Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations

By Wei Zheng, Chengxin Zhang, Yang Li, Robin Pearce, Eric W. Bell, Yang Zhang. Cell Reports Methods, 2021

502 citations recorded by the source corpus.

SUMMARY Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dd34fe3fe27a6e8e8c85473a286f85f76709ae2e

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In Molecular Biology

  1. Robin Pearce
  2. Yang Zhang
  3. Chengxin Zhang
  4. Eric W. Bell
  5. Yang Li
  6. Wei Zheng
  7. Xiaogen Zhou
  8. Guijun Zhang

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