  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-membrane proteins (non-MPs) and then
predicting three-dimensional 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 (TMscore at least 0.6), and generates
three-dimensional models with RMSD less than 4 Angstrom and 5 Angstrom for 57
and 108 MPs, respectively. A rigorous blind test in the continuous automated
model evaluation (CAMEO) project shows that our method predicted
high-resolution three-dimensional models for two recent test MPs of 210
residues with RMSD close to 2 Angstrom. We estimated that our method could
predict correct folds for between 1,345 and 1,871 reviewed human multi-pass MPs
including a few hundred new folds, which shall facilitate the discovery of
drugs targeting at membrane proteins.
