Immunoinformatics (Amsterdam, Netherlands)最新文献

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCR-epitope prediction benchmark performed on 23 prediction models in the context of the ImmRep 2022 TCR-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as CDR1/2 or V/J information, when available, improves classification obtained with CDR3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark.

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Upon infection, foreign antigenic proteins stimulate the host's immune system to produce antibodies targeting the pathogen. These antibodies bind to regions on the antigen called epitopes. Structural similarity (molecular mimicry) of epitopes between an infecting pathogen and host proteins or other pathogenic proteins the host has previously encountered can impact the host immune response to the pathogen and may lead to cross-reactive antibodies. The ability to identify potential regions of molecular mimicry in a pathogen can illuminate immune effects which are especially important to pathogen treatment and vaccine design. Here we present Epitopedia, a software pipeline that facilitates the identification of regions that may exhibit potential three-dimensional molecular mimicry between an antigenic pathogen protein and known immune epitopes as catalogued by the Immune Epitope Database (IEDB). Epitopedia is open-source software released under the MIT license and is freely available on GitHub, including a Docker container with all other software dependencies preinstalled. We performed an analysis describing how various secondary structure states, identity between pentapeptide pairs, and identity between the parent sequences of pentapeptide pairs affects RMSD. We found that pentapeptides pairs in a helical conformation had considerably lower RMSD values than those in extended or coil conformations. We also found that RMSD is significantly increased when pentapeptide pairs are from non-homologous sequences.

Chimeric antigen receptor (CAR) T-cell based immunotherapy has shown its potential in treating blood cancers, and its application to solid tumors is currently being extensively investigated. For glioma brain tumors, various CAR T-cell targets include IL13R$\alpha$2, EGFRvIII, HER2, EphA2, GD2, B7-H3, and chlorotoxin. In this work, we are interested in developing a mathematical model of IL13R$\alpha$2 targeting CAR T-cells for treating glioma. We focus on extending the work of Kuznetsov et al. (1994) by considering binding of multiple CAR T-cells to a single glioma cell, and the dynamics of these multi-cellular conjugates. Our model more accurately describes experimentally observed CAR T-cell killing assay data than the models which do not consider multi-cellular conjugates. Moreover, we derive conditions in the CAR T-cell expansion rate that determines treatment success or failure. Finally, we show that our model captures distinct CAR T-cell killing dynamics from low to high antigen receptor densities in patient-derived brain tumor cells.

African Swine Fever (ASF) is caused by a highly contagious and fatal hemorrhagic virus, which in 2019 alone in Asia, has killed 8 million pigs with a devastating estimated economic loss amounting to $130 billion. There were attempts to control ASFV transmission; however, developed vaccines failed to produce lasting immunity. Currently, no vaccine has been approved yet. This study designed a novel multi-epitope and multi-antigenic vaccine using open-access bioinformatics tools. B-cell, helper-T and cytotoxic T-cell epitopes were predicted using consensus sequences from ASFV genotypes of antigens p12, p17, p22, p54, p72, and CD2v, and combined with adjuvants and linkers to form the ASF vaccine. Analyses revealed that the ASF vaccine is stable, antigenic, non-allergenic, and not cross-reactive. Docking of SLA-1 to CTL-HTL regions of the developed vaccine revealed that it effectively binds to SLA-1, a vital process in priming an effective immune response. Immune simulations demonstrated that the designed ASF vaccine can elicit primary and secondary immune responses, and stimulate the production of effector immune cells and cytokines. Overall, these results revealed that the designed multi-epitope and multi-antigenic ASF vaccine is potentially effective and warrants further in vitro and in vivo studies to confirm its protective function against ASFV infection.

The Omicron (BA.1/B.1.1.529) variant of SARS-CoV-2 harbors an alarming 37 mutations on its spike protein, reducing the efficacy of current COVID-19 vaccines. In this study, we identified CD8+ and CD4+ T cell epitopes from SARS-CoV-2 S protein mutants. To identify the highest quality CD8 and CD4 epitopes from the Omicron variant, we selected epitopes with a high binding affinity towards both MHC I and MHC II molecules. We applied other clinical checkpoint predictors, including immunogenicity, antigenicity, allergenicity, instability and toxicity. Subsequently, we found eight Omicron (BA.1/B.1.1.529) specific CD8+ and eleven CD4+ T cell epitopes with a world population coverage of 76.16% and 97.46%, respectively. Additionally, we identified common epitopes across Omicron BA.1 and BA.2 lineages that target mutations critical to SARS-CoV-2 virulence. Further, we identified common epitopes across B.1.1.529 and other circulating SARS-CoV-2 variants, such as B.1.617.2 (Delta). We predicted CD8 epitopes’ binding affinity to murine MHC alleles to test the vaccine candidates in preclinical models. The CD8 epitopes were further validated using our previously developed software tool PCOptim. We then modeled the three-dimensional structures of our top CD8 epitopes to investigate the binding interaction between peptide-MHC and peptide-MHC-TCR complexes. Notably, our identified epitopes are targeting the mutations on the RNA-binding domain and the fusion sites of S protein. This could potentially eliminate viral infections and form long-term immune responses compared to relatively short-lived mRNA vaccines and maximize the efficacy of vaccine candidates against the current pandemic and potential future variants.

