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KAUST DepartmentComputational Bioscience Research Center (CBRC)
Permanent link to this recordhttp://hdl.handle.net/10754/625815.1
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AbstractComputational 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.
CitationWang S, Li Z, Yu Y, Xu J (2017) Folding membrane proteins by deep transfer learning. Available: http://dx.doi.org/10.1101/181628.
SponsorsThis work is supported by National Institutes of Health grant R01GM089753 to J.X. and National Science Foundation grant DBI-1564955 to J.X. The authors are also grateful to the support of Nvidia Inc. and the computational resources provided by XSEDE.
PublisherCold Spring Harbor Laboratory
Except where otherwise noted, this item's license is described as The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
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