This study introduces the Multidimensional Antiviral Antibody Database (MAAD), a comprehensive and standardized platform integrating sequence, structure, and functional data for antibodies targeting three high-impact RNA virus families. MAAD serves not only as a curated data repository but also as an interactive analytical toolbox designed to support rational antibody engineering, structure-based vaccine design, and AI-driven antibody discovery.
Key Highlights:
Database Content & Scale: MAAD systematically collates and annotates 27,414 entries of antibodies, nanobodies, and single-chain variable fragments (scFvs) from 805 peer-reviewed publications and 140 patents. It focuses on key pathogens from three major RNA virus families: Coronaviridae (SARS-CoV-1, SARS-CoV-2, MERS-CoV), Orthomyxoviridae (influenza), and Pneumoviridae (RSV, hMPV).
Standardized Annotation & Integration: Each entry is enriched with standardized metadata, including sequences, antigen targets, binding/neutralization profiles, complementarity-determining region (CDR) annotations, V/J germline gene usage, somatic hypermutation (SHM) data, and corresponding structural information, achieving a true integration of sequence-structure-function data.
Interactive Analysis Modules: The platform is equipped with a suite of powerful interactive tools:
Sequence Analysis: Supports full-length or CDR-based similarity searches, germline gene usage profiling, and CDR3 sequence logo generation.
Structural Analysis: Integrates 1,394 antigen-antibody complex structures. An interactive viewer allows exploration of interface residues, which are annotated with site-specific Shannon entropy and mutation frequency to identify immune escape hotspots.
Phylogenetic Clustering: Enables phylogenetic tree construction for user-provided sequences and exploration of pre-computed trees, facilitating functional inference for unvalidated antibodies based on evolutionary proximity to characterized ones.
Unique Insights & Findings: Analysis reveals both conserved and pathogen-specific germline gene biases. For instance, IGHV3-21 is enriched in RSV antibodies, while IGHV1-69 and IGHV3-30 are broadly utilized across multiple viruses. Entropy analysis of interface residues visually maps key regions of viral variability and potential immune escape.
Applications & Significance: MAAD transcends the role of a static database, serving as a dynamic, multifunctional platform for rational antibody/vaccine design, AI model training, and antibody evolution studies. Its modular architecture and standardized data schema are designed for future integration of quantitative experimental data (e.g., binding affinity, DMS results) and expansion to cover more pathogens.
Overall Significance:
By constructing this comprehensive, open-access database with robust analytical capabilities, the study significantly advances the systematic understanding of sequence-structure-function relationships in antiviral antibodies. MAAD provides an essential data resource and research toolkit to enhance preparedness against current and future viral threats.
DOI:
10.1093/procel/pwaf106