ArticleJournal of the American Chemical Society2023
Fully Automated Characterization of Protein-Peptide Binding by Microfluidic 2D NMR.
Article in Journal of the American Chemical Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Peptides as programmable molecular scaffolds: from chemical synthesis and engineering to translational medicine.RSC chemical biology · 2026Review
- A native mass spectrometry approach to qualitatively elucidate interfacial epitopes of transient protein-protein interactions.Chemical communications (Cambridge, England) · 2024Article
- Broadband stripline Lenz lens achieves 11 × NMR signal enhancement.Scientific reports · 2024Article
- Artificial intelligence-driven shimming for parallel high field nuclear magnetic resonance.Scientific reports · 2023Article
- Chemical shift transfer: an effective strategy for protein NMR assignment with ARTINA.Frontiers in molecular biosciences · 2023Article
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
We demonstrate an automated microfluidic nuclear magnetic resonance (NMR) system that quantitatively characterizes protein-ligand interactions without user intervention and with minimal sample needs through protein-detected heteronuclear 2D NMR spectroscopy. Quantitation of protein-ligand interactions is of fundamental importance to the understanding of signaling and other life processes. As is well-known, NMR provides rich information both on the thermodynamics of binding and on the binding site. However, the required titrations are laborious and tend to require large amounts of sample, which are not always available. The present work shows how the analytical power of NMR detection can be brought in line with the trend of miniaturization and automation in life science workflows.
Indexed as
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.