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--- |
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license_name: cc-by-with-restrictions |
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license_link: LICENSE.md |
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tags: |
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- biology |
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- protein |
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- antibody |
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pretty_name: GDPa1 |
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size_categories: |
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- n<1K |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: GDPa1_v1.2_20250814.csv |
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extra_gated_fields: |
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First name: text |
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Last name: text |
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Company: text |
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Work email: text |
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I want to use this dataset for: text |
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--- |
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# GDPa1: Antibody developability dataset |
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Contains the assay data for 242 antibodies across 9 assays as described in our latest preprint, [PROPHET-Ab: A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training](https://www.biorxiv.org/content/10.1101/2025.05.01.651684v1). |
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## Example usage |
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Using pandas: |
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``` |
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import pandas as pd |
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# Login using e.g. `huggingface-cli login` to access this dataset |
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df = pd.read_csv("hf://datasets/ginkgo-datapoints/GDPa1/GDPa1_v1.2_20250814.csv") |
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``` |
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Using Hugging Face datasets: |
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``` |
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from datasets import load_dataset |
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# Login using e.g. `huggingface-cli login` to access this dataset |
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ds = load_dataset("ginkgo-datapoints/GDPa1") |
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``` |
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## Data processing |
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The main table in this data (`GDPa1_v1.1_20250612.csv`) is an averaged form of the tidy data format. We perform the following averaging: |
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1. Choose only the first production batch (since production batches differed in their constant regions, and the first production batch contained all 246 antibodies) |
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2. Average by taking the median across all replicates. |
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This CSV also contains the following computed 5-fold cross-validation columns: |
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- random_fold: Randomly assigned folds |
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- hierarchical_cluster_fold: Hierarchical clustering using pairwise sequence identities computed by MMseqs2 |
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- hierarchical_cluster_IgG_isotype_stratified_fold: The same as hierarchical clustering, while attempting to keep IgG subclass representation uniform |
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across groups. |
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This is further described in our preprint, in the "Predictive Model Training" section. |
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We encourage using `hierarchical_cluster_IgG_isotype_stratified_fold` for reporting results. |
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## Antibody production |
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Antibodies were expressed in HEK293F and purified using Protein A chromatography prior to developability assessment for all assays. |
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Antibodies tested on DLS-kD went through an additional polishing SEC step. |
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A smaller subset of antibodies (20 IgGs) was produced in ExpiCHO and purified using Protein A chromatography. |
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## Developability assays |
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1. Titer by Valita |
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2. Purity by rCE-SDS |
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3. Aggregation by SEC |
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4. Thermostability by nanoDSF and DSF |
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5. Colloidal stability by SMAC |
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6. Hydrophobicity by HIC |
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7. Heparin binding by HAC |
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8. Self association by AC-SINS |
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9. Polyreactivity by bead-based method against CHO SMP and ovalbumin |
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10. Self association by DLS-kD (only performed on 10 antibodies, present in the full datasheet) |
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## Full Datasheet |
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Our full datasheet in Excel format contains the following information: |
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- Definitions of column headers in other datasheets |
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- Antibody sequences |
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- Assay data in “tidy data” format with one row per replicate |
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- Assay data summary statistics with average, standard deviation, and replicates for each assay |
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- Data for nanodsf vs dsf with the same ramp rate in “tidy data” format |
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- Prior literature data summarizing prior published results compared with GDPa1 data in the associated preprint |
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## Changelog |
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- Monday August 18th: Added AC-SINS data with better curve fitting, and corrected PR score calculation. Added a sheet called "Versioning" in the full Excel file. |
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- Thursday July 3rd: Added ABodyBuilder3 predicted structures. |
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## Contact |
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For more information on this data, see our website at https://datapoints.ginkgo.bio/, or contact us at datapoints@ginkgobioworks.com for specific questions about the data. |
