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Record W2059289278 · doi:10.2202/1934-2659.1544

Coarse-Grained Molecular Dynamics Simulation of Lysozyme Protein Crystals

2011· article· en· W2059289278 on OpenAlexaff
Nafiseh Farhadian, Mojtaba Shariaty-Niassar, Kourosh Malek, Ali Maghari

Bibliographic record

VenueChemical Product and Process Modeling · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMolecular dynamicsGranularityProtein crystallizationLysozymeChemical physicsDiffusionMaterials scienceNanoporeStatistical physicsBiological systemCrystallographyChemistryComputational chemistryNanotechnologyThermodynamicsCrystallizationPhysicsComputer science

Abstract

fetched live from OpenAlex

Many biological phenomena of interest occur on a time scale that is too great to be studied by atomistic simulations. The use of coarse-graining methods to represent a system can alleviate this restriction by reducing the number of degrees of freedom thus extending the time and length scale in molecular modeling. Coarse-grained molecular dynamics (CGMD) technique was employed to simulate diffusion of water in the nanopores of lysozyme protein crystals. Good agreement was obtained between the atomistic and CG simulations in view of the stability of the protein crystal structure and water transport properties. Our simulations demonstrate that the CG method is a suitable technique for simulation the solvent diffusion process in the lysozyme protein crystal and also can be a good technique to predict the behavior of solvent and solutes in the biological systems at longer length and time scales.

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: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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