---
tags:
- chemistry
- biology
pretty_name: A-Alpha Bio open source data
size_categories:
- 10K<n<10M
configs:
  - config_name: YM_0005
    data_files: "data/YM_0005/data.parquet"
  - config_name: YM_0549
    data_files: "data/YM_0549/data.parquet"
  - config_name: YM_0693
    data_files: "data/YM_0693/data.parquet"
  - config_name: YM_0852
    data_files: "data/YM_0852/data.parquet"
  - config_name: YM_0985
    data_files: "data/YM_0985/data.parquet"
  - config_name: YM_0988
    data_files: "data/YM_0988/data.parquet"
  - config_name: YM_0989
    data_files: "data/YM_0989/data.parquet"
  - config_name: YM_0990
    data_files: "data/YM_0990/data.parquet"
  - config_name: YM_1068
    data_files: "data/YM_1068/data.parquet"
---
# Open Protein–Protein Interaction Affinity Datasets with AlphaSeq

Protein-protein interactions (PPIs) are fundamental to countless biological processes. One of the most informative biophysical properties of a PPI is the binding affinity: the strength of how two proteins interact. Yet, despite its importance, publicly available affinity data remains limited, constraining the development and benchmarking of protein modeling methods.

Our high-throughput yeast mating assay, [AlphaSeq](https://www.pnas.org/doi/10.1073/pnas.1705867114), enables the quantitative measurement of PPIs at scale (often generating libraries up to 1M interactions per experiment!).

To help bridge the gap between experimental affinity measurements and computational protein models, we’re open-sourcing a selection of our datasets.

Each dataset captures the results of a yeast mating experiment between two protein libraries—one of binders and one of targets. Detailed experimental context and metadata are provided in the accompanying data cards.

## Quickstart

Load one dataset:

```python
# Replace with the specific experiment ID you want to load
from datasets import load_dataset
ds = load_dataset("aalphabio/open-alphaseq", "YM_0549", split="all")
# From here, cast it to pandas / polars dataframe for analysis if desired
df = ds.to_pandas()

# ...or load directly with pandas
import pandas as pd
ym_id = "YM_0549"
df = pd.read_parquet(f"hf://datasets/aalphabio/open-alphaseq/data/{ym_id}/data.parquet")
print(df)
```

Fetch multiple experiments at once:

```python
from datasets import load_dataset, concatenate_datasets

repo = "aalphabio/open-alphaseq"
cfgs = ["YM_0549", "YM_1068"]

# Load datasets and add experiment column
datasets_list = [load_dataset(repo, cfg, split="all") for cfg in cfgs]
datasets_labeled = [
    ds.add_column("experiment", [cfg] * len(ds))
    for ds, cfg in zip(datasets_list, cfgs)
]
combined = concatenate_datasets(datasets_labeled)
print(combined)
```

Use different experiments for modeling splits:

```python
from datasets import load_dataset, DatasetDict

repo = "aalphabio/open-alphaseq"
ds = DatasetDict({
    "train": load_dataset(repo, "YM_0693", split="all"),
    "test": load_dataset(repo, "YM_0988", split="all"),
})
print(ds)
```

## Experiment glossary

| YM ID | Description | Details |
|:--------|:-------------|:---------|
| YM_0005 | Anti-CoV Ab Panel x CoV2-RBD Mutagenesis | [details](./data/YM_0005/README.md) |
| YM_0549 | VHH72 Optimization Variants Iter0 x CoV2-RBD | [details](./data/YM_0549/README.md) |
| YM_1068 | VHH72 Optimization Variants Iter1 x CoV2-RBD | [details](./data/YM_1068/README.md) |
| YM_0693 | PP489 Optimization Variants Iter0 x TIGIT | [details](./data/YM_0693/README.md) |
| YM_0988 | PP489 Optimization Variants Iter1 x TIGIT | [details](./data/YM_0988/README.md) |
| YM_0852 | Pembrolizumab-scFv Optimiziation Variants Iter0 x PD-1 | [details](./data/YM_0852/README.md) |
| YM_0985 | Pembrolizumab-scFv Optimiziation Variants Iter1 x PD-1 | [details](./data/YM_0985/README.md) |
| YM_0989 | Trastuzumab-scFv CDR3 Optimization Variants Iter0 x HER-2 | [details](./data/YM_0989/README.md) |
| YM_0990 | Trastuzumab-scFv CDR+FW Optimization Variants Iter0 x HER-2 | [details](./data/YM_0990/README.md) |

