NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

In silico study to explore the mechanism of Toxoplasma-induced inflammation and target therapy based on sero and salivary Toxoplasma

We aimed to assess salivary and seroprevalence of Toxoplasma immunoglobulins in risky populations and evaluate drug docking targeting TgERP. A cross-sectional study was conducted in Alexandria University hospitals' outpatient clinics. 192 participants were enrolled from September 2022 to November 2023. Anti-Toxoplasma IgG and IgM were determined in serum and saliva by ELISA. An in-Silico study examined TgERP's protein-protein interactions (PPIs) with pro-inflammatory cytokine receptors, anti-inflammatory cytokine, cell cycle progression regulatory proteins, a proliferation marker, and nuclear envelope integrity-related protein Lamin B1. Our findings revealed that anti-T. gondii IgG were detected in serum (66.1%) and saliva (54.7%), with 2.1% of both samples were positive for IgM. Salivary IgG had 75.59% sensitivity, 86.15% specificity, 91.40% PPV, 64.40% NPP, 79.17% accuracy and fair agreement with serum IgG. On the other hand, the sensitivity, specificity, PPV, NPV, and accuracy in detecting salivary IgM were 75.0%, 99.47%, 75.0%, 99.47%, and 98.96%. AUC 0.859 indicates good discriminatory power. Examined synthetic drugs and natural products can target specific amino acids residues of TgERP that lie at the same binding interface with LB1 and Ki67, subsequently, hindering their interaction. Hence, salivary samples can be a promising diagnostic approach. The studied drugs can counteract the pro-inflammatory action of TgERP.

Nonconserved epitopes dominate reverse preexisting T cell immunity in COVID-19 convalescents

The herd immunity against SARS-CoV-2 is continuously consolidated across the world during the ongoing pandemic. However, the potential function of the nonconserved epitopes in the reverse preexisting cross-reactivity induced by SARS-CoV-2 to other human coronaviruses is not well explored. In our research, we assessed T cell responses to both conserved and nonconserved peptides shared by SARS-CoV-2 and SARS-CoV, identifying cross-reactive CD8+ T cell epitopes using enzyme-linked immunospot and intracellular cytokine staining assays. Then, in vitro refolding and circular dichroism were performed to evaluate the thermal stability of the HLA/peptide complexes. Lastly, single-cell T cell receptor reservoir was analyzed based on tetramer staining. Here, we discovered that cross-reactive T cells targeting SARS-CoV were present in individuals who had recovered from COVID-19, and identified SARS-CoV-2 CD8+ T cell epitopes spanning the major structural antigens. T cell responses induced by the nonconserved peptides between SARS-CoV-2 and SARS-CoV were higher and played a dominant role in the cross-reactivity in COVID-19 convalescents. Cross-T cell reactivity was also observed within the identified series of CD8+ T cell epitopes. For representative immunodominant peptide pairs, although the HLA binding capacities for peptides from SARS-CoV-2 and SARS-CoV were similar, the TCR repertoires recognizing these peptides were distinct. Our results could provide beneficial information for the development of peptide-based universal vaccines against coronaviruses.

Computational design and investigation of the monomeric spike SARS-CoV-2-ferritin nanocage vaccine stability and interactions

Since the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) outbreak, several solutions have been proposed to manage the disease. The most viable option for controlling this virus is to produce effective vaccines. Most of the current SARS-CoV-2 vaccines have focused on the infusion spike protein. Spike exists as a trimer and plays a vital role in infecting host cells by binding to the Angiotensin-Converting Enzyme 2 (ACE2) receptor through its Receptor Binding Domain (RBD). Ferritin protein, a naturally occurring iron-storage protein, has gained attention for vaccine production due to its self-assembling property, non-toxic nature, and biocompatibility. Ferritin nanocages have recently been employed in the development of a SARS-CoV-2 vaccination eliciting not only long-term protective memory cells but also a sustained antibody response. In this study, a combination of in silico investigations including molecular docking, molecular dynamics simulations, and immune simulations were carried out to computationally model the monomeric spike protein on the ferritin nanocage as well as to evaluate its stability and interactions for the first time. The structural dynamics of the modeled complex demonstrated noticeable stability. In particular, the Receptor Binding Domain (RBD) and ferritin within the monomeric spike-ferritin complex illustrated significant stability. The lack of alterations in the secondary structure further supported the overall steadiness of the complex. The decline in the distance between ferritin and spike suggests a strong interaction over time. The cross-correlation matrices revealed that the monomeric spike and ferritin move towards each other supporting the stable interaction between spike and ferritin. Further, the orientation of monomeric spike protein within the ferritin unit facilitated the exposure of critical epitopes, specifically upward active Receptor Binding Domain (RBD), enabling effective interactions with the ACE2 receptor. The immune simulations of the model indicated high-level stimulations of both cellular and humoral immunity in the human body. It was also found that the employed model is effective regardless of the mutated spikes in different variants. These findings shed light on the current status of the SARS-CoV-2-ferritin nanoparticle vaccines and could be used as a framework for other similar vaccine designs.

TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes

The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.

Identification of nonsense-mediated decay inhibitors that alter the tumor immune landscape

Despite exciting developments in cancer immunotherapy, its broad application is limited by the paucity of targetable antigens on the tumor cell surface. As an intrinsic cellular pathway, nonsense-mediated decay (NMD) conceals neoantigens through the destruction of the RNA products from genes harboring truncating mutations. We developed and conducted a high throughput screen, based on the ratiometric analysis of transcripts, to identify critical mediators of NMD. This screen implicated disruption of kinase SMG1's phosphorylation of UPF1 as a potential disruptor of NMD. This led us to design a novel SMG1 inhibitor, KVS0001, that elevates the expression of transcripts and proteins resulting from truncating mutations in vivo and in vitro . Most importantly, KVS0001 concomitantly increased the presentation of immune-targetable HLA class I-associated peptides from NMD-downregulated proteins on the surface of cancer cells. KVS0001 provides new opportunities for studying NMD and the diseases in which NMD plays a role, including cancer and inherited diseases.

One Sentence Summary

Disruption of the nonsense-mediated decay pathway with a newly developed SMG1 inhibitor with in-vivo activity increases the expression of T-cell targetable cancer neoantigens resulting from truncating mutations.

