onurborasahin/DARPA_Lift_2026

AgAb DB: Antigen Specific Antibody Database

A comprehensive collection of antibody-antigen interaction data for computational biology and therapeutic design.

Dataset Summary

AgAb DB aggregates antibody-antigen binding data from multiple sources, containing over 1.2 million antibody-antigen pairs with binding affinity measurements. This dataset is essential for training machine learning models in computational immunology and antibody engineering.

Key Statistics

• —1,227,083 antibody-antigen interaction records
• —309,884 unique antibodies (full antibodies, nanobodies, scFvs)
• —4,334 unique antigens
• —170,660 complete heavy/light chain pairs
• —70,388 nanobodies and 132,157 scFv antibodies
• —Focus on human health: Infectious diseases, cancer, autoimmune conditions
• —Diverse antigen types: Viral proteins, bacterial antigens, cancer markers, autoantigens

Note: Statistics for unique antibodies/antigens are from original documentation and may be proportionally larger in the full 1.2M record dataset.

Data Quality Distribution

• —51% very_high confidence (robust sequences and methodology)
• —high confidence (manually curated datasets)
• —medium confidence (automated discovery, some uncertainty)

Affinity Measurement Types

• —Quantitative metrics: Gibbs free energy changes, kinetic constants, IC₅₀
• —Qualitative binding assessments
• —Mixed data types across different sources

Data Structure

Core Fields

Additional Metadata

Dataset Split

• —Train: All 1,227,083 records in a single training set

The full dataset is provided as a single training split to maximize available data for machine learning applications. Users can create their own validation/test splits as needed for their specific use cases.

Confidence Categories

• —very_high: Both sequences and methodology used for calculating affinity were robust (e.g., AbDesign, BioMap, SKEMPI 2.0)
• —high: Manually curated datasets or those containing antigen names/mutations rather than full sequences (e.g., FLAB datasets)
• —medium: Automated data discovery with some uncertainty (e.g., patent databases)

Antibody Types Included

• —Full antibodies: Complete heavy and light chain pairs (traditional monoclonal antibodies)
• —Nanobodies: Single-domain antibodies (VHH format) - 70K+ entries across datasets
• —scFv: Single-chain variable fragments - 132K+ entries, primarily from AlphaSeq
• —Mixed formats: Various antibody fragment types and engineered variants

Nanobody Distribution by Source

scFv Distribution by Source

Sequence Characteristics

• —Predominantly short sequences: <150 amino acids typical
• —Majority include both chains: Heavy and light chain pairs
• —Diverse antigen targets: Infectious diseases, cancer, autoimmune conditions
• —Multiple affinity measurement types: KD, IC₅₀, ΔG, binary binding

Usage

Load the Dataset

from datasets import load_dataset

# Load from OpenMed
dataset = load_dataset("OpenMed/agab-db")

# Access the training data (full dataset)
train_data = dataset["train"]

# Optional: Create your own validation/test splits
from sklearn.model_selection import train_test_split
import pandas as pd

# Convert to pandas for splitting
df = pd.DataFrame(train_data)
train_df, test_df = train_test_split(df, test_size=0.1, random_state=42)
train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=42)

Filter for Research

# High-quality data only
high_quality = dataset.filter(lambda x: x["confidence"] == "very_high")

# Nanobodies for specialized studies
nanobodies = dataset.filter(lambda x: x["nanobody"] == True)

# Specific antigens
covid_data = dataset.filter(lambda x: "covid" in x["target_name"].lower())

Prepare for ML Training

# Extract sequences for language models
sequences = []
for item in dataset["train"]:
    if item["heavy_sequence"]:
        sequences.append(item["heavy_sequence"])
    if item["light_sequence"]:
        sequences.append(item["light_sequence"])

Applications

Machine Learning Use Cases

• —Antibody language models: Train sequence models on antibody repertoires for generative design
• —Binding affinity prediction: Develop regression models for antibody-antigen interaction strength
• —Therapeutic design: Guide rational antibody engineering and optimization
• —Computational immunology: Study immune responses and antibody development patterns
• —Virtual screening: Prioritize antibody candidates for experimental validation
• —Structure-affinity relationships: Learn connections between 3D structures and binding properties

Research Applications

• —Antibody repertoire analysis: Study natural antibody diversity and evolution
• —Cross-reactivity prediction: Identify potential off-target effects
• —Immunogenicity assessment: Predict antibody developability and safety
• —Drug discovery pipelines: Accelerate hit identification and lead optimization
• —Comparative immunology: Study antibody responses across different species

Integration with Other Tools

• —Protein structure prediction: Use with ESMFold for 3D structure generation
• —Molecular dynamics: Combine with simulation tools for binding mechanism studies
• —High-throughput screening: Guide experimental antibody library screening
• —CRISPR engineering: Design antibodies for gene therapy applications

Data Sources

Aggregated from 25+ datasets including GenBank, SKEMPI 2.0, peer-reviewed publications, and patent databases.

Major Dataset Components

Inclusion Criteria

• —Transparency and completeness of data
• —Relevance to human health
• —Quantitative binding affinity measurements
• —Complete amino acid sequences for all biomolecules

Data Processing Pipeline

1. 1.Aggregation: Collection from 14 distinct sources → 25 integrated datasets
2. 2.Curation: Multi-stage pipeline with automated extraction, normalization, and manual verification
3. 3.Standardization: Common structure implemented across all studies
4. 4.Validation: Automated feasibility checks and manual verification of critical datasets

Citation

@dataset{agab_db,
  title={AgAb DB: Antigen Specific Antibody Database},
  author={NaturalAntibody},
  year={2024},
  url={https://naturalantibody.com/agab/}
}

License

Available for non-commercial research use only. Contact NaturalAntibody for commercial licensing.

Dataset provided by [NaturalAntibody](https://naturalantibody.com/agab/)