ReviewJournal of magnetic resonance (San Diego, Calif. : 1997)2014
Sparse labeling of proteins: structural characterization from long range constraints.
Review in Journal of magnetic resonance (San Diego, Calif. : 1997), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- AssignSLP_GUI, a software tool exploiting AI for NMR resonance assignment of sparsely labeled proteins.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2022Article
- Sparse isotope labeling for nuclear magnetic resonance (NMR) of glycoproteins using 13C-glucose.Glycobiology · 2021Article
- An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling.Frontiers in molecular biosciences · 2021Article
- NMR Resonance Assignment Methodology: Characterizing Large Sparsely Labeled Glycoproteins.Journal of molecular biology · 2019Article
- Cysteine-ethylation of tissue-extracted membrane proteins as a tool to detect conformational states by solid-state NMR spectroscopy.Methods in enzymology · 2019Article
- Paramagnetic Tag for Glycosylation Sites in Glycoproteins: Structural Constraints on Heparan Sulfate Binding to Robo1.ACS chemical biology · 2018Article
- Structural Characterization of a Heparan Sulfate Pentamer Interacting with LAR-Ig1-2.Biochemistry · 2018Article
- Integrating NOE and RDC using sum-of-squares relaxation for protein structure determination.Journal of biomolecular NMR · 2017Article
- NMR assignments of sparsely labeled proteins using a genetic algorithm.Journal of biomolecular NMR · 2017Article
- Structural Aspects of Heparan Sulfate Binding to Robo1-Ig1-2.ACS chemical biology · 2016Article
- The Structural Role of Antibody N-Glycosylation in Receptor Interactions.Structure (London, England : 1993) · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Structural characterization of biologically important proteins faces many challenges associated with degradation of resolution as molecular size increases and loss of resolution improving tools such as perdeuteration when non-bacterial hosts must be used for expression. In these cases, sparse isotopic labeling (single or small subsets of amino acids) combined with long range paramagnetic constraints and improved computational modeling offer an alternative. This perspective provides a brief overview of this approach and two discussions of potential applications; one involving a very large system (an Hsp90 homolog) in which perdeuteration is possible and methyl-TROSY sequences can potentially be used to improve resolution, and one involving ligand placement in a glycosylated protein where resolution is achieved by single amino acid labeling (the sialyltransferase, ST6Gal1). This is not intended as a comprehensive review, but as a discussion of future prospects that promise impact on important questions in the structural biology area.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.