Epitope screening and self-assembled nanovaccine molecule design of PDCoV-S protein based on immunoinformatics

Based on the whole virus or spike protein of pigs, δ coronavirus (PDCoV) as an immunogen may have unrelated antigenic epitope interference. Therefore, it is essential for screening and identifying advantageous protective antigen epitopes. In addition, immunoinformatic tools are described as an important aid in determining protective antigenic epitopes. In this study, the primary, secondary, and tertiary structures of vaccines were measured using ExPASy, PSIPRED 4.0, and trRosetta servers. Meanwhile, the molecular docking analysis and vector of the candidate nanovaccine were constructed. The immune response of the candidate vaccine was simulated and predicted using the C-ImmSim server. This experiment screened B cell epitopes with strong immunogenicity and high conservation, CTL epitopes, and Th epitopes with IFN-γ and IL-4 positive spike proteins. Ferritin is used as a self-assembled nanoparticle element for designing candidate nanovaccine. After analysis, it has been found to be soluble, stable, non-allergenic, and has a high affinity for its target receptor, TLR-3. The preliminary simulation analysis results show that the candidate nanovaccine has the ability to induce a humoral and cellular immune response. Therefore, it may provide a new theoretical basis for research on coronavirus self-assembled nanovaccines. It may be an effective candidate vaccine for controlling and preventing PDCoV.

The Neo-Open Reading Frame Peptides That Comprise the Tumor Framome Are a Rich Source of Neoantigens for Cancer Immunotherapy

Identification of immunogenic cancer neoantigens as targets for therapy is challenging. Here, we integrate the whole-genome and long-read transcript sequencing of cancers to identify the collection of neo-open reading frame peptides (NOP) expressed in tumors. We termed this collection of NOPs the tumor framome. NOPs represent tumor-specific peptides that are different from wild-type proteins and may be strongly immunogenic. We describe a class of hidden NOPs that derive from structural genomic variants involving an upstream protein coding gene driving expression and translation of noncoding regions of the genome downstream of a rearrangement breakpoint, i.e., where no gene annotation or evidence for transcription exists. The entire collection of NOPs represents a vast number of possible neoantigens particularly in tumors with many structural genomic variants and a low number of missense mutations. We show that NOPs are immunogenic and epitopes derived from NOPs can bind to MHC class I molecules. Finally, we provide evidence for the presence of memory T cells specific for hidden NOPs in peripheral blood from a patient with lung cancer. This work highlights NOPs as a major source of possible neoantigens for personalized cancer immunotherapy and provides a rationale for analyzing the complete cancer genome and transcriptome as a basis for the detection of NOPs.

Anthrax Vaccination, Gulf War Illness, and Human Leukocyte Antigen (HLA)

We report on a highly significant, positive association between anthrax vaccination and occurrence of Gulf War Illness (GWI) in 111 Gulf War veterans (42 with GWI and 69 controls). GWI was diagnosed in 47.1% of vaccinated veterans but only in 17.2% of non-vaccinated veterans (Pearson χ2 = 7.08, p = 0.008; odds ratio = 3.947; relative risk = 2.617), with 1.6x higher GWI symptom severity in vaccinated veterans (p = 0.007, F-test in analysis of covariance). Next, we tested the hypothesis that the susceptibility to GWI following anthrax vaccination could be due to inability to make antibodies against the anthrax protective antigen (PA), the key protein contained in the vaccine. Since the first step in initiating antibody production would be the binding of PA peptide fragments (typically 15-amino acid long [15-mer]) to peptide-binding motifs of human leukocyte antigen (HLA) Class II molecules, we assessed the binding-motif affinities of such HLA specific molecules to all linear 15-mer peptide fragments of the anthrax PA. We identified a total of 58 HLA Class II alleles carried by the veterans in our sample and found that, of those, 18 (31%) were present in the vaccinated group that did not develop GWI but were absent from the vaccinated group who developed GWI. Remarkably, in silico analyses revealed very high binding affinities of peptide-binding motifs of those 18 HLA alleles with fragments of anthrax vaccine PA, leading to the successful production of anti-PA antibodies. Conversely, the absence of these protective HLA alleles points to a reduced ability to develop antibodies against PA, thus resulting in harmful PA persistence and development of GWI.

Spatial Architecture of Myeloid and T Cells Orchestrates Immune Evasion and Clinical Outcome in Lung Cancer

Understanding the role of the tumor microenvironment (TME) in lung cancer is critical to improving patient outcomes. We identified four histology-independent archetype TMEs in treatment-naïve early-stage lung cancer using imaging mass cytometry in the TRACERx study (n = 81 patients/198 samples/2.3 million cells). In immune-hot adenocarcinomas, spatial niches of T cells and macrophages increased with clonal neoantigen burden, whereas such an increase was observed for niches of plasma and B cells in immune-excluded squamous cell carcinomas (LUSC). Immune-low TMEs were associated with fibroblast barriers to immune infiltration. The fourth archetype, characterized by sparse lymphocytes and high tumor-associated neutrophil (TAN) infiltration, had tumor cells spatially separated from vasculature and exhibited low spatial intratumor heterogeneity. TAN-high LUSC had frequent PIK3CA mutations. TAN-high tumors harbored recently expanded and metastasis-seeding subclones and had a shorter disease-free survival independent of stage. These findings delineate genomic, immune, and physical barriers to immune surveillance and implicate neutrophil-rich TMEs in metastasis.

SIGNIFICANCE

This study provides novel insights into the spatial organization of the lung cancer TME in the context of tumor immunogenicity, tumor heterogeneity, and cancer evolution. Pairing the tumor evolutionary history with the spatially resolved TME suggests mechanistic hypotheses for tumor progression and metastasis with implications for patient outcome and treatment. This article is featured in Selected Articles from This Issue, p. 897.

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.

Epistasis Between HLA-DRB1*16:02:01 and SLC16A11 T-C-G-T-T Reduces Odds for Type 2 Diabetes in Southwest American Indians

We sought to identify genetic/immunologic contributors of type 2 diabetes (T2D) in an indigenous American community by genotyping all study participants for both high-resolution HLA-DRB1 alleles and SLC16A11 to test their risk and/or protection for T2D. These genes were selected based on independent reports that HLA-DRB1*16:02:01 is protective for T2D and that SLC16A11 associates with T2D in individuals with BMI <35 kg/m2. Here, we test the interaction of the two loci with a more complete data set and perform a BMI sensitivity test. We defined the risk protection haplotype of SLC16A11, T-C-G-T-T, as allele 2 of a diallelic genetic model with three genotypes, SLC16A11*11, *12, and *22, where allele 1 is the wild type. Both earlier findings were confirmed. Together in the same logistic model with BMI ≥35 kg/m2, DRB1*16:02:01 remains protective (odds ratio [OR] 0.73), while SLC16A11 switches from risk to protection (OR 0.57 [*22] and 0.78 [*12]); an added interaction term was statistically significant (OR 0.49 [*12]). Bootstrapped (b = 10,000) statistical power of interaction, 0.4801, yielded a mean OR of 0.43. Sensitivity analysis demonstrated that the interaction is significant in the BMI range of 30-41 kg/m2. To investigate the epistasis, we used the primary function of the HLA-DRB1 molecule, peptide binding and presentation, to search the entire array of 15-mer peptides for both the wild-type and ancient human SLC16A11 molecules for a pattern of strong binding that was associated with risk and protection for T2D. Applying computer binding algorithms suggested that the core peptide at SLC16A11 D127G, FSAFASGLL, might be key for moderating risk for T2D with potential implications for type 1 diabetes.