## References

This dataset collection builds on the AlphaSeq high-throughput yeast mating assay technology and includes data from multiple publications:

### Core Technology

- Younger, D., Berger, S., Baker, D., & Klavins, E. (2017). High-throughput characterization of protein-protein interactions by reprogramming yeast mating. Proceedings of the National Academy of Sciences of the United States of America, 114(46), 12166–12171. [https://doi.org/10.1073/pnas.1705867114](https://doi.org/10.1073/pnas.1705867114)

### Dataset Sources

**YM_0005:**

- Engelhart, E., Lopez, R., Emerson, R., Lin, C., Shikany, C., Guion, D., Kelley, M., & Younger, D. (2022). Massively multiplexed affinity characterization of therapeutic antibodies against SARS-CoV-2 variants. Antibody therapeutics, 5(2), 130–137. [https://doi.org/10.1093/abt/tbac011](https://doi.org/10.1093/abt/tbac011)

**YM_0549, YM_0693, YM_0852, YM_0985, YM_0988, YM_0989, YM_0990, YM_1068:**

- Agarwal, A. A., Harrang, J., Noble, D., McGowan, K. L., Lange, A. W., Engelhart, E., Lahman, M. C., Adamo, J., Yu, X., Serang, O., Minch, K. J., Wellman, K. Y., Younger, D. A., Lopez, R. M., & Emerson, R. O. (2025). AlphaBind, a domain-specific model to predict and optimize antibody-antigen binding affinity. mAbs, 17(1), 2534626. [https://doi.org/10.1080/19420862.2025.2534626](https://doi.org/10.1080/19420862.2025.2534626)

## Dataset schema

| Column | Description |
|:--------|:-------------|
| **mata_description** | Unique description of each MATa library element. Also contains negative control strains (ANeg1, ANeg2, ANeg3). Often supplemented with assay-specific information such as nomenclature or replicate labeling. |
| **matalpha_description** | Unique description of each MATα library element. Also contains negative control strains (AlphaNeg1, AlphaNeg2, AlphaNeg3). Often supplemented with assay-specific information such as nomenclature or replicate labeling. |
| **mata_sequence** | Amino acid sequence corresponding to the MATa library element. The sequence excludes linkers, epitope tags, and the cell wall anchor Aga2, and is empty for negative controls. |
| **matalpha_sequence** | Amino acid sequence corresponding to the MATα library element. The sequence excludes linkers, epitope tags, and the cell wall anchor Aga2, and is empty for negative controls. |
| **alphaseq_affinity** | Pairwise interaction affinity, reported as log₁₀(estimated Kd in nM). Values map as –1 = 0.1 nM, 0 = 1 nM, 1 = 10 nM, 2 = 100 nM, etc. Missing values indicate no observed cell fusions between the protein pair (see Younger et al., 2017). |
| **affinity_upper_bound** | Upper (stronger) bound of the 95 % confidence interval for `alphaseq_affinity`, computed with the Wilson score interval. Provided even when no point estimate is available. |
| **affinity_lower_bound** | Lower (weaker) bound of the 95 % confidence interval for `alphaseq_affinity`, computed with the Wilson score interval. |
| **normalized_affinity** | Z-score comparing each interaction’s affinity to the distribution of all interactions involving the same MATa or MATα protein. Computed by successive normalization of the affinity matrix (Olshen & Rajaratnam 2010). More negative values indicate higher specificity (e.g., –2 ≈ 2 SD below the mean). Strongly negative values are not expected when many proteins bind broadly. |
| **above_background** | Boolean flag indicating whether the interaction affinity is significantly stronger than the assay background (q < 0.05, Benjamini–Hochberg corrected t-test). |
| **sufficient_replicate_observations** | Boolean flag indicating whether the interaction was observed in > 50 % of replicate samples. A value of False suggests a potentially spurious or unreliable interaction. |

