The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Measuring Protein Concentrations by NMR Spectroscopy
Abstract
In applications of NMR to biological macromolecules in solution, the concentration of the NMR sample is an important parameter describing the sample and providing information for the selection and planning of experiments. Although concentrations can be measured directly by NMR spectroscopy, other methods are usually preferred to measure the concentration of macromolecules in NMR samples. The reasons are the difficulties in the correlation of the sample of interest with the signal intensity representing a known concentration. This correlation is usually obtained by adding to the sample a reference compound with known concentration and comparing the integral over resolved resonance lines of the molecules with known and unknown concentrations. For solutions of biological macromolecules it is very difficult to find a compound that does not interact with the macromolecules and has a resonance outside their spectral range. We introduce PULCON which is a method that correlates the absolute intensities of two spectra measured in different solution conditions. PULCON is easy to implement and apply on all NMR spectrometers; it does not need any special hardware or software. PULCON is very robust and at the same time delivers accurate concentrations of samples in the NMR tube. We demonstrate that PULCON has the potential to replace UV spectroscopy for concentration measurements of NMR samples.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Bradford, M. M. (1976) · Analytical Biochemistry · doi:10.1016/0003-2697(76)90527-3 · Cited by 255,302
NMR with Proteins and Nucleic Acids
Wüthrich, K. (1986) · Europhysics News · doi:10.1051/epn/19861701011 · Cited by 7,989
Gradient-tailored excitation for single-quantum NMR spectroscopy of aqueous solutions
Piotto, M., Saudek, V., & Sklenář, V. (1992) · Journal of Biomolecular NMR · doi:10.1007/bf02192855 · Cited by 3,712
The principle of reciprocity in signal strength calculations?A mathematical guide
Hoult, D. I. (2000) · Concepts in Magnetic Resonance · doi:10.1002/1099-0534(2000)12:4<173::aid-cmr1>3.0.co;2-q · Cited by 550
Concentration Measurement by Proton NMR Using the ERETIC Method
Akoka, S., Barantin, L., & Trierweiler, M. (1999) · Analytical Chemistry · doi:10.1021/ac981422i · Cited by 519
Quantitative1H NMR: Development and Potential of a Method for Natural Products Analysis
Pauli, G. F., Jaki, B. U., & Lankin, D. C. (2005) · Journal of Natural Products · doi:10.1021/np0497301 · Cited by 518
Attention-Based Motion Perception
Cavanagh, P. (1992) · Science · doi:10.1126/science.1523411 · Cited by 477
NMR Determination of Residual Structure in a Urea-Denatured Protein, the 434-Repressor
Neri, D., Billeter, M., Wider, G., & Wüthrich, K. (1992) · Science · doi:10.1126/science.1523410 · Cited by 448
Self-citationA new method for absolute quantitation MRS metabolites
Barantin, L., Pape, A. L., & Akoka, S. (1997) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910380203 · Cited by 159
Wider, G. (1998) · Progress in Nuclear Magnetic Resonance Spectroscopy · doi:10.1016/s0079-6565(98)00014-4 · Cited by 109
Self-citation
Cited by
Quantitative 1H NMR spectroscopy
Bharti, S. K., & Roy, R. (2012) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2012.02.007 · Cited by 799
Würtz, P., Kangas, A. J., Soininen, P., Lawlor, D. A., Davey Smith, G., & Ala-Korpela, M. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx016 · Cited by 686
An EB1-binding motif acts as a microtubule tip localization signal
Honnappa, S., Gouveia, S. M., Weisbrich, A., Damberger, F. F., Bhavesh, N. S., Jawhari, H., Grigoriev, I., van Rijssel, F. J. A., Buey, R. M., Lawera, A., et al. (2009) · Cell · doi:10.1016/j.cell.2009.04.065 · Cited by 611
Pauli, G. F., Chen, S.-N., Simmler, C., Lankin, D. C., Gödecke, T., Jaki, B. U., Friesen, J. B., McAlpine, J. B., & Napolitano, J. G. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm500734a · Cited by 398
High‐Throughput Metabolomics by 1D NMR
Vignoli, A., Ghini, V., Meoni, G., Licari, C., Takis, P. G., Tenori, L., Turano, P., & Luchinat, C. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201804736 · Cited by 365
Metadata
- Authors
- Wider, G., & Dreier, L.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2006-02-04
- Volume
- 128
- Issue
- 8
- Pages
- 2571-2576
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2037723020
- Cited by
- 461
- References
- 10
- FWCI
- 2.66
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
