EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

Area of Science:

• Immunoinformatics
• Vaccine Development
• Computational Biology

Background:

• The COVID-19 pandemic necessitates rapid vaccine development.
• In silico epitope prediction aids in identifying vaccine candidates.
• Existing methods require optimization for novel virus strains.

Purpose of the Study:

• To introduce and validate the EpiCurator workflow for predicting and prioritizing SARS-CoV-2 epitopes.
• To identify novel epitopes with desirable immunological properties.
• To design and computationally validate a multi-epitope immunogen.

Main Methods:

• Utilized immunoinformatic approaches and computational filtering tools for epitope prediction.
• Analyzed SARS-CoV-2 genomes from the GISAID database.
• Performed in silico validation of multi-epitope immunogenic potential, including antigenicity, stability, and TLR interactions.

Main Results:

• Identified 11 novel receptor-binding domain (RBD) epitopes from the Spike glycoprotein.
• These epitopes exhibit high antigenicity, conservancy, and broad population coverage, and are present in current variants of concern.
• The designed multi-epitope construct demonstrated stability, high antigenicity, and strong interactions with Toll-Like Receptors (TLRs).

Conclusions:

• The EpiCurator workflow is effective for discovering potential SARS-CoV-2 vaccine epitopes.
• The identified novel epitopes and the multi-epitope design show promise for immunogen development.
• This approach accelerates the identification of vaccine candidates against emerging infectious diseases.