| | --- |
| | license: cc-by-nc-sa-4.0 |
| | task_categories: |
| | - regression |
| | tags: |
| | - protein-protein interaction |
| | - binding affinity |
| | - protein language models |
| | - drug discovery |
| | - bioinformatics |
| | - multi-chain proteins |
| | - ppb affinity |
| | pipeline_tag: regression |
| | --- |
| | |
| | ## Dataset Description |
| |
|
| | This repository provides several enhanced versions of the **PPB-Affinity dataset**, ready for both sequence and structure-based modeling of multi-chain protein-protein interactions. The original PPB-Affinity dataset was introduced in the paper "[PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery](https://www.nature.com/articles/s41597-024-03997-4)". |
| |
|
| | This version of the dataset was prepared for the study: "*[Beyond Simple Concatenation: Fairly Assessing PLM Architectures for Multi-Chain Protein-Protein Interactions Prediction](https://arxiv.org/pdf/2505.20036)*." |
| |
|
| | Code: https://github.com/Proteinea/ppiseq |
| |
|
| | The primary enhancements in this repository include: |
| | * Various levels of data filtration and processing. |
| | * The addition of pre-extracted "Ligand Sequences" and "Receptor Sequences" columns, making the dataset ready for use with sequence-based models without requiring PDB file parsing. For complexes with multiple ligand or receptor chains, the sequences are comma-separated. |
| |
|
| | ## Dataset Configurations |
| |
|
| | This dataset offers four distinct configurations: |
| |
|
| | ### 1. `raw` |
| | * **Description**: Minimally processed data from the original PPB-Affinity dataset. Only annotation inconsistencies have been resolved (see Section 2.1.1 of "*Beyond Simple Concatenation...*" for details). |
| | * **Size**: 12,048 entries. |
| | * **Splits**: Contains a single `train` split encompassing all entries. |
| | * **How to load**: |
| | ```python |
| | from datasets import load_dataset |
| | |
| | raw_ds = load_dataset( |
| | "proteinea/ppb_affinity", |
| | name="raw", |
| | trust_remote_code=True |
| | )["train"] |
| | ``` |
| | |
| | ### 2. `raw_rec` |
| | * **Description**: Similar to the `raw` version, but with an additional step to recover missing residues in the protein sequences (see Section 2.1.2 of "*Beyond Simple Concatenation...*" for details). |
| | * **Size**: 12,048 entries. |
| | * **Splits**: Contains a single `train` split. |
| | * **How to load**: |
| | ```python |
| | from datasets import load_dataset |
| |
|
| | raw_rec_ds = load_dataset( |
| | "proteinea/ppb_affinity", |
| | name="raw_rec", |
| | trust_remote_code=True |
| | )["train"] |
| | ``` |
| | |
| | ### 3. `filtered` |
| | * **Description**: This version includes additional cleaning and filtration steps applied to the raw with missing residues recovered data (see Section 2.1.2 of "*Beyond Simple Concatenation...*" for details on filtration). It comes with pre-defined train, validation, and test splits (see Section 2.1.3 of "*Beyond Simple Concatenation...*" for splitting methodology). |
| | * **Size**: |
| | * Train: 6,485 entries |
| | * Validation: 965 entries |
| | * Test: 757 entries |
| | * **Splits**: `train`, `validation`, `test`. |
| | * **How to load**: |
| | ```python |
| | from datasets import load_dataset |
| | |
| | dataset_dict = load_dataset( |
| | "proteinea/ppb_affinity", |
| | name="filtered", |
| | trust_remote_code=True |
| | ) |
| | train_ds = dataset_dict["train"] |
| | val_ds = dataset_dict["validation"] |
| | test_ds = dataset_dict["test"] |
| | ``` |
| | |
| | ### 4. `filtered_random` |
| | * **Description**: This version uses the same cleaned and filtered entries as the `filtered` configuration but provides random 80%-10%-10% splits for train, validation, and test, respectively. The shuffling is performed with a fixed seed (42) for reproducibility. |
| | * **Size**: Same total entries as `filtered`, split as: |
| | * Train: 6,565 entries |
| | * Validation: 820 entries |
| | * Test: 822 entries |
| | * **Splits**: `train`, `validation`, `test`. |
| | * **How to load**: |
| | ```python |
| | from datasets import load_dataset |
| |
|
| | dataset_dict = load_dataset( |
| | "proteinea/ppb_affinity", |
| | name="filtered_random", |
| | trust_remote_code=True |
| | ) |
| | train_ds = dataset_dict["train"] |
| | val_ds = dataset_dict["validation"] |
| | test_ds = dataset_dict["test"] |
| | ``` |
| | |
| | ## Data Fields |
| |
|
| | All configurations share a common set of columns. These include columns from the original PPB-Affinity dataset (refer to the original paper for more details), plus two new sequence columns: |
| |
|
| | * **`Ligand Sequences`**: `string` - Comma-separated amino acid sequences of the ligand chain(s). |
| | * **`Receptor Sequences`**: `string` - Comma-separated amino acid sequences of the receptor chain(s). |
| |
|
| | **Note on Sequences**: When multiple ligand or receptor chains are present in a complex, their respective amino acid sequences are concatenated with a comma (`,`) as a separator in the "Ligand Sequences" and "Receptor Sequences" fields. |
