README.md

---
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

```python
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

```python
# 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

```python
# 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

```bibtex
@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/)*