## FAQ

Please see below for some clarifying details:

### Where can I find experimental details for each dataset?

Each dataset has a corresponding README.md in its subfolder summarizing the experiment's goals, library composition, and citation info. See `./data/YM_0005/README.md` for an example.

### What kind of sequences are in the library?

While not a strict rule, the A-libraries typically contain designed sequences, while the Alpha-libraries contain corresponding targets of interest. Historically, we’ve used VHHs or scFvs in the A-library and antigen targets in the Alpha-library. Each dataset will have a card that details specific information of the individual assay run. When building or training models, note that PPIs can generally be treated as symmetric. However, members within the same library may share sequence, functional, or structural similarities. Also, some models are sensitive to input order — so ensure that (A, Alpha) pairs are treated consistently between training and testing.

### Why are there duplicate PPIs in the dataset?

Some datasets include technical replicates, often for the wild-type (“WT”) or parent sequence in mutation studies. Replicates help capture the experimental and biological variation in measured affinities. This can be useful for analyses that assess the statistical significance of observed affinity difference, such as identifying how much a vaiant changes binding strength relative to a parent protein.

### What is considered a strong or good binder?

Affinity measurements are reported in log10 Kd nM (a value of 0 indicates 1 nM, 3 is 1 uM, 5 is 100 uM). Lower values indicate stronger binding. In practice, we often compare relative affinities - for example, assessing differences in binding strength as a target interface is mutated, or comparing variant binders to their parent.

### The dataset has NaN values in the affinity, why?

Not all PPIs form detectable interactions; weak or non-binding interactions may result in no paired barcode reads, yielding NaN values. For these cases, it may be more useful to look at the lower or upper bound affinities to help interpret the range of possible affinity within the assay.

### What do Iter0 / Iter1 mean?

Iter0 and Iter1 (abbreviated for Iteration) are our nomenclature for describing antibody variant libraries designed for antibody optimization campaigns. Iter0 libraries are generated in a zero-shot fashion (ie random mutations or selections by ESM) while Iter1 libraries are generated with models trained on Iter0 datasets.

### How should I cite this dataset?

To properly acknowledge this work, please cite:

1. **This dataset repository:**

   ```text
   A-Alpha Bio (2025). Open Protein–Protein Interaction Affinity Datasets with AlphaSeq.
   https://huggingface.co/datasets/aalphabio/open-alphaseq
   ```

2. **The AlphaSeq technology paper:**

   ```text
   Younger, D., Berger, S., Baker, D., & Klavins, E. (2017).
   High-throughput characterization of protein–protein interactions by reprogramming yeast mating.
   Proceedings of the National Academy of Sciences, 114(46), 12166-12171.
   https://doi.org/10.1073/pnas.1705867114
   ```

3. **The specific experiment paper(s) for the dataset(s) you use:**
   - **If using YM_0005:** Cite the CoV epitope mapping paper (Engelhart et al., 2022) - see [References](#references) section
   - **If using any other experiments (YM_0549, YM_0693, YM_0852, YM_0985, YM_0988, YM_0989, YM_0990, YM_1068):** Cite the AlphaBind paper (2025) - see [References](#references) section

For complete citation details and DOI links, see the [References](#references) section above.

### Can I use this dataset for model training or benchmarking?

Yes — the dataset is released fully open source, and is suitable for both academic and commercial use.

### Who can I contact with questions or feedback?

Feel free to email maintainers [Natasha Murakowska](mailto:nmurakowska@aalphabio.com) or [David Noble](mailto:dnoble@aalphabio.com). We will host a discussions tab for open discourse as well.