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Record W3146979743 · doi:10.5488/cmp.10.3.441

Towards coarse-grained modelling of proteins

2007· article· en· W3146979743 on OpenAlexaff
Stepanova

Bibliographic record

VenueCondensed Matter Physics · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of AlbertaNational Institute for Nanotechnology
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This paper introduces a basic theoretical background to the description of conformational dynamics of proteins through a system of interacting domains. The essential collective degrees of freedom derived by principal component analysis of a molecular dynamics trajectory are used as dynamic variables defining the projection operator technique that underlies the formalism suggested. The explicit form of the corresponding projection operator is obtained, and the projection method is employed to derive systems of coupled generalized Langevin equations for both individual atomic degrees of freedom and essential collective degrees of freedom in a protein. A definition of correlated domains in proteins is introduced based on the analysis of the essential dynamics. Examples of identification of such domains are presented. A system of coupled generalized Langevin equations is derived representing the protein through a few interacting domains embedded into a dissipative medium. Further developments and potential applications of the formalism are outlined.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2007
Admission routes1
Has abstractyes

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