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dc.contributor.authorWang, Sheng
dc.contributor.authorLi, Zhen
dc.contributor.authorYu, Yizhou
dc.contributor.authorXu, Jinbo
dc.date.accessioned2018-01-16T13:48:46Z
dc.date.available2017-10-05T12:47:09Z
dc.date.available2018-01-16T13:48:46Z
dc.date.issued2017-08-29
dc.date.issued2017-09-27
dc.identifier.citationWang S, Li Z, Yu Y, Xu J (2017) Folding Membrane Proteins by Deep Transfer Learning. Cell Systems 5: 202–211.e3. Available: http://dx.doi.org/10.1016/j.cels.2017.09.001.
dc.identifier.issn2405-4712
dc.identifier.pmid28957654
dc.identifier.doi10.1101/181628
dc.identifier.doi10.1016/j.cels.2017.09.001
dc.identifier.urihttp://hdl.handle.net/10754/625815
dc.description.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.
dc.description.sponsorshipThis work is supported by NIH 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 and the computational resources provided by XSEDE. An early version of this paper was submitted to and peer reviewed at the 2017 Annual International Conference on Research in Computational Molecular Biology (RECOMB). The manuscript was revised and then independently further reviewed at Cell Systems.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S2405471217303897
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Cell Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Cell Systems, [, , (2017-09-27)] DOI: 10.1016/j.cels.2017.09.001 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectmembrane protein folding
dc.subjectmembrane protein contact prediction
dc.subjectdeep learning
dc.subjectdeep transfer learning
dc.subjecthomology modeling
dc.subjectco-evolution analysis
dc.subjectmultiple sequence alignment
dc.titleFolding Membrane Proteins by Deep Transfer Learning
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalCell Systems
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Human Genetics, University of Chicago, Chicago, IL 60637, USA
dc.contributor.institutionToyota Technological Institute at Chicago, Chicago, IL 60637, USA
dc.contributor.institutionDepartment of Computer Science, University of Hong Kong, Hong Kong
dc.identifier.arxividarXiv:1708.08407
kaust.personWang, Sheng
refterms.dateFOA2018-06-14T02:14:43Z


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