The heavy burden imposed by the COVID-19 pandemic on our society triggered the race toward the development of therapies or preventive strategies. Among these, antibodies and vaccines are particularly attractive because of their high specificity, low probability of drug-drug interaction, and potentially long-standing protective effects. While the threat at hand justifies the pace of research, the implementation of therapeutic strategies cannot be exempted from safety considerations. There are several potential adverse events reported after the vaccination or antibody therapy, but two are of utmost importance: antibody-dependent enhancement (ADE) and cytokine storm syndrome (CSS). On the other hand, the depletion or exhaustion of T-cells has been reported to be associated with worse prognosis in COVID-19 patients. This observation suggests a potential role of vaccines eliciting cellular immunity, which might simultaneously limit the risk of ADE and CSS. Such risk was proposed to be associated with FcR-induced activation of proinflammatory macrophages (M1) by Fu et al. (2020) and Iwasaki and Yang (2020). All aspects of the newly developed vaccine (including the route of administration, delivery system, and adjuvant selection) may affect its effectiveness and safety. In this work we use a novel in silico approach (based on AI and bioinformatics methods) developed to support the design of epitope-based vaccines. We evaluated the capabilities of our method for predicting the immunogenicity of epitopes. Next, the results of our approach were compared with other vaccine-design strategies reported in the literature. The risk of immuno-toxicity was also assessed. The analysis of epitope conservation among other Coronaviridae was carried out in order to facilitate the selection of peptides shared across different SARS-CoV-2 strains and which might be conserved in emerging zootic coronavirus strains. Finally, the potential applicability of the selected epitopes for the development of a vaccine eliciting cellular immunity for COVID-19 was discussed, highlighting the benefits and challenges of such an approach.

The heavy burden imposed by the COVID-19 pandemic on our society triggered the race towards the development of therapies or preventive strategies. Among these, antibodies and vaccines are particularly attractive because of their high specificity, low probability of drug-drug interaction, and potentially long-standing protective effects. While the threat at hand justifies the pace of research, the implementation of therapeutic strategies cannot be exempted from safety considerations. There are several potential adverse events reported after the vaccination or antibody therapy, but two are of utmost importance: antibody-dependent enhancement (ADE) and cytokine storm syndrome (CSS). On the other hand, the depletion or exhaustion of T-cells has been reported to be associated with worse prognosis in COVID-19 patients. This observation suggests a potential role of vaccines eliciting cellular immunity, which might simultaneously limit the risk of ADE and CSS. Such risk was proposed to be associated with FcR-induced activation of proinflammatory macrophages (M1) by Fu et al. 2020 and Iwasaki et al. 2020. All aspects of the newly developed vaccine (including the route of administration, delivery system, and adjuvant selection) may affect its effectiveness and safety. In this work we use a novel in silico approach (based on AI and bioinformatics methods) developed to support the design of epitope-based vaccines. We evaluated the capabilities of our method for predicting the immunogenicity of epitopes. Next, the results of our approach were compared with other vaccine-design strategies reported in the literature. The risk of immuno-toxicity was also assessed. The analysis of epitope conservation among other Coronaviridae was carried out in order to facilitate the selection of peptides shared across different SARS-CoV-2 strains and which might be conserved in emerging zootic coronavirus strains. Finally, the potential applicability of the selected epitopes for the development of a vaccine eliciting cellular immunity for COVID-19 was discussed, highlighting the benefits and challenges of such an approach.

