MAAD: multidimensional antiviral antibody database.

Area of Science:

• Immunology and Virology
• Bioinformatics and Computational Biology
• Drug Discovery and Development

Background:

• Antibodies are crucial for preventing and treating viral infections.
• Current antibody discovery is shifting from screening to rational design, requiring integrated sequence-structure-function data.
• Existing resources lack standardization and comprehensive multidimensional features for antiviral antibodies.

Purpose of the Study:

• To introduce the Multidimensional Antiviral Antibody Database (MAAD) as a standardized, curated resource.
• To integrate multidimensional features of antibodies targeting high-impact RNA viruses.
• To facilitate systematic exploration, cross-pathogen comparison, and rational antibody design.

Main Methods:

• Developed MAAD, a database focusing on Coronaviridae, Orthomyxoviridae, and Pneumoviridae.
• Incorporated interactive analysis modules for CDR/germline annotation, sequence analysis, clustering, and interface residue identification.
• Included per-site entropy and mutation rate profiling for in-depth sequence characteristic exploration.

Main Results:

• MAAD provides a comprehensive, curated dataset of antiviral antibodies, nanobodies, and single-chain variable fragments.
• Interactive modules enable detailed analysis of antibody sequence, structure, and functional relationships.
• The platform supports structure-guided identification of critical antibody residues and mutation profiles.

Conclusions:

• MAAD bridges antibody sequence, structure, and function for advanced research.
• The database offers an open and standardized platform for comparative antiviral research.
• MAAD significantly supports and advances the discovery and rational design of therapeutic antibodies.