Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 223

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Solution NMR-derived global fold of a monomeric 82-kDa enzyme

Open access
Tugarinov, Vitali, Choy, Wing-Yiu, Orekhov, Vladislav Yu., Kay, Lewis E.·2005·Proceedings of the National Academy of Sciences

Abstract

The size of proteins that can be studied by solution NMR spectroscopy has increased significantly because of recent developments in methodology. Important experiments include those that make use of approaches that increase the lifetimes of NMR signals or that define the orientation of internuclear bond vectors with respect to a common molecular frame. The advances in NMR techniques are strongly coupled to isotope labeling methods that increase sensitivity and reduce the complexity of NMR spectra. We show that these developments can be exploited in structural studies of high-molecular-weight, single-polypeptide proteins, and we present the solution global fold of the monomeric 723-residue (82-kDa) enzyme malate synthase G from Escherichia coli , which has been extensively characterized by NMR in the past several years.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.0407792102

Cites

3 of 3 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 3 references

Cited by

See all 223

Metadata

Authors
Tugarinov, V., Choy, W.-Y., Orekhov, V. Y., & Kay, L. E.
Venue
Proceedings of the National Academy of Sciences
Published
2005-01-06
Volume
102
Issue
3
Pages
622-627
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2036664878
Cited by
223
References
3
FWCI
7.37
Type
journal article
Language
en
Open access
green

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Solution NMR-derived global fold of a monomeric 82-kDa enzyme. https://app.cochat.ai/papers/10.1073/pnas.0407792102
The paper
Tugarinov, V., Choy, W.-Y., Orekhov, V. Y., & Kay, L. E. (2005). Solution NMR-derived global fold of a monomeric 82-kDa enzyme. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0407792102

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.