Folding Membrane Proteins by Deep Transfer Learning

S. Wang, Z. Li, Y. Yu, J. Xu
Cell Syst., volume 5, issue 3, pp. 202-211, e3, (2017)

Folding Membrane Proteins by Deep Transfer Learning


Co-evolution analysis, Deep learning, Deep transfer learning, Homology modeling, Membrane protein contact prediction, Membrane protein folding, Multiple sequence alignment


​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.


DOI: 10.1016/j.cels.2017.09.001


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