ArticleJournal of biomolecular NMR2014
Automated robust and accurate assignment of protein resonances for solid state NMR.
Article in Journal of biomolecular NMR, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Transition metal transporting P-type ATPases: terminal metal-binding domains serve as sensors for autoinhibitory tails.The FEBS journal · 2025Article
- Protein NMR assignment by isotope pattern recognition.Science advances · 2024Article
- Detecting and accounting for multiple sources of positional variance in peak list registration analysis and spin system grouping.Journal of biomolecular NMR · 2017Article
- On the problem of resonance assignments in solid state NMR of uniformly ¹⁵N,¹³C-labeled proteins.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2015Article
- MAS NMR of HIV-1 protein assemblies.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2015Article
- Towards automatic protein backbone assignment using proton-detected 4D solid-state NMR data.Journal of biomolecular NMR · 2014Article
- Rapid proton-detected NMR assignment for proteins with fast magic angle spinning.Journal of the American Chemical Society · 2014Article
- VirtualSpectrum, a tool for simulating peak list for multi-dimensional NMR spectra.Journal of biomolecular NMR · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The process of resonance assignment represents a time-consuming and potentially error-prone bottleneck in structural studies of proteins by solid-state NMR (ssNMR). Software for the automation of this process is therefore of high interest. Procedures developed through the last decades for solution-state NMR are not directly applicable for ssNMR due to the inherently lower data quality caused by lower sensitivity and broader lines, leading to overlap between peaks. Recently, the first efforts towards procedures specifically aimed for ssNMR have been realized (Schmidt et al. in J Biomol NMR 56(3):243-254, 2013). Here we present a robust automatic method, which can accurately assign protein resonances using peak lists from a small set of simple 2D and 3D ssNMR experiments, applicable in cases with low sensitivity. The method is demonstrated on three uniformly (13)C, (15)N labeled biomolecules with different challenges on the assignments. In particular, for the immunoglobulin binding domain B1 of streptococcal protein G automatic assignment shows 100% accuracy for the backbone resonances and 91.8% when including all side chain carbons. It is demonstrated, by using a procedure for generating artificial spectra with increasing line widths, that our method, GAMES_ASSIGN can handle a significant amount of overlapping peaks in the assignment. The impact of including different ssNMR experiments is evaluated as well.
Indexed as
Identifiers
24817190What 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.