proteinea/ppb_affinity

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".

This version of the dataset was prepared for the study: "Beyond Simple Concatenation: Fairly Assessing PLM Architectures for Multi-Chain Protein-Protein Interactions Prediction."

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:

  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:

  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:

  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:

  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.