ARTICLE HIGHLIGHTS

NF9 peptide specific cytotoxic T lymphocyte clone cross react to Y453F mutation of SARS-CoV-2 virus spike protein

The recognition by cytotoxic T cells (CTLs) is essential for the clearance of SARS-CoV-2 virus-infected cells. Several viral proteins have been described to be recognized by CTLs. Among them, the spike (S) protein is one of the immunogenic proteins. The S protein acts as a ligand for its receptors, and several mutants with different affinities for its cognate receptors have been reported, and certain mutations in the S protein, such as L452R and Y453F, have been found to inhibit the HLA-A24-restricted CTL response. In this study, we conducted a screening of candidate peptides derived from the S protein, specifically targeting those carrying the HLA-A24 binding motif. Among these peptides, we discovered that NF9 (NYNYLYRLF) represents an immunogenic epitope. CTL clones specific to the NF9 peptide were successfully established. These CTL clones exhibited the ability to recognize endogenously expressed NF9 peptide. Interestingly, the CTL clone demonstrated cross-reactivity with the Y453F peptide (NYNYLFRLF) but not with the L452R peptide (NYNYRYRLF). The CTL clone was able to identify the endogenously expressed Y453F mutant peptide. These findings imply that the NF9-specific CTL clone possesses the capability to recognize and respond to the Y453F mutant peptide.

Genome-wide association analysis reveals the associations of NPHP4, TYW1-AUTS2 and SEMA6D for Behçet's disease and HLA-B*46:01 for its intestinal involvement

BACKGROUND

Intestinal involvement in Behçet's disease (BD) is associated with poor prognosis and is more prevalent in East Asian than in Mediterranean populations. Identifying the genetic causes of intestinal BD is important for understanding the pathogenesis and for appropriate treatment of BD patients.

METHODS

We performed genome-wide association studies (GWAS) and imputation/replication genotyping of human leukocyte antigen (HLA) alleles for 1,689 Korean and Turkish patients with BD (including 379 patients with intestinal BD) and 2,327 healthy controls, followed by replication using 593 Japanese patients with BD (101 patients with intestinal BD) and 737 healthy controls. Stratified cross-phenotype analyses were performed for 1) overall BD, 2) intestinal BD, and 3) intestinal BD without association of overall BD.

RESULTS

We identified three novel genome-wide significant susceptibility loci including NPHP4 (rs74566205; P=1.36 × 10-8), TYW1-AUTS2 (rs60021986; P=1.14 × 10-9), and SEMA6D (rs4143322; P=5.54 × 10-9) for overall BD, and a new association with HLA-B*46:01 for intestinal BD (P=1.67 × 10-8) but not for BD without intestinal involvement. HLA peptide binding analysis revealed that Mycobacterial peptides, have a stronger binding affinity to HLA-B*46:01 compared to the known risk allele HLA-B*51:01.

CONCLUSIONS

HLA-B*46:01 is associated with the development of intestinal BD; NPHP4, TYW1-AUTS2, and SEMA6D are susceptibility loci for overall BD.

Ancient genomes reveal insights into ritual life at Chichén Itzá

The ancient city of Chichén Itzá in Yucatán, Mexico, was one of the largest and most influential Maya settlements during the Late and Terminal Classic periods (AD 600-1000) and it remains one of the most intensively studied archaeological sites in Mesoamerica1-4. However, many questions about the social and cultural use of its ceremonial spaces, as well as its population's genetic ties to other Mesoamerican groups, remain unanswered2. Here we present genome-wide data obtained from 64 subadult individuals dating to around AD 500-900 that were found in a subterranean mass burial near the Sacred Cenote (sinkhole) in the ceremonial centre of Chichén Itzá. Genetic analyses showed that all analysed individuals were male and several individuals were closely related, including two pairs of monozygotic twins. Twins feature prominently in Mayan and broader Mesoamerican mythology, where they embody qualities of duality among deities and heroes5, but until now they had not been identified in ancient Mayan mortuary contexts. Genetic comparison to present-day people in the region shows genetic continuity with the ancient inhabitants of Chichén Itzá, except at certain genetic loci related to human immunity, including the human leukocyte antigen complex, suggesting signals of adaptation due to infectious diseases introduced to the region during the colonial period.

Experimental Data on PIRCHE and T-Cell Reactivity: HLA-DPB1-Derived Peptides Identified by PIRCHE-I Show Binding to HLA-A*02:01 in vitro and T-Cell Activation in vivo

Introduction

Human leukocyte antigen (HLA)-DPB1 mismatches during hematopoietic stem cell transplantation (HSCT) with an unrelated donor result in an increased risk for the development of graft-versus-host disease (GvHD). The number of CD8+ T-cell epitopes available for indirect allorecognition as predicted by the PIRCHE algorithm has been shown to be associated with GvHD development. As a proof of principle, PIRCHE-I predictions for HLA-DPB1 mismatches were validated in vitro and in vivo.

Methods

PIRCHE-I analysis was performed to identify HLA-DPB1-derived peptides that could theoretically bind to HLA-A*02:01. PIRCHE-I predictions for HLA-DPB1 mismatches were validated in vitro by investigating binding affinities of HLA-DPB1-derived peptides to the HLA-A*02:01 in a competition-based binding assay. To investigate the capacity of HLA-DPB1-derived peptides to elicit a T-cell response in vivo, mice were immunized with these peptides. T-cell alloreactivity was subsequently evaluated using an interferon-gamma ELISpot assay.

Results

The PIRCHE-I algorithm identified five HLA-DPB1-derived peptides (RMCRHNYEL, YIYNREEFV, YIYNREELV, YIYNREEYA, and YIYNRQEYA) to be presented by HLA-A*02:01. Binding of these peptides to HLA-A*02:01 was confirmed in a competition-based peptide binding assay, all showing an IC50 value of 21 μm or lower. The peptides elicited an interferon-gamma response in vivo.

Conclusion

Our results indicate that the PIRCHE-I algorithm can identify potential immunogenic HLA-DPB1-derived peptides present in recipients of an HLA-DPB1-mismatched donor. These combined in vitro and in vivo observations strengthen the validity of the PIRCHE-I algorithm to identify HLA-DPB1 mismatch-related GvHD development upon HSCT.

