Abstract:
Immunization continues to be a cornerstone of global public health, drastically reducing mortality rates worldwide. Over time, the field of vaccinology has progressively shifted from utilizing whole-pathogen preparations toward highly targeted epitope and subunit formulations. However, traditional laboratory-based methods for vaccine discovery remain heavily constrained by high financial costs and extended developmental timelines. To mitigate these bottlenecks, this thesis introduces a comprehensive suite of computational resources for designing epitope or subunit based vaccines. The work presented in this thesis is organized into three major components: identification of epitope-based vaccine candidates, safety evaluation of predicted candidates, and assessment of stability under physiological and environmental conditions. Under the first objective, a tool named CLBtope was developed to predict both linear and conformational B-cell epitopes from antigen sequences, facilitating the identification of potential vaccine candidates capable of eliciting protective B-cell and antibody responses. To ensure candidate safety, two predictive models were established: DMPPred, which identifies peptides associated with type 1 diabetes mellitus, and AlgPred3, which comprehensively evaluates the allergenic risks of proposed vaccine components. Finally, to address peptide and protein stability under diverse physiological and environmental conditions, three distinct platforms were developed: AFProPred for the characterization and design of antifreeze proteins, PPTStab for engineering thermostable proteins, and PlifePred2 for estimating peptide half-lives in body fluids. All tools developed in this thesis are freely available to the scientific community as web servers and standalone software through platforms such as GitHub and PyPI. State-of-the-art AI methodologies, including machine learning, deep learning, and large language models, were deployed to design effective subunit vaccines.
