SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning.

One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.

Development of adenoviral vector-based mucosal vaccine against influenza.

The recent pandemic threat of the influenza virus makes the increased safety and efficiency of vaccination against the pathogen a most important issue. It has been well established that for maximum protective effect, the vaccination should mimic natural infection. Therefore, recent efforts to develop a new influenza vaccine have focused on intranasal immunization strategies. Intranasal immunization is capable of inducing secretory IgA and serum IgG responses to provide a double defense against mucosal pathogens. On the other hand, it is desirable that a live pathogen is not present in the vaccine. In addition, for optimal induction of the immune responses via the nasal route, efficient and safe mucosal adjuvants are also required. This is possible to attain using an adenoviral vector for vaccine development. Adenoviral vectors are capable of delivering and protecting the antigen encoding sequence. They also possess a natural mechanism for penetrating into the nasal mucous membrane and are capable of activating the innate immune response. This review describes the basic prerequisites for the involvement of recombinant adenoviruses for mucosal (nasal) vaccine development against the influenza virus.