Novel Cancer Prevention Strategies in Individuals With Hereditary Cancer Syndromes: Focus on BRCA1, BRCA2, and Lynch Syndrome

Germline pathogenic variants (PVs) in the BRCA1 and BRCA2 genes confer elevated risks of breast, ovarian, and other cancers. Lynch syndrome (LS) is associated with increased risks of multiple cancer types including colorectal and uterine cancers. Current cancer risk mitigation strategies have focused on pharmacologic risk reduction, enhanced surveillance, and preventive surgeries. While these approaches can be effective, they stand to be improved on because of either limited efficacy or undesirable impact on quality of life. The current review summarizes ongoing investigational efforts in cancer risk prevention strategies for patients with germline PVs in BRCA1, BRCA2, or LS-associated genes. These efforts span radiation, surgery, and pharmacology including vaccine strategies. Understanding the molecular events involved in the premalignant to malignant transformation in high-risk individuals may ultimately contribute significantly to novel prevention strategies.

Transformers meets neoantigen detection: a systematic literature review

Cancer immunology offers a new alternative to traditional cancer treatments, such as radiotherapy and chemotherapy. One notable alternative is the development of personalized vaccines based on cancer neoantigens. Moreover, Transformers are considered a revolutionary development in artificial intelligence with a significant impact on natural language processing (NLP) tasks and have been utilized in proteomics studies in recent years. In this context, we conducted a systematic literature review to investigate how Transformers are applied in each stage of the neoantigen detection process. Additionally, we mapped current pipelines and examined the results of clinical trials involving cancer vaccines.

The identification of effective tumor-suppressing neoantigens using a tumor-reactive TIL TCR-pMHC ternary complex

Neoantigens are ideal targets for cancer immunotherapy because they are expressed de novo in tumor tissue but not in healthy tissue and are therefore recognized as foreign by the immune system. Advances in next-generation sequencing and bioinformatics technologies have enabled the quick identification and prediction of tumor-specific neoantigens; however, only a small fraction of predicted neoantigens are immunogenic. To improve the predictability of immunogenic neoantigens, we developed the in silico neoantigen prediction workflows VACINUSpMHC and VACINUSTCR: VACINUSpMHC incorporates physical binding between peptides and MHCs (pMHCs), and VACINUSTCR integrates T cell reactivity to the pMHC complex through deep learning-based pairing with T cell receptors (TCRs) of putative tumor-reactive CD8 tumor-infiltrating lymphocytes (TILs). We then validated our neoantigen prediction workflows both in vitro and in vivo in patients with hepatocellular carcinoma (HCC) and in a B16F10 mouse melanoma model. The predictive abilities of VACINUSpMHC and VACINUSTCR were confirmed in a validation cohort of 8 patients with HCC. Of a total of 118 neoantigen candidates predicted by VACINUSpMHC, 48 peptides were ultimately selected using VACINUSTCR. In vitro validation revealed that among the 48 predicted neoantigen candidates, 13 peptides were immunogenic. Assessment of the antitumor efficacy of the candidate neoepitopes using a VACINUSTCR in vivo mouse model suggested that vaccination with the predicted neoepitopes induced neoantigen-specific T cell responses and enabled the trafficking of neoantigen-specific CD8 + T cell clones into the tumor tissue, leading to tumor suppression. This study showed that the prediction of immunogenic neoantigens can be improved by integrating a tumor-reactive TIL TCR-pMHC ternary complex.

Allelic loss of HLA class I facilitates evasion from immune surveillance in cervical intraepithelial neoplasia

Loss of heterozygosity (LOH) has been reported to occur in HLA regions in cervical intraepithelial neoplasia (CIN) and cervical cancer. However, the details of how this is related to the progression of CIN have been unclear. In this study, we examined the human papillomavirus (HPV) antigen-presenting capacity of people with CIN and the significance of LOH of HLA class I in the progression of CIN. It was shown that differences in antigen-presenting capacity among each case depended on HLA types, not HPV genotypes. Focusing on the HLA type, there was a positive correlation between antigen-presenting capacity against HPV and the frequency of allelic loss. Furthermore, the lost HLA-B alleles had a higher HPV antigen-presenting capacity than intact alleles. In addition, frequency of LOH of HLA class I was significantly higher in advanced CIN (CIN2-3) than in cervicitis or early-stage CIN (CIN1): around half of CIN2-3 had LOH of any HLA class I. Moreover, the antigen-presenting capacity against E5, which is the HPV proteins that facilitate viral escape from this immune surveillance by suppressing HLA class I expression, had the most significant impact on the LOH in HLA-B. This study suggests that HPV evades immune surveillance mechanisms when host cells lose the capacity for antigen presentation by HLA class I molecules, resulting in long-term infection and progression to advanced lesions.

Diversity and antigenic potentials of Mycoplasmopsis bovis secreted and outer membrane proteins within a core genome of strains isolated from North American bison and cattle

Mycoplasmopsis bovis is a worldwide economically important pathogen of cattle that can cause or indirectly contribute to bovine respiratory disease. M. bovis is also a primary etiological agent of respiratory disease in bison with high mortality rates. A major challenge in the development of an efficacious M. bovis vaccine is the design of antigens that contain both MHC-1 and MHC-2 T-cell epitopes, and that account for population level diversity within the species. Publicly available genomes and sequence read archive libraries of 381 M. bovis strains isolated from cattle (n = 202) and bison (n = 179) in North America were used to identify a core genome of 575 genes, including 38 that encode either known or predicted secreted or outer membrane proteins. The antigenic potentials of the proteins were characterized by the presence and strength of their T-cell epitopes, and their protein variant diversity at the population-level. The proteins had surprisingly low diversity and varying predictive levels of T-cell antigenicity. These results provide a reference for the selection or design of antigens for vaccine testing against strains infecting North American cattle and bison.

Conservation of HLA Spike Protein Epitopes Supports T Cell Cross-Protection in SARS-CoV-2 Vaccinated Individuals against the Potentially Zoonotic Coronavirus Khosta-2

Heterologous vaccines, which induce immunity against several related pathogens, can be a very useful and rapid way to deal with new pandemics. In this study, the potential impact of licensed COVID-19 vaccines on cytotoxic and helper cell immune responses against Khosta-2, a novel sarbecovirus that productively infects human cells, was analyzed for the 567 and 41 most common HLA class I and II alleles, respectively. Computational predictions indicated that most of these 608 alleles, covering more than 90% of the human population, contain sufficient fully conserved T-cell epitopes between the Khosta-2 and SARS-CoV-2 spike-in proteins. Ninety percent of these fully conserved peptides for class I and 93% for class II HLA molecules were verified as epitopes recognized by CD8+ or CD4+ T lymphocytes, respectively. These results show a very high correlation between bioinformatic prediction and experimental assays, which strongly validates this study. This immunoinformatics analysis allowed a broader assessment of the alleles that recognize these peptides, a global approach at the population level that is not possible with experimental assays. In summary, these findings suggest that both cytotoxic and helper cell immune protection elicited by currently licensed COVID-19 vaccines should be effective against Khosta-2 virus infection. Finally, by being rapidly adaptable to future coronavirus pandemics, this study has potential public health implications.

Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity

Tumors with the same diagnosis can have different molecular profiles and response to treatment. It remains unclear when and why these differences arise. Somatic genomic aberrations occur within the context of a highly variable germline genome. Interrogating 5870 breast cancer lesions, we demonstrated that germline-derived epitopes in recurrently amplified genes influence somatic evolution by mediating immunoediting. Individuals with a high germline-epitope burden in human epidermal growth factor receptor 2 (HER2/ERBB2) are less likely to develop HER2-positive breast cancer compared with other subtypes. The same holds true for recurrent amplicons defining three aggressive estrogen receptor (ER)-positive subgroups. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an "immune cold" phenotype. These data show that the germline genome plays a role in dictating somatic evolution.

A novel approach to design chimeric multi epitope vaccine against Leishmania exploiting infected host cell proteome

Leishmaniasis is a major infectious disease having high mortality which could be attributed to lack of a suitable vaccine candidate. We propose a novel approach to design multiepitope vaccine to leishmaniasis exploiting specific membrane proteome from infected macrophage from host. The MHC-I, MHC-II and BC epitopes predicted for unique proteins from the infected macrophages and Leishmania and a MEV designed in various combinations (1a-1m). The epitope arrangements 1a, 1k, 1l, and 1 m showed a strong antigenicity profile and immune response. The molecular dynamics simulation indicate the 1k, 1l, and 1 m constructs have strong affinity toward TLR-2, TLR-3, and TLR-4. Overall the structural and immunogenicity profile suggests 1k is top candidate. Further, a computational model system with TLR-2, TLR-3, TLR-4, BCR, MHC-I and MHC-II was generated for 1k construct to understand the MEV interactions with immune components. Dihedral distribution and distance was enumerated to understand the movement of immune components towards 1k. The results indicate 1k has strong affinity for the immune response molecules especially TLR-3, BCR and MHC-II are coming in close contact with the MEV through the simulation. The study suggests that designed multi-epitope vaccine 1k has potential to induce proper immune response but warrants further studies.

Immunogenicity of Non-Mutated Ovarian Cancer-Specific Antigens

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

Limited Immunogenicity of an HLA-A*03:01-restricted Epitope of Erv-k-env in Non-hiv-1 Settings: Implications for Adoptive Cell Therapy in Cancer

Repetitive elements (REs) are often expressed at higher levels in tumor cells than normal cells, implicating these genomic regions as an untapped pool of tumor-associated antigens. In ovarian cancer (OC), protein from the RE ERV-K is frequently expressed by tumor cells. Here we determined whether the targeting of a previously identified immunogenic epitope in the envelope gene (env) of ERV-K resulted in target antigen specificity in non-HIV-1 settings. We found that transducing healthy donor T cells with an ERV-K-Env-specific T cell receptor construct resulted in antigen specificity only when co-cultured with HLA-A*03:01 B lymphoblastoid cells. Furthermore, these transduced T cells were not specific for HLA-A*03:01 + OC cells nor for the cognate peptide in HLA-matched systems from multiple healthy donors. These data suggest that the ERV-K-Env epitope recognized by this T cell receptor is of low immunogenicity and has limited potential as a T cell target for OC.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from cerebrospinal fluid (CSF) and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass offset searches.

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

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

Dual role of the peptide-loading complex as proofreader and limiter of MHC-I presentation

Antigen presentation via major histocompatibility complex class I (MHC-I) molecules is essential for surveillance by the adaptive immune system. Central to this process is the peptide-loading complex (PLC), which translocates peptides from the cytosol to the endoplasmic reticulum and catalyzes peptide loading and proofreading of peptide-MHC-I (pMHC-I) complexes. Despite its importance, the impact of individual PLC components on the presented pMHC-I complexes is still insufficiently understood. Here, we used stoichiometrically defined antibody-nanobody complexes and engineered soluble T cell receptors (sTCRs) to quantify different MHC-I allomorphs and defined pMHC-I complexes, respectively. Thereby, we uncovered distinct effects of individual PLC components on the pMHC-I surface pool. Knockouts of components of the PLC editing modules, namely tapasin, ERp57, or calreticulin, changed the MHC-I surface composition to a reduced proportion of HLA-A*02:01 presentation compensated by a higher ratio of HLA-B*40:01 molecules. Intriguingly, these knockouts not only increased the presentation of suboptimally loaded HLA-A*02:01 complexes but also elevated the presentation of high-affinity peptides overexpressed in the cytosol. Our findings suggest that the components of the PLC editing module serve a dual role, acting not only as peptide proofreaders but also as limiters for abundant peptides. This dual function ensures the presentation of a broad spectrum of antigenic peptides.

Loss of p14 diminishes immunogenicity in melanoma via non-canonical Wnt signaling by reducing the peptide surface density

Immunotherapy has achieved tremendous success in melanoma. However, only around 50% of advanced melanoma patients benefit from immunotherapy. Cyclin-dependent kinase inhibitor 2A (CDKN2A), encoding the two tumor-suppressor proteins p14ARF and p16INK4a, belongs to the most frequently inactivated gene loci in melanoma and leads to decreased T cell infiltration. While the role of p16INK4a has been extensively investigated, knowledge about p14ARF in melanoma is scarce. In this study, we elucidate the impact of reduced p14ARF expression on melanoma immunogenicity. Knockdown of p14ARF in melanoma cell lines diminished their recognition and killing by melanoma differentiation antigen (MDA)-specific T cells. Resistance was caused by a reduction of the peptide surface density of presented MDAs. Immunopeptidomic analyses revealed that antigen presentation via human leukocyte antigen class I (HLA-I) molecules was enhanced upon p14ARF downregulation in general, but absolute and relative expression of cognate peptides was decreased. However, this phenotype is associated with a favorable outcome for melanoma patients. Limiting Wnt5a signaling reverted this phenotype, suggesting an involvement of non-canonical Wnt signaling. Taken together, our data indicate a new mechanism limiting MDA-specific T cell responses by decreasing both absolute and relative MDA-peptide presentation in melanoma.

