Folding Membrane Proteins by Deep Transfer Learning

Sheng Wang, Zhen Li, Yizhou Yu, Jinbo Xu

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling. Here, we describe a high-throughput deep transfer learning method that first predicts MP contacts by learning from non-MPs and then predicts 3D structure models using the predicted contacts as distance restraints. Tested on 510 non-redundant MPs, our method has contact prediction accuracy at least 0.18 better than existing methods, predicts correct folds for 218 MPs, and generates 3D models with root-mean-square deviation (RMSD) less than 4 and 5 Å for 57 and 108 MPs, respectively. A rigorous blind test in the continuous automated model evaluation project shows that our method predicted high-resolution 3D models for two recent test MPs of 210 residues with RMSD ∼2 Å. We estimated that our method could predict correct folds for 1,345–1,871 reviewed human multi-pass MPs including a few hundred new folds, which shall facilitate the discovery of drugs targeting at MPs.
Original languageEnglish (US)
Pages (from-to)202-211.e3
Number of pages210799
JournalCell Systems
Volume5
Issue number3
DOIs
StatePublished - Aug 29 2017

ASJC Scopus subject areas

  • Histology
  • Pathology and Forensic Medicine
  • Cell Biology

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