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Record W2951531210 · doi:10.1021/ar700189y

Probing Invisible, Low-Populated States of Protein Molecules by Relaxation Dispersion NMR Spectroscopy: An Application to Protein Folding

2008· review· en· W2951531210 on OpenAlexaff
Dmitry M. Korzhnev, Lewis E. Kay

Bibliographic record

VenueAccounts of Chemical Research · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExcited stateChemistryRelaxation (psychology)Chemical physicsNuclear magnetic resonance spectroscopySpectroscopyProtein dynamicsBiomoleculeFolding (DSP implementation)MoleculePicosecondMolecular dynamicsComputational chemistryAtomic physicsPhysicsStereochemistry

Abstract

fetched live from OpenAlex

Biological function depends on molecular dynamics that lead to excursions from highly populated ground states to much less populated excited states. The low populations and the transient formation of such excited states render them invisible to the conventional methods of structural biology. Thus, while detailed pictures of ground-state structures of biomolecules have emerged over the years, largely through X-ray diffraction and solution nuclear magnetic resonance (NMR) spectroscopy studies, much less structural data has been accumulated on the conformational properties of the invisible excited states that are necessary to fully explain function. NMR spectroscopy is a powerful tool for studying conformational dynamics because it is sensitive to dynamics over a wide range of time scales, extending from picoseconds to seconds and because information is, in principle, available at nearly every position in the molecule. Here an NMR method for quantifying millisecond time scale dynamics that involve transitions between different molecular conformations is described. The basic experimental approach, termed relaxation dispersion NMR spectroscopy, is outlined to provide the reader with an intuitive feel for the technology. A variety of different experiments that probe conformational exchange at different sites in proteins are described, including a brief summary of data-fitting procedures to extract both the kinetic and thermodynamic properties of the exchange process and the structural features of the invisible excited states along the exchange pathway. It is shown that the methodology facilitates detection of intermediates and other excited states that are populated at low levels, 0.5% or higher, that cannot be observed directly in spectra, so long as they exchange with the observable ground state of the protein on the millisecond time scale. The power of the methodology is illustrated by a detailed application to the study of protein folding of the small modular SH3 domain. The kinetics and thermodynamics that describe the folding of this domain have been characterized through the effects of temperature, pressure, side-chain deuteration, and mutation, and the structural features of a low-populated folding intermediate have been assessed. Despite the fact that many previous studies have shown that SH3 domains fold via a two-state mechanism, the NMR methods presented unequivocally establish the presence of an on-pathway folding intermediate. The unique capabilities of NMR relaxation dispersion follow from the fact that large numbers of residues can be probed individually in a single experiment. By contrast, many other forms of spectroscopy monitor properties that are averaged over all residues in the molecule or that make use of only one or two reporters. The NMR methodology is not limited to protein folding, and applications to enzymatic catalysis, binding, and molecular recognition are beginning to emerge.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.360
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations268
Published2008
Admission routes1
Has abstractyes

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