Dose escalation study of a personalized peptide-based neoantigen vaccine (EVX-01) in patients with metastatic melanoma

BACKGROUND

Neoantigens can serve as targets for T cell-mediated antitumor immunity via personalized neopeptide vaccines. Interim data from our clinical study NCT03715985 showed that the personalized peptide-based neoantigen vaccine EVX-01, formulated in the liposomal adjuvant, CAF09b, was safe and able to elicit EVX-01-specific T cell responses in patients with metastatic melanoma. Here, we present results from the dose-escalation part of the study, evaluating the feasibility, safety, efficacy, and immunogenicity of EVX-01 in addition to anti-PD-1 therapy.

METHODS

Patients with metastatic melanoma on anti-PD-1 therapy were treated in three cohorts with increasing vaccine dosages (twofold and fourfold). Tumor-derived neoantigens were selected by the AI platform PIONEER and used in personalized therapeutic cancer peptide vaccines EVX-01. Vaccines were administered at 2-week intervals for a total of three intraperitoneal and three intramuscular injections. The study's primary endpoint was safety and tolerability. Additional endpoints were immunological responses, survival, and objective response rates.

RESULTS

Compared with the base dose level previously reported, no new vaccine-related serious adverse events were observed during dose escalation of EVX-01 in combination with an anti-PD-1 agent given according to local guidelines. Two patients at the third dose level (fourfold dose) developed grade 3 toxicity, most likely related to pembrolizumab. Overall, 8 out of the 12 patients had objective clinical responses (6 partial response (PR) and 2 CR), with all 4 patients at the highest dose level having a CR (1 CR, 3 PR). EVX-01 induced peptide-specific CD4+ and/or CD8+T cell responses in all treated patients, with CD4+T cells as the dominating responses. The magnitude of immune responses measured by IFN-γ ELISpot assay correlated with individual peptide doses. A significant correlation between the PIONEER quality score and induced T cell immunogenicity was detected, while better CRs correlated with both the number of immunogenic EVX-01 peptides and the PIONEER quality score.

CONCLUSION

Immunization with EVX-01-CAF09b in addition to anti-PD-1 therapy was shown to be safe and well tolerated and elicit vaccine neoantigen-specific CD4+and CD8+ T cell responses at all dose levels. In addition, objective tumor responses were observed in 67% of patients. The results encourage further assessment of the antitumor efficacy of EVX-01 in combination with anti-PD-1 therapy.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

Human insulin as both antigen and protector in type 1 diabetes

Type 1 diabetes (T1D) is characterized by T-cell responses to islet antigens. Investigations in humans and the nonobese diabetic (NOD) mouse model of T1D have revealed that T-cell reactivity to insulin plays a central role in the autoimmune response. As there is no convenient NOD-based model to study human insulin (hIns) or its T-cell epitopes in the context of spontaneous T1D, we developed a NOD mouse strain transgenically expressing hIns in islets under the control of the human regulatory region. Female NOD.hIns mice developed T1D at approximately the same rate and overall incidence as NOD mice. Islet-infiltrating T cells from NOD.hIns mice recognized hIns peptides; both CD8 and CD4 T-cell epitopes were identified. We also demonstrate that islet-infiltrating T cells from HLA-transgenic NOD.hIns mice can be used to identify potentially patient-relevant hIns T-cell epitopes. Besides serving as an antigen, hIns was expressed in the thymus of NOD.hIns mice and could serve as a protector against T1D under certain circumstances, as previously suggested by genetic studies in humans. NOD.hIns mice and related strains facilitate human-relevant epitope discovery efforts and the investigation of fundamental questions that cannot be readily addressed in humans.

Prospects for the computational humanization of antibodies and nanobodies

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the 'humanness' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.

Hotspot DNA Methyltransferase 3A (DNMT3A) and Isocitrate Dehydrogenase 1 and 2 (IDH1/2) Mutations in Acute Myeloid Leukemia and Their Relevance as Targets for Immunotherapy

DNA methyltransferase 3A (DNMT3A) and isocitrate dehydrogenase 1 and 2 (IDH1/2) are genes involved in epigenetic regulation, each mutated in 7-23% of patients with acute myeloid leukemia. Here, we investigated whether hotspot mutations in these genes encode neoantigens that can be targeted by immunotherapy. Five human B-lymphoblastoid cell lines expressing common HLA class I alleles were transduced with a minigene construct containing mutations that often occur in DNMT3A or IDH1/2. From these minigene-transduced cell lines, peptides were eluted from HLA class I alleles and analyzed using tandem mass spectrometry. The resulting data are available via ProteomeXchange under the identifier PXD050560. Mass spectrometry revealed an HLA-A*01:01-binding DNMT3AR882H peptide and an HLA-B*07:02-binding IDH2R140Q peptide as potential neoantigens. For these neopeptides, peptide-HLA tetramers were produced to search for specific T-cells in healthy individuals. Various T-cell clones were isolated showing specific reactivity against cell lines transduced with full-length DNMT3AR882H or IDH2R140Q genes, while cell lines transduced with wildtype genes were not recognized. One T-cell clone for DNMT3AR882H also reacted against patient-derived acute myeloid leukemia cells with the mutation, while patient samples without the mutation were not recognized, thereby validating the surface presentation of a DNMT3AR882H neoantigen that can potentially be targeted in acute myeloid leukemia via immunotherapy.

An immunoinformatic approach for developing a multi-epitope subunit vaccine against Monkeypox virus

An in-silico approach was implemented to develop a multi-epitope subunit vaccine construct against the recent outbreak of the Monkeypox virus. The contribution of 10 different antigenic proteins based on their antigenicity led to the selection of 10 HTL, 9 CTL, and 6 BCL epitopes. The construct was further investigated for its allergenicity, antigenicity, and physio-chemical properties using servers such as AllerTOP and Allergen FP, VaxiJen and ANTIGENPro, and ProtParam respectively. The secondary structure of the vaccine was predicted using the SOPMA server followed by I-TASSER for the 3D structure. After refinement and validation of structural stability of the modelled vaccine, a molecular docking assay was implemented to study the interaction of the known TLR4 receptor with that of the constructed vaccine using the ClusPro server. The docked vaccine and TLR4 receptor were studied using the molecular dynamics (MD) simulation to validate the stability of the complex. After codon optimization the cDNA was constructed and in-silico cloning of the vaccine construct was carried out. The vaccine was also subjected to computational immune assay which predicted a powerful immune response against the Monkeypox virus validating that the developed multi-epitope vaccine construct can be a potent vaccine candidate.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00220-5.

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.