Perioperative anaphylaxis has a risk of mortality and compromised quality of patient care. It is difficult to design an evaluation system for risk of anaphylaxis using preoperative tests available in clinical practice. To develop a personalized risk forecast platform for general anesthesia-related anaphylaxis, as a first step, we aimed to investigate the feasibility of machine-learning-based classification using clinical features of patients for risk prediction of anesthesia-related anaphylaxis. After data pre-processing, the performance of five classification methods: Logistic Regression Analysis, Support Vector Machine, Random Forest, Linear Discriminant Analysis, and Naïve Bayes), which were integrated with four feature selection methods (Recursive Feature Elimination, Chi-Squared Method, Correlation-based Feature Selection, and Information Gain Ratio), was evaluated using two-layer cross-validation. Seventy-four features, which were defined from 225 participants, were applied for model fitting. Linear Discriminant Analysis in conjunction with Recursive Feature Elimination showed good performance, with accuracy of 0.867 and Matthews correlation coefficient (MCC) of 0.558 with 25 features used in the classification. Logistic Regression in conjunction with Recursive Feature Elimination model also showed adequate performance, with accuracy of 0.858 and MCC of 0.541 with six features used in the classification. This study presents initial proof of the capability of a machine-learning-based strategy for forecasting low-prevalence anesthesia-related anaphylaxis from a clinical perspective. It could provide a basis for establishing an effective risk-scoring and predictive system for perioperative anaphylaxis that would help identify preoperatively whether anaphylaxis will occur and could be used to predict unstable patient states preceding anaphylactic shock.

The short time between the first cases of COVID-19 and the declaration of a pandemic initiated the search for ways to stop the spread of SARS-CoV-2. There are great expectations regarding the development of effective vaccines that protect against all variants, and in the search for it, we hypothesized the obtention of a predicted rational immunogenic peptide from structural components of SARS-CoV-2 might help the vaccine research direction. In the search for a candidate of an immunogenic peptide of the SARS-CoV-2 envelope (E), membrane (M), nucleocapsid (N), or spike (S) proteins, we access the predicted sequences of each protein after the genome sequenced worldwide. We obtained the consensus amino acid sequences of about 14,441 sequences of each protein of each continent and the worldwide consensus sequence. For epitope identification and characterization from each consensus structural protein related to MHC-I or MHC-II interaction and B-cell receptor recognition, we used the IEDB reaching 68 epitopes to E, 174 to M, 245 to N, and 833 to S proteins. To select an epitope with the highest probability of binding to the MHC or BCR, all epitopes of each consensus sequence were aligned. The curation indicated 1, 4, 8, and 21 selected epitopes for E, M, N, and S proteins, respectively. Those epitopes were tested in silico for antigenicity obtaining 16 antigenic epitopes. Physicochemical properties and allergenicity evaluation of the obtained epitopes were done. Ranking the results, we obtained one epitope of each protein except for the S protein that presented two epitopes after the selection. To check the 3D position of each selected epitope in the protein structure, we used molecular homology modeling. Afterward, each selected epitope was evaluated by molecular docking to reference MHC-I or MHC-II allelic protein sequences. Taken together, the results obtained in this study showed a rational search for a putative immunogenic peptide of SARS-CoV-2 structural proteins that can improve vaccine development using in silico approaches. The epitopes selected represent the most conserved sequence of new coronavirus and may be used in a variety of vaccine development strategies since they are also presented in the described variants of SARS-CoV-2.

Combined oncolytic virotherapy and immunotherapy (immunovirotherapy) protocols represent a promising treatment strategy for a range of cancers and offer many advantages over conventional anti-cancer therapies. Nevertheless, there are considerable challenges for this therapeutic modality, and clinical treatment failures remain prevalent. Determining which combination regimens to investigate given the burgeoning number of virotherapy and immunotherapy derivatives remains a tremendous challenge for the field. Fortunately, mathematical modelling is well placed to assist in identifying optimal combination regimens and improving these treatments. However, translation of modelling predictions to actionable changes is severely lacking. Here, two mathematicians and two experimentalists discuss their respective viewpoints concerning the current state of immunovirotherapy, the challenges facing this promising field and how contributions from this modelling and experimental research can be better integrated in the future. By initiating this dialogue, we arrive at the conclusion that the translational process can be improved by first conducting extensive mathematical investigations using relevant data before proceeding to pre-clinical and finally clinical trials. By exploiting mathematical approaches such as virtual clinical trials, we may be able to limit the number of virotherapy and immunotherapy combinations that should be tested clinically. Overall, the current integration of efforts by modellers and experimentalists is insufficient to support major translational advances in this field, and it is only with cross-disciplinary efforts that immunovirotherapy can be a robustly effective cancer treatment.