Folding membrane proteins by deep transfer learning

Wang, S.; Li, Z.; Yu, Y.; Xu, J.

doi:10.1101/181628

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bioRxiv · 10.1101/181628

Folding membrane proteins by deep transfer learning

Wang, S.·Li, Z.·Yu, Y.·Xu, J.

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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-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>0.6), and generates three-dimensional models with RMSD less than 4[A] and 5[A] 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 [~]2[A]. 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 membrane proteins.

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Wang, S., Li, Z., Yu, Y., Xu, J.. 2017-08-28. Folding membrane proteins by deep transfer learning. https://doi.org/10.1101/181628

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