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Record W3159290358 · doi:10.1017/9781108526227.021

Tutorial on Coarse-Grained Molecular Dynamics with Peptides

2018· other· en· W3159290358 on OpenAlexaff
William Hoiles, Vikram Krishnamurthy, Bruce Cornell

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMolecular dynamicsForce field (fiction)Lipid bilayerMembraneConstruct (python library)ChemistryComputational chemistryNanotechnologyComputer scienceStatistical physicsMaterials sciencePhysicsArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

In this appendix we use GROMACS 5.1, PyMOL 1.7x, and VMD 1.9.3, together with the MARTINI force field to outline how to set up coarse-grained molecular dynamics (CGMD) simulations of a peptide. Our description includes how to construct the all-atom structure of the peptide, how to build all-atom models of bilayer lipid membranes, how to insert the peptides into the