InflANNet: a neural network predictor for Influenza A CTL and HTL epitopes to aid robust vaccine design

Abstract

Abstract Background An efficient and reliable data-driven method is essential to aid robust vaccine design, particularly in the case of an epidemic like Influenza A. Although various prediction tools are existing, most of them focus on the MHC-peptide binding affinity predictions. A tool which can incorporate more features other than binding affinity which characterizes the T-cell epitopes as vaccine candidates would be of much value in this scenario. The objective of this study is to develop two separate neural network models for the predictions of CTLs (cytotoxic T lymphocyte) and HTLs (helper T lymphocyte) with the manually curated datasets as a part of this study from the raw viral sequences of Influenza A. Results The epitope datasets curated from the raw sequences of the broadly protective Neuraminidase protein were utilized for building and training the models for CTLs and HTLs. Each set consisted of nearly a balanced mix of vaccine candidates and non-vaccine candidates for both CTLs and HTLs. These were fed to neural networks as they are proven to be powerful for the predictions when compared with the other machine/deep learning algorithms. A set of epitopes experimentally proved were chosen to validate the model which was also tested through mutational analysis and cross-reactivity. The prepared dataset gave some valuable insights into the epitope distribution statistics and their conservancy in various outbreaks. An idea about the most probable range of peptide-MHC binding affinities was also obtained. Both the models performed well giving high accuracies when validated. These epitopes were checked for cross-reactivity with other antigens upon which it proved to be highly conservative and ideal for vaccine formulation. Conclusions The combination of various features and the resulting model efficiencies in turn proved that the collected features are valuable in the easy identification of the vaccine candidates. This suggests that our proposed models have more potential for conserved epitope prediction compared to other existing models trained on similar data and features. The possibility of refining the model with more set threshold values based on more parameters is an added feature that makes it more user driven. Furthermore, the uniqueness of the model due to exclusive set of Neuraminidase epitopes paves a robust way for rapid vaccine design. Graphical abstract

Keywords

• Vaccine design
• Influenza A
• T-cell epitopes
• Neuraminidase
• Machine learning
• Deep learning