ProFSADB

Dataset Description
ProFSADB is a large-scale protein-ligand interaction pretraining dataset generated by simulating pocket-ligand complexes from high-resolution protein structures. It addresses the scarcity of experimentally determined protein-ligand complexes (e.g., PDB) by extracting 5+ million non-redundant pocket-pseudo-ligand pairs through fragmentation and interaction modeling. Each complex mimics ligand-receptor interactions to enable robust pretraining for biomedical tasks like druggability prediction and ligand affinity estimation.

Dataset Structure

• Samples: Over 5 million complexes.
• Format: PDB files containing one complex per file.
  • Receptor chain (R): Pocket residues surrounding the pseudo-ligand.
  • Ligand chain (L): Drug-like protein fragment acting as a pseudo-ligand.
• Stratified Sampling: Aligned with the PDBBind (v2020) distribution to ensure biological relevance.

Creation Process

1. Fragment Isolation: Protein structures are segmented into fragments (pseudo-ligands), with terminal corrections to address peptide bond-breaking artifacts.
2. Pocket Definition: Excludes the five nearest residues on each fragment side to focus on long-range interactions. Pockets are defined as residues with ≥1 heavy atom within 6Å of the fragment.
3. Quality Control: Complexes are filtered to retain only high-confidence interaction pairs.

Unique Advantages

• Scale: 50× larger than existing experimental complex datasets (e.g., PDB).
• Interaction Modeling: Contrastive pretraining aligns pocket features with pretrained small-molecule representations.
• Diversity: Leverages structural variety from protein databases to reduce data bias.

License

CC-BY-4.0

Citation

If you use ProFSADB or the ProFSA method, please cite:

@inproceedings{gao2023self,  
  title={Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment},  
  author={Gao, Bowen* and Jia, Yinjun* and Mo, Yuanle and Ni, Yuyan and Ma, Weiying and Ma, Zhiming and Lan, Yanyan†},  
  booktitle={International Conference on Learning Representations (ICLR)},  
  year={2023},  
  url={https://openreview.net/forum?id=uMAujpVi9m}  
}

Links

• Paper: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
• Homepage: Project Page

ProFSA Model Weights

The weights of our best model pretrained using the ProFSADB data is located at checkpoint_best.pt.