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• W2894624059 abstract "At resolutions worse than 3.5 Å, the electron density is weak or nonexistent at the locations of the side chains. Consequently, the assignment of the protein sequences to their correct positions along the backbone is a difficult problem. In this work, we propose a fully automated computational approach to assign sequence at low resolution. It is based on our surprising observation that standard reciprocal-space indicators, such as the initial unrefined R value, are sensitive enough to detect an erroneous sequence assignment of even a single backbone position. Our approach correctly determines the amino acid type for 15%, 13%, and 9% of the backbone positions in crystallographic datasets with resolutions of 4.0 Å, 4.5 Å, and 5.0 Å, respectively. We implement these findings in an application for threading a sequence onto a backbone structure. For the three resolution ranges, the application threads 83%, 81%, and 64% of the sequences exactly as in the deposited PDB structures." @default.
• W2894624059 created "2018-10-12" @default.
• W2894624059 creator A5014431161 @default.
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• W2894624059 date "2018-11-01" @default.
• W2894624059 modified "2023-10-17" @default.
• W2894624059 title "Automatic Inference of Sequence from Low-Resolution Crystallographic Data" @default.
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• W2894624059 doi "https://doi.org/10.1016/j.str.2018.08.011" @default.
• W2894624059 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6221995" @default.
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• W2894624059 hasPublicationYear "2018" @default.
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