README.md

metadata

dataset_info:
  features:
    - name: dataset
      dtype: string
    - name: heavy_sequence
      dtype: string
    - name: light_sequence
      dtype: string
    - name: scfv
      dtype: bool
    - name: affinity_type
      dtype: string
    - name: affinity
      dtype: string
    - name: antigen_sequence
      dtype: string
    - name: confidence
      dtype:
        class_label:
          names:
            '0': medium
            '1': high
            '2': very_high
    - name: nanobody
      dtype: bool
    - name: processed_measurement
      dtype: float64
    - name: target_name
      dtype: string
    - name: target_pdb
      dtype: string
    - name: target_uniprot
      dtype: string
    - name: source_url
      dtype: string
    - name: heavy_cdr1
      dtype: string
    - name: heavy_cdr2
      dtype: string
    - name: heavy_cdr3
      dtype: string
    - name: light_cdr1
      dtype: string
    - name: light_cdr2
      dtype: string
    - name: light_cdr3
      dtype: string
  splits:
    - name: train
      num_bytes: 2137958513
      num_examples: 1227083
  download_size: 339997839
  dataset_size: 2137958513
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
pretty_name: 'AgAb DB: Antigen Specific Antibody Database'
tags:
  - biology
  - immunology
  - antibodies
  - protein-protein-interactions
  - drug-discovery
  - computational-biology
  - therapeutics
  - machine-learning
  - protein-sequence-modeling
  - binding-affinity-prediction
  - antibody-design
task_categories:
  - text-classification
license: other
license_details: >-
  Non-commercial research use only. Commercial inquiries should be directed to
  NaturalAntibody.
language:
  - en

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

Field	Type	Description
heavy_sequence	string	Antibody heavy chain amino acid sequence
light_sequence	string	Antibody light chain amino acid sequence
antigen_sequence	string	Target antigen amino acid sequence
affinity	string	Binding affinity value
confidence	string	Data quality level (very_high, high, medium)

Additional Metadata

Field	Type	Description
dataset	string	Original source dataset
affinity_type	string	Measurement type (KD, IC₅₀, etc.)
nanobody	bool	Whether it's a nanobody
scfv	bool	Single-chain variable fragment
target_name	string	Antigen name
target_pdb	string	PDB structure ID
target_uniprot	string	UniProt accession
heavy_cdr1/cdr2/cdr3	string	Complementarity-determining regions
light_cdr1/cdr2/cdr3	string	Light chain CDRs

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

Source	Nanobody Count	Notes
AlphaSeq	67,058	Mutations for improved binding
Patents	40,517	Patent literature extraction
Literature	1,936	Research paper curation
Structures	1,258	PDB structure-derived
AATP, OSH, RMNA	~133	Specialized datasets

scFv Distribution by Source

Source	scFv Count	Notes
AlphaSeq	131,645	Primary scFv source
Literature	512	Research paper curation

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

Dataset	Records	Unique Antibodies	Key Characteristics
BUZZ	524,346	524,346	Trastuzumab mutations binding to HER2
AlphaSeq	198,703	193,867	Antibody mutations across 4 targets (TIGIT, SARS-CoV2-RBD, PD-1, HER2)
ABBD	155,853	88,946	Eight antibody-antigen cases with heavy chain mutations
Patents	217,463	31,173	NLP-extracted sequences from patent literature
COVID-19	27,301	6,759	SARS-CoV-2 neutralization data (Cov-AbDab)
HIV	48,008	192	HIV-targeting antibodies (LANL database)
BioMap	2,725	728	Binding ΔG values across 8 species
Literature	5,580	4,841	Curated from research articles (1,940 nanobodies)
FLAB	6,849	6,798	Five publications on viral/cancer targets
ABDesign	672	672	Systematic CDR-H3 point mutations

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

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Dataset provided by NaturalAntibody