FASTMAP-a flexible and scalable immunopeptidomics pipeline for HLA- and antigen-specific T-cell epitope mapping based on artificial antigen-presenting cells

The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world's population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population

MOTIVATION

Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity.

RESULTS

Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally validated neoantigens over ranking-based approaches in a study of seven patients.

AVAILABILITY AND IMPLEMENTATION

The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.

Expanding the repertoire reveals recurrent, cryptic, and hematopoietic HLA class I minor histocompatibility antigens

ABSTRACT

Allogeneic stem cell transplantation (alloSCT) is a curative treatment for hematological malignancies. After HLA-matched alloSCT, antitumor immunity is caused by donor T cells recognizing polymorphic peptides, designated minor histocompatibility antigens (MiHAs), that are presented by HLA on malignant patient cells. However, T cells often target MiHAs on healthy nonhematopoietic tissues of patients, thereby inducing side effects known as graft-versus-host disease. Here, we aimed to identify the dominant repertoire of HLA-I-restricted MiHAs to enable strategies to predict, monitor or modulate immune responses after alloSCT. To systematically identify novel MiHAs by genome-wide association screening, T-cell clones were isolated from 39 transplanted patients and tested for reactivity against 191 Epstein-Barr virus transformed B cell lines of the 1000 Genomes Project. By discovering 81 new MiHAs, we more than doubled the antigen repertoire to 159 MiHAs and demonstrated that, despite many genetic differences between patients and donors, often the same MiHAs are targeted in multiple patients. Furthermore, we showed that one quarter of the antigens are cryptic, that is translated from unconventional open reading frames, for example long noncoding RNAs, showing that these antigen types are relevant targets in natural immune responses. Finally, using single cell RNA-seq data, we analyzed tissue expression of MiHA-encoding genes to explore their potential role in clinical outcome, and characterized 11 new hematopoietic-restricted MiHAs as potential targets for immunotherapy. In conclusion, we expanded the repertoire of HLA-I-restricted MiHAs and identified recurrent, cryptic and hematopoietic-restricted antigens, which are fundamental to predict, follow or manipulate immune responses to improve clinical outcome after alloSCT.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

Genomic Landscape of Lynch Syndrome Colorectal Neoplasia Identifies Shared Mutated Neoantigens for Immunoprevention

BACKGROUND & AIMS

Lynch syndrome (LS) carriers develop mismatch repair-deficient neoplasia with high neoantigen (neoAg) rates. No detailed information on targetable neoAgs from LS precancers exists, which is crucial for vaccine development and immune-interception strategies. We report a focused somatic mutation and frameshift-neoAg landscape of microsatellite loci from colorectal polyps without malignant potential (PWOMP), precancers, and early-stage cancers in LS carriers.

METHODS

We generated paired whole-exome and transcriptomic sequencing data from 8 colorectal PWOMP, 41 precancers, 8 advanced precancers, and 12 early-stage cancers of 43 LS carriers. A computational pipeline was developed to predict, rank, and prioritize the top 100 detected mutated neoAgs that were validated in vitro using ELISpot and tetramer assays.

RESULTS

Mutation calling revealed >10 mut/Mb in 83% of cancers, 63% of advanced precancers, and 20% of precancers. Cancers displayed an average of 616 MHC-I neoAgs/sample, 294 in advanced precancers, and 107 in precancers. No neoAgs were detected in PWOMP. A total of 65% of our top 100 predicted neoAgs were immunogenic in vitro, and were present in 92% of cancers, 50% of advanced precancers, and 29% of precancers. We observed increased levels of naïve CD8+ and memory CD4+ T cells in mismatch repair-deficient cancers and precancers via transcriptomics analysis.

CONCLUSIONS

Shared frameshift-neoAgs are generated within unstable microsatellite loci at initial stages of LS carcinogenesis and can induce T-cell responses, generating opportunities for vaccine development, targeting LS precancers and early-stage cancers.

The phosphatase inhibitor LB-100 creates neoantigens in colon cancer cells through perturbation of mRNA splicing

Perturbation of protein phosphorylation represents an attractive approach to cancer treatment. Besides kinase inhibitors, protein phosphatase inhibitors have been shown to have anti-cancer activity. A prime example is the small molecule LB-100, an inhibitor of protein phosphatases 2A/5 (PP2A/PP5), enzymes that affect cellular physiology. LB-100 has proven effective in pre-clinical models in combination with immunotherapy, but the molecular underpinnings of this synergy remain understood poorly. We report here a sensitivity of the mRNA splicing machinery to phosphorylation changes in response to LB-100 in colorectal adenocarcinoma. We observe enrichment for differentially phosphorylated sites within cancer-critical splicing nodes of U2 snRNP, SRSF and hnRNP proteins. Altered phosphorylation endows LB-100-treated colorectal adenocarcinoma cells with differential splicing patterns. In PP2A-inhibited cells, over 1000 events of exon skipping and intron retention affect regulators of genomic integrity. Finally, we show that LB-100-evoked alternative splicing leads to neoantigens that are presented by MHC class 1 at the cell surface. Our findings provide a potential explanation for the pre-clinical and clinical observations that LB-100 sensitizes cancer cells to immune checkpoint blockade.

In silico design of high-affinity antigenic peptides for HLA-B44

Cancer cell-killing by CD8+ T cells demands effective tumor antigen presentation by human leukocyte antigen class I (HLA-I) molecules. Screening and designing highly immunogenic neoantigens require quantitative computations to reliably predict HLA-peptide binding affinities. Here, with all-atom molecular dynamics (MD) simulations and free energy perturbation (FEP) methods, we design a collection of antigenic peptide candidates through in silico mutagenesis studies on immunogenic neoantigens, yielding enhanced binding affinities to HLA-B*44:02. In-depth structural dissection shows that introducing positively charged residues such as arginine to position 6 or lysine to position 7 of the candidates triggers conformational shifts in both peptides and the antigen-binding groove of the HLA, following the "induced-fit" mechanism. Enhancement in binding affinities compared to the wild-type was found in three out of five mutated candidates. The HLA pocket, capable of accommodating positively charged residues in positions from 5 to 7, is designated as the "dynamic pocket". Taken together, we showcase an effective structure-based binding affinity optimization framework for antigenic peptides of HLA-B*44:02 and underscore the importance of dynamic nature of the antigen-binding groove in concert with the anchoring motifs. This work provides structural insights for rational design of favorable HLA-peptide bindings and future developments in neoantigen-based therapeutics.

