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HPmod

HPmod (Human Proteome Model) deposit human protein structure and function that automatically predicted by the state-of-the-art algorithms from Yang Zhang Lab. Protein structure models are predicted by D-I-TASSER, and the protein functions, including Gene Ontology (GO), Enzyme Commission (EC), and ligand-binding sites, are predicted by COFACTOR. The database contains 19,512 proteins collected from Uniprot, which can be classified as 12,236 single-domain proteins and 7,276 multi-domain proteins. (>>more about the HPmod database)

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HPmod News

The current database contains 19,512 entries.

  • Apr 30, 2022: 76 models for human complete genome proteins newly reported by Science are generated by D-I-TASSER modeling.
  • Apr 20, 2022: 1,689 models for proteins of human proteome are generated by D-I-TASSER modeling. The distance prediction is created by AlphaFold2, AttentionPotential and DeepPotential, the initial conformations are from LOMETS3 templates, then a Replica-Exchange Montre Carlo simulation is used for constructing models. Multi-domain protein is first spilt by FUpred to domain-level sequences, individual domain models are predicted by D-I-TASSER modeling pipeline, then the first D-I-TASSER domain-level models are assembled by DeepDEMO to a full-length model.
  • Sep 1, 2021: 19,512 models for proteins of human proteome are generated by full D-I-TASSER modeling. The distance prediction is created by AttentionPotential and DeepPotential, and initial conformations are from LOMETS3 templates, then a Replica-Exchange Montre Carlo simulation is used for constructing 5 models. Multi-domain protein is first spilt by FUpred to domain-level sequences, individual domain models are predicted by D-I-TASSER modeling pipeline, then the first D-I-TASSER domain-level models are assembled by DeepDEMO to a full-length model.
  • May 1, 2021: 19,326 models for proteins of human proteome are generated by D-I-TASSER ab initio modeling. The distance prediction is created by DeepPotential, then a L-BFGS system is used for constructing 10 initial models. Those initial models are then refined by D-I-TASSER to generate 5 models. Multi-domain protein is first spilt by FUpred to domain-level sequences, individual domain models are predicted by D-I-TASSER ab initial pipeline, then be assembled by DEMO to a full-length model.

References

  • Wei Zheng, Qiqige Wuyun, Yang Li, Quancheng Liu, Xiaogen Zhou, Chunxiang Peng, Yiheng Zhu, Lydia Freddolino, Yang Zhang Deep learning-based single- and multi-domain protein structure prediction with D-I-TASSER. Nature Biotechnology, https://doi.org/10.1038/s41587-025-02654-4 (2025).

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