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Volumetric Properties of Proteins

2003· review· en· W2164513199 on OpenAlexaff
Tigran V. Chalikian

Bibliographic record

VenueAnnual Review of Biophysics and Biomolecular Structure · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProtein foldingFolding (DSP implementation)Protein structureNucleic acidIntrinsically disordered proteinsComputational biologyChemistryProtein aggregationIntermolecular forceBiophysicsNanotechnologyBiologyBiochemistryMaterials scienceMoleculeEngineering

Abstract

fetched live from OpenAlex

Structural and thermodynamic characterizations of a variety of intra- and intermolecular interactions stabilizing/destabilizing protein systems represent a major part of multidisciplinary efforts aimed at solving the problems of protein folding and binding. To this end, volumetric techniques have been successfully used to gain insights into protein hydration and intraglobular packing. Despite the fact that the use of volumetric measurements in protein-related studies dates back to the 1950s, such measurements still represent a relatively untapped yet potentially informative means for tackling the problems of protein folding and binding. This notion has been further emphasized by recent advances in the development of highly sensitive volumetric instrumentation that has led to intensifying volumetric investigations of protein systems. This paper reviews the volumetric properties of proteins and their low-molecular-weight analogs, in particular, discussing the recent progress in the use of volumetric data for studying conformational transitions of proteins as well as protein-ligand, protein-protein, and protein-nucleic acid interactions.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.009
GPT teacher head0.266
Teacher spread0.257 · 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 designNot applicable
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

Citations327
Published2003
Admission routes1
Has abstractyes

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