Ontology type: schema:ScholarlyArticle Open Access: True
2016-12
AUTHORSTeppei Ikeya, Tomomi Hanashima, Saori Hosoya, Manato Shimazaki, Shiro Ikeda, Masaki Mishima, Peter Güntert, Yutaka Ito
ABSTRACTInvestigating three-dimensional (3D) structures of proteins in living cells by in-cell nuclear magnetic resonance (NMR) spectroscopy opens an avenue towards understanding the structural basis of their functions and physical properties under physiological conditions inside cells. In-cell NMR provides data at atomic resolution non-invasively, and has been used to detect protein-protein interactions, thermodynamics of protein stability, the behavior of intrinsically disordered proteins, etc. in cells. However, so far only a single de novo 3D protein structure could be determined based on data derived only from in-cell NMR. Here we introduce methods that enable in-cell NMR protein structure determination for a larger number of proteins at concentrations that approach physiological ones. The new methods comprise (1) advances in the processing of non-uniformly sampled NMR data, which reduces the measurement time for the intrinsically short-lived in-cell NMR samples, (2) automatic chemical shift assignment for obtaining an optimal resonance assignment, and (3) structure refinement with Bayesian inference, which makes it possible to calculate accurate 3D protein structures from sparse data sets of conformational restraints. As an example application we determined the structure of the B1 domain of protein G at about 250 μM concentration in living E. coli cells. More... »
PAGES38312
http://scigraph.springernature.com/pub.10.1038/srep38312
DOIhttp://dx.doi.org/10.1038/srep38312
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PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/27910948
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"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1049594107"
]
}
],
"sameAs": [
"https://doi.org/10.1038/srep38312",
"https://app.dimensions.ai/details/publication/pub.1049594107"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T20:53",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8684_00000551.jsonl",
"type": "ScholarlyArticle",
"url": "http://www.nature.com/srep/2016/161202/srep38312/full/srep38312.html"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/srep38312'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/srep38312'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/srep38312'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/srep38312'
This table displays all metadata directly associated to this object as RDF triples.
334 TRIPLES
21 PREDICATES
86 URIs
37 LITERALS
25 BLANK NODES