Evidence map›Paper›PMID 32852098›Full record

ArticleAngewandte Chemie (International ed. in English)2020

Non-Stationary Complementary Non-Uniform Sampling (NOSCO NUS) for Fast Acquisition of Serial 2D NMR Titration Data.

Javier A Romero, Ewa K Nawrocka, Alexandra Shchukina, Francisco J Blanco, Tammo Diercks, Krzysztof Kazimierczuk

Abstract read
In one paragraph

Article in Angewandte Chemie (International ed. in English), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Javier A RomeroCentre of New Technologies, University of Warsaw, Banacha 2C, 02-097, Warsaw, Poland.
Ewa K NawrockaCentre of New Technologies, University of Warsaw, Banacha 2C, 02-097, Warsaw, Poland.ORCID 0000-0002-4801-7894
Alexandra ShchukinaFaculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Zwirki i Wigury 101, 02-089, Warsaw, Poland.ORCID 0000-0001-7273-217X
Francisco J BlancoStructural and Chemical Biology Department, Centro de Investigaciones Biológicas, CIB-CSIC, 28040, Madrid, Spain.ORCID 0000-0003-2545-4319
Tammo DiercksCIC bioGUNE, Parque Tecnológico de Bizkaia, Ed. 800, 48160-, Derio, Spain.ORCID 0000-0002-5200-0905
Krzysztof KazimierczukCentre of New Technologies, University of Warsaw, Banacha 2C, 02-097, Warsaw, Poland.ORCID 0000-0001-9585-1737

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

NMR spectroscopy offers unique benefits for ligand binding studies on isotopically labelled target proteins. These benefits include atomic resolution, direct distinction of binding sites and modes, a lowest detectable affinity limit, and function independent setup. Yet, retracing protein signal assignments from apo to holo states to derive exact dissociation constants and chemical shift perturbation amplitudes (for ligand docking and structure-based optimization) requires lengthy titration series of 2D heteronuclear correlation spectra at variable ligand concentration that may exceed the protein's lifetime and available spectrometer time. We present a novel method to overcome this critical limitation, based on non-stationary complementary non-uniform sampling (NOSCO NUS) combined with a robust particle swarm optimization algorithm. We illustrate its potential in two challenging studies with very distinct protein sizes and binding affinities, showing that NOSCO NUS can reduce measurement times by an order of magnitude to make such highly informative NMR titration studies more broadly feasible.

Indexed as

compressed sensingligand bindingNMR spectroscopynon-uniform samplingtitration

Identifiers

PMID32852098
PMCPMC7756666

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Registered trials

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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.