AnnCovDB: a manually curated annotation database for mutations in SARS-CoV-2 spike protein

Area of Science:

• Virology
• Molecular Biology
• Bioinformatics

Background:

• Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved numerous variants of concern (VOCs) and interest (VOIs).
• Mutations in the spike (S) protein are critical for viral infection, transmission, immune evasion, and pathogenicity.
• Existing knowledge on S protein mutation effects is fragmented across numerous publications.

Purpose of the Study:

• To create a manually curated database, AnnCovDB, for functional annotations of SARS-CoV-2 S protein mutations.
• To organize and present information on mutation effects in a structured, accessible format.

Main Methods:

• Selected mutations from at least 8000 variants in GISAID.
• Searched PubMed using mutation keywords to identify relevant publications.
• Manually curated 2093 annotation entities for 205 single and 93 multiple mutations.
• Organized annotations into multilevel hierarchical categories.

Main Results:

• AnnCovDB provides manually curated functional annotations for SARS-CoV-2 S protein mutations.
• The database includes 2093 annotation entities covering 205 single and 93 multiple mutations.
• Annotations are structured hierarchically, e.g., 'Infectious cycle➔Attachment➔ACE2 binding affinity➔Increase' for N501Y.

Conclusions:

• AnnCovDB serves as a centralized resource for understanding the functional impact of SARS-CoV-2 S protein mutations.
• The database facilitates querying specific mutations and browsing functional annotation entities.
• AnnCovDB supports research on viral evolution, immune response, and therapeutic development.