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?:abstract
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Progress in cryo-electron microscopy (cryo-EM) has provided the potential for large-size protein structure determination. However, the solution rate for multi-domain proteins remains low due to the difficulty in modeling inter-domain orientations. We developed DEMO-EM, an automatic method to assemble multi-domain structures from cryo-EM maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep neural network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to twelve continuous and discontinuous domains with medium-to-low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (TM-score >0.5) for 98% of cases and significantly outperformed the state-of-the-art methods. DEMO-EM was applied to SARS-Cov-2 coronavirus genome and generated models with average TM-score/RMSD of 0.97/1.4Å to the deposited structures. These results demonstrated an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modeling with atomic-level accuracy from cryo-EM maps.
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?:doi
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?:doi
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10.1101/2020.10.15.340455
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document_parses/pdf_json/93e8a59a6453dda637ec7364e62809e78caaf86d.json; document_parses/pdf_json/63e2e4d114db7016bebb2a0d55b927d53411feb7.json; document_parses/pdf_json/be40415708b717414da36c42b9aae211404dc5b5.json
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document_parses/pmc_json/PMC7574260.xml.json
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?:title
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Progressive and accurate assembly of multi-domain protein structures from cryo-EM density maps
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