AI4Protein/GO_MF_AlphaFold2

GO-MF Dataset with AlphaFold2 Structural Sequence

• Description: Molecular Function of Gene Ontology (GO) project.
• Number of labels: 489
• Problem Type: multi_label_classification
• Columns:
  • aa_seq: protein amino acid sequence
  • foldseek_seq: foldseek 20 3di structural sequence
  • ss8_seq: DSSP 8 secondary structure sequence

Github

Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models

https://github.com/tyang816/SES-Adapter

VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning

https://github.com/ai4protein/VenusFactory

Citation

Please cite our work if you use our dataset.

@article{tan2024ses-adapter,
  title={Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models},
  author={Tan, Yang and Li, Mingchen and Zhou, Bingxin and Zhong, Bozitao and Zheng, Lirong and Tan, Pan and Zhou, Ziyi and Yu, Huiqun and Fan, Guisheng and Hong, Liang},
  journal={Journal of Chemical Information and Modeling},
  year={2024},
  publisher={ACS Publications}
}

@article{tan2025venusfactory,
  title={VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning},
  author={Tan, Yang and Liu, Chen and Gao, Jingyuan and Wu, Banghao and Li, Mingchen and Wang, Ruilin and Zhang, Lingrong and Yu, Huiqun and Fan, Guisheng and Hong, Liang and Zhou, Bingxin},
  journal={arXiv preprint arXiv:2503.15438},
  year={2025}
}