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Introduction: The purpose of this study is to present a novel method for neoepitope prediction, along with extensive benchmarks to existing solutions and identification of the most predictive constituent biological features. The accurate prediction of neoepitope immunogenicity represents an invaluable tool for the design of personalized cancer vaccines with effective treatment outcomes. The effectiveness of the host’s adaptive immune response against cancer relies on the correct HLA-mediated neoepitope presentation and the recognition by specific CD8+ clones. Cancer immunotherapies act by boosting the activity of these effector T-cells. Methods: The proposed AI-driven bioinformatics solution allows to perform an accurate prediction of HLA I-restricted neoepitope immunogenicity by including several analytical modules simulating biological processes leading to the activation of CD8+ T-cells. These modules can be flexibly composed on the basis of the data available and include: (i) selection of potential neoepitope based on cancer NGS data, (ii) expression of neoepitope-associated genes, (iii) similarity to self, (iv) prediction of neoepitope-HLA binding affinity and stability, (v) effect of post-translational modification on neoepitope presentation and TCR recognition, (vi) prediction of neoepitope:TCR recognition probability based on neoepitope-HLA:TCR structural-derived features and TCR CDR3 sequence similarities. The model was trained on a curated dataset joining records of multiple studies containing neoepitopes experimentally validated for their ability to elicit adaptive immune response. Results: We present the results of a benchmarking study where we compare the performance of widely used methods for the assessment of neoepitope immunogenicity along with our solution. We investigate the predictive power of each analytical module to determine the individual information gain and determine the most informative ones. All the methods included in the benchmarking study were tested using the dataset provided by Chowell et al. 2015. As the metric for performance evaluation we use ROC AUC and the overall percentage of correctly detected immunogenic neoepitopes (precision). Conclusion: The results present the utility of the compared methods for personalized cancer vaccine design and the importance of modules related to biological processes underlying the neoepitope presentation and immunogenicity. Citation Format: Giovanni Mazzocco, Iga Niemiec, Oleksandr Myronov, Edyta Kowalczyk, Jan Kaczmarczyk, Anna Sanecka-Duin, Piotr Stepniak. Predicting immunogenic neoepitopes with biology-aware machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1447.

Background: The purpose of our study is to aid the development of adoptive T-cell receptor (TCR) -based cancer therapies with an AI platform that models binding between peptide-human leukocyte antigen (pHLA) complexes and TCRs. One of the main challenges in the field is finding the right pHLA target and the TCR that binds it. The development of in silico methods that can reduce the cost and time of laboratory research is the ‘holy grail’ of novel TCR therapies design. Molecular Dynamics (MD) simulation is a computational method allowing to gain a direct insight into the movements of protein complexes and giving the possibility to study the dynamics of the pHLA:TCR interaction. The combination of the MD and AI models helps us to increase the accuracy of the pHLA:TCR binding prediction. Methods: We conducted large-scale MD simulations of the crystal structures of 130 pHLA:TCR complexes available in the Protein Data Bank (PDB). Next, we analyzed the obtained trajectories in terms of formed bonds and other physico-chemical properties. This led us to postulate an MD model of interaction further used as an inductive bias for the AI binding prediction model, in which we applied deep learning on public single-cell pHLA:TCR datasets to estimate the probability of binding.In order to validate and improve our platform, we are building a pHLA:TCR interaction database with paired alpha and beta chain TCR sequences from a cohort of 100 colorectal cancer patients (NCT04994093). Results: We established the agretopicity and epitopicity of peptide residues within the pHLA:TCR systems based on the characteristics of the bonds formed between peptides and HLAs or TCRs in MD simulations. Additionally, the incorporation of other molecular properties, e.g. solvent accessible surface area, improved our AI pHLA:TCR model. In order to refine our results, we also account for the HLA type and physico-chemical properties of peptides. The introduction of the detailed TCR-peptide interaction matrix allowed the model to generalize to previously unseen pHLA targets. Conclusions: Rational design of effective TCR therapies can benefit from novel pHLA:TCR models for which the predictive accuracy is driven by the synergistic AI-MD approach. Citation Format: Alexander Myronov, Sławomir Stachura, Anna Sanecka-Duin, Oskar Gniewek, Łukasz Grochowalski, Maciej Jasiński, Paulina Król, Joanna Marczyńska-Grzelak, Piotr Skoczylas, Daniel Wojciechowski, Giovanni Mazzocco, Mikołaj Mizera, Jan Kaczmarczyk, Agnieszka Blum. Modeling pHLA:TCR interactions for effective TCR therapies: Leveraging AI and molecular dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2810.