Computational antigenic insights into the novel NADC-34-like Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) isolate YC-2020

In this computational study, we advanced the understanding of the antigenic properties of the NADC-34-like isolate of the Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), named YC-2020, relevant in veterinary pathology. We utilized sequence comparison analyses of the M and N proteins, comparing them with those of NADC34, identifying substantial amino acid homology that allowed us to highlight conserved epitopes and crucial variants. Through the application of Clustal Omega for multiple sequence alignment and platforms like Vaxijen and AllerTOP for predicting antigenic and allergenic potential, our analyses revealed important insights into the conservation and variation of epitopes essential for the development of effective diagnostic tools and vaccines. Our findings, aligned with initial experimental studies, underscore the importance of these epitopes in the development of targeted immunodiagnostic platforms and significantly contribute to the management and control of PRRSV. However, further studies are required to validate the computational predictions of antigenicity for this new viral isolate. This approach underscores the potential of computational models to enable ongoing monitoring and control of PRRSV evolution in swine. While this study provides valuable insights into the antigenic properties of the novel PRRSV isolate YC-2020 through computational analysis, it is important to acknowledge the limitations inherent to in silico predictions, specifically, the absence of laboratory validation.

In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches

Infection with the hepatitis C virus (HCV) is one of the causes of liver cancer, which is the world's sixth most prevalent and third most lethal cancer. The current treatments do not prevent reinfection; because they are expensive, their usage is limited to developed nations. Therefore, a prophylactic vaccine is essential to control this virus. Hence, in this study, an immunoinformatics method was applied to design a multi-epitope vaccine against HCV. The best B- and T-cell epitopes from conserved regions of the E2 protein of seven HCV genotypes were joined with the appropriate linkers to design a multi-epitope vaccine. In addition, cholera enterotoxin subunit B (CtxB) was included as an adjuvant in the vaccine construct. This study is the first to present this epitopes-adjuvant combination. The vaccine had acceptable physicochemical characteristics. The vaccine's 3D structure was predicted and validated. The vaccine's binding stability with Toll-like receptor 2 (TLR2) and TLR4 was confirmed using molecular docking and molecular dynamics (MD) simulation. The immune simulation revealed the vaccine's efficacy by increasing the population of B and T cells in response to vaccination. In silico expression in Escherichia coli (E. coli) was also successful.

Genome-Wide Association Study Identifies IFIH1 and HLA-DQB1*05:02 Loci Associated With Anti-NMDAR Encephalitis

BACKGROUND AND OBJECTIVES

Anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is a rare autoimmune neurologic disorder, the genetic etiology of which remains poorly understood. Our study aims to investigate the genetic basis of this disease in the Chinese Han population.

METHODS

We performed a genome-wide association study and fine-mapping study within the major histocompatibility complex (MHC) region of 413 Chinese patients with anti-NMDAR encephalitis recruited from 6 large tertiary hospitals and 7,127 healthy controls.

RESULTS

Our genome-wide association analysis identified a strong association at the IFIH1 locus on chromosome 2q24.2 (rs3747517, p = 1.06 × 10-8, OR = 1.55, 95% CI, 1.34-1.80), outside of the human leukocyte antigen (HLA) region. Furthermore, through a fine-mapping study of the MHC region, we discovered associations for 3 specific HLA class I and II alleles. Notably, HLA-DQB1*05:02 (p = 1.43 × 10-12; OR, 2.10; 95% CI 1.70-2.59) demonstrates the strongest association among classical HLA alleles, closely followed by HLA-A*11:01 (p = 4.36 × 10-7; OR, 1.52; 95% CI 1.29-1.79) and HLA-A*02:07 (p = 1.28 × 10-8; OR, 1.87; 95% CI 1.50-2.31). In addition, we uncovered 2 main HLA amino acid variation associated with anti-NMDAR encephalitis including HLA-DQβ1-126H (p = 1.43 × 10-12; OR, 2.10; 95% CI 1.70-2.59), exhibiting a predisposing effect, and HLA-B-97R (p = 3.40 × 10-8; OR, 0.63; 95% CI 0.53-0.74), conferring a protective effect. Computational docking analysis suggested a close relationship between the NR1 subunit of NMDAR and DQB1*05:02.

DISCUSSION

Our findings indicate that genetic variation in IFIH1, involved in the type I interferon signaling pathway and innate immunity, along with variations in the HLA class I and class II genes, has substantial implications for the susceptibility to anti-NMDAR encephalitis in the Chinese Han population.

Design and computational evaluation of a novel multi-epitope hybrid vaccine against monkeypox virus: Potential targets and immunogenicity assessment for pandemic preparedness

Monkeypox is a type of DNA-enveloped virus that belongs to the orthopoxvirus family, closely related to the smallpox virus. It can cause an infectious disease in humans known as monkeypox disease. Although there are multiple drugs and vaccines designed to combat orthopoxvirus infections, with a primary focus on smallpox, the recent spread of the monkeypox virus to over 50 countries have ignited a mounting global concern. This unchecked viral proliferation has raised apprehensions about the potential for a pandemic corresponding to the catastrophic impact of COVID-19. This investigation explored the structural proteins of monkeypox virus as potential candidates for designing a novel hybrid multi-epitope vaccine. The epitopes obtained from the selected proteins were screened to ensure their non-allergenicity, non-toxicity, and antigenicity to trigger T and B-cell responses. The interaction of the vaccine with toll-like receptor-3 (TLR-3) and major histocompatibility complexes (MHCs) was assessed using Cluspro 2.0. To establish the reliability of the docked complexes, a comprehensive evaluation was conducted using Immune and MD Simulations and Normal Mode Analysis. However, to validate the computational results of this study, additional in-vitro and in-vivo research is essential.

A Structural In Silico Analysis of the Immunogenicity of L-Asparaginase from Penicillium cerradense

L-asparaginase is an essential drug used to treat acute lymphoid leukemia (ALL), a cancer of high prevalence in children. Several adverse reactions associated with L-asparaginase have been observed, mainly caused by immunogenicity and allergenicity. Some strategies have been adopted, such as searching for new microorganisms that produce the enzyme and applying protein engineering. Therefore, this work aimed to elucidate the molecular structure and predict the immunogenic profile of L-asparaginase from Penicillium cerradense, recently revealed as a new fungus of the genus Penicillium and producer of the enzyme, as a motivation to search for alternatives to bacterial L-asparaginase. In the evolutionary relationship, L-asparaginase from P. cerradense closely matches Aspergillus species. Using in silico tools, we characterized the enzyme as a protein fragment of 378 amino acids (39 kDa), including a signal peptide containing 17 amino acids, and the isoelectric point at 5.13. The oligomeric state was predicted to be a homotetramer. Also, this L-asparaginase presented a similar immunogenicity response (T- and B-cell epitopes) compared to Escherichia coli and Dickeya chrysanthemi enzymes. These results suggest a potentially useful L-asparaginase, with insights that can drive strategies to improve enzyme production.