cancer treatment plans. We wrap off by discussing the potential of miRNA vaccines and highlighting how they will influence the development of vaccination techniques for cancer and infectious diseases in the future.

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.

Exploring computational approaches to design mRNA Vaccine against vaccinia and Mpox viruses.

BACKGROUND: Messenger RNA (mRNA) vaccines emerged as a powerful tool in the fight against infections. Unlike traditional vaccines, this unique type of vaccine elicits robust and persistent innate and humoral immune response with a unique host cell-mediated pathogen gene expression and antigen presentation. METHODS: This offers a novel approach to combat poxviridae infections. From the genome of vaccinia and Mpox viruses, three key genes (E8L, E7R, and H3L) responsible for virus attachment and virulence were selected and employed for designing the candidate mRNA vaccine against vaccinia and Mpox viral infection. Various bioinformatics tools were employed to generate (B cell, CTL, and HTL) epitopes, of which 28 antigenic and immunogenic epitopes were selected and are linked to form the mRNA vaccine construct. Additional components, including a 5' cap, 5' UTR, adjuvant, 3' UTR, and poly(A) tail, were incorporated to enhance stability and effectiveness. Safety measures such as testing for human homology and in silico immune simulations were implemented to avoid autoimmunity and to mimics the immune response of human host to the designed mRNA vaccine, respectively. The mRNA vaccine's binding affinity was evaluated by docking it with TLR-2, TLR-3, TLR-4, and TLR-9 receptors which are subsequently followed by molecular dynamics simulations for the highest binding one to predict the stability of the binding complex. RESULTS: With a 73% population coverage, the mRNA vaccine looks promising, boasting a molecular weight of 198 kDa and a molecular formula of C8901H13609N2431O2611S48 and it is said to be antigenic, nontoxic and nonallergic, making it safe and effective in preventing infections with Mpox and vaccinia viruses, in comparison with other insilico-designed vaccine for vaccinia and Mpox viruses. CONCLUSIONS: However, further validation through in vivo and in vitro techniques is underway to fully assess its potential.

Universal peptide-based potential vaccine design against canine distemper virus (CDV) using a vaccinomic approach.

Canine distemper virus (CDV) affects many domestic and wild animals. Variations among CDV genome linages could lead to vaccination failure. To date, there are several vaccine alternatives, such as a modified live virus and a recombinant vaccine; however, most of these alternatives are based on the ancestral strain Onderstepoort, which has not been circulating for years. Vaccine failures and the need to update vaccines have been widely discussed, and the development of new vaccine candidates is necessary to reduce circulation and mortality. Current vaccination alternatives cannot be used in wildlife animals due to the lack of safety data for most of the species, in addition to the insufficient immune response against circulating strains worldwide in domestic species. Computational tools, including peptide-based therapies, have become essential for developing new-generation vaccines for diverse models. In this work, a peptide-based vaccine candidate with a peptide library derived from CDV H and F protein consensus sequences was constructed employing computational tools. The molecular docking and dynamics of the selected peptides with canine MHC-I and MHC-II and with TLR-2 and TLR-4 were evaluated. In silico safety was assayed through determination of antigenicity, allergenicity, toxicity potential, and homologous canine peptides. Additionally, in vitro safety was also evaluated through cytotoxicity in cell lines and canine peripheral blood mononuclear cells (cPBMCs) and through a hemolysis potential assay using canine red blood cells. A multiepitope CDV polypeptide was constructed, synthetized, and evaluated in silico and in vitro by employing the most promising peptides for comparison with single CDV immunogenic peptides. Our findings suggest that predicting immunogenic CDV peptides derived from most antigenic CDV proteins could aid in the development of new vaccine candidates, such as multiple single CDV peptides and multiepitope CDV polypeptides, that are safe in vitro and optimized in silico. In vivo studies are being conducted to validate potential vaccines that may be effective in preventing CDV infection in domestic and wild animals.

Natural malaria infection elicits rare but potent neutralizing antibodies to the blood-stage antigen RH5.

Plasmodium falciparum reticulocyte-binding protein homolog 5 (RH5) is the most advanced blood-stage malaria vaccine candidate and is being evaluated for efficacy in endemic regions, emphasizing the need to study the underlying antibody response to RH5 during natural infection, which could augment or counteract responses to vaccination. Here, we found that RH5-reactive B cells were rare, and circulating immunoglobulin G (IgG) responses to RH5 were short-lived in malaria-exposed Malian individuals, despite repeated infections over multiple years. RH5-specific monoclonal antibodies isolated from eight malaria-exposed individuals mostly targeted non-neutralizing epitopes, in contrast to antibodies isolated from five RH5-vaccinated, malaria-naive UK individuals. However, MAD8-151 and MAD8-502, isolated from two malaria-exposed Malian individuals, were among the most potent neutralizers out of 186 antibodies from both cohorts and targeted the same epitopes as the most potent vaccine-induced antibodies. These results suggest that natural malaria infection may boost RH5-vaccine-induced responses and provide a clear strategy for the development of next-generation RH5 vaccines.

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.

The application of vaccines is one of the most promising treatments for numerous infectious diseases. However, the design and development of effective vaccines involve huge investments and resources, and only a handful of candidates successfully reach the market. Only relying on traditional methods is both time-consuming and expensive. Various computational tools and software have been developed to accelerate the vaccine design and development. Further, AI-enabled computational tools have revolutionized the field of vaccine design and development by creating predictive models and data-driven decision-making processes. Therefore, information and awareness of these AI-enabled computational resources will immensely facilitate the development of vaccines against emerging pathogens. In this review, we have meticulously summarized the available computational tools for each step of in-silico vaccine design and development, delving into the transformative applications of AI and ML in this domain, which would help to choose appropriate tools for each step during vaccine development, and also highlighting the limitations of these tools to facilitate the selection of appropriate tools for each step of vaccine design.

pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection.

Background: Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies are underway globally. Accurate identification and prioritization of neoantigens is highly relevant to designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address. Results: We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize candidates across three different levels, including variant, transcript and peptide information. Conclusions: pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings The application is available as part of the pVACtools pipeline at pvactools.org and as an online server at pvacview.org.

Leveraging artificial intelligence in vaccine development: A narrative review.

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

Respiratory Syncytial Virus Vaccine Design Using Structure-Based Machine-Learning Models.

When designing live-attenuated respiratory syncytial virus (RSV) vaccine candidates, attenuating mutations can be developed through biologic selection or reverse-genetic manipulation and may include point mutations, codon and gene deletions, and genome rearrangements. Attenuation typically involves the reduction in virus replication, due to direct effects on viral structural and replicative machinery or viral factors that antagonize host defense or cause disease. However, attenuation must balance reduced replication and immunogenic antigen expression. In the present study, we explored a new approach in order to discover attenuating mutations. Specifically, we used protein structure modeling and computational methods to identify amino acid substitutions in the RSV nonstructural protein 1 (NS1) predicted to cause various levels of structural perturbation. Twelve different mutations predicted to alter the NS1 protein structure were introduced into infectious virus and analyzed in cell culture for effects on viral mRNA and protein expression, interferon and cytokine expression, and caspase activation. We found the use of structure-based machine learning to predict amino acid substitutions that reduce the thermodynamic stability of NS1 resulted in various levels of loss of NS1 function, exemplified by effects including reduced multi-cycle viral replication in cells competent for type I interferon, reduced expression of viral mRNAs and proteins, and increased interferon and apoptosis responses.

Strategies of rational and structure-driven vaccine design for Arenaviruses.

The COVID-19 outbreak has highlighted the importance of pandemic preparedness for the prevention of future health crises. One virus family with high pandemic potential are Arenaviruses, which have been detected almost worldwide, particularly in Africa and the Americas. These viruses are highly understudied and many questions regarding their structure, replication and tropism remain unanswered, making the design of an efficacious and molecularly-defined vaccine challenging. We propose that structure-driven computational vaccine design will contribute to overcome these challenges. Computational methods for stabilization of viral glycoproteins or epitope focusing have made progress during the last decades and particularly during the COVID-19 pandemic, and have proven useful for rational vaccine design and the establishment of novel diagnostic tools. In this review, we summarize gaps in our understanding of Arenavirus molecular biology, highlight challenges in vaccine design and discuss how structure-driven and computationally informed strategies will aid in overcoming these obstacles.

Development and application of EpitopeScan, a Python3 toolset for mutation tracking in SARS-CoV-2 immunogenic epitopes.

Introduction: Outbreaks of coronaviruses and especially the recent COVID-19 pandemic emphasize the importance of immunological research in this area to mitigate the effect of future incidents. Bioinformatics approaches are capable of providing multisided insights from virus sequencing data, although currently available software options are not entirely suitable for a specific task of mutation surveillance within immunogenic epitopes of SARS-CoV-2. Method: Here, we describe the development of a mutation tracker, EpitopeScan, a Python3 package with command line and graphical user interface tools facilitating the investigation of the mutation dynamics in SARS-CoV-2 epitopes via analysis of multiple-sequence alignments of genomes over time. We provide an application case by examining three Spike protein-derived immunodominant CD4+ T-cell epitopes restricted by HLA-DRB1*04:01, an allele strongly associated with susceptibility to rheumatoid arthritis (RA). Mutations in these peptides are relevant for immune monitoring of CD4+ T-cell responses against SARS-CoV-2 spike protein in patients with RA. The analysis focused on 2.3 million SARS-CoV-2 genomes sampled in England. Results: We detail cases of epitope conservation over time, partial loss of conservation, and complete divergence from the wild type following the emergence of the N969K Omicron-specific mutation in November 2021. The wild type and the mutated peptide represent potential candidates to monitor variant-specific CD4+ T-cell responses. EpitopeScan is available via GitHub repository https://github.com/Aleksandr-biochem/EpitopeScan.

Recent advances in the characterization of essential genes and development of a database of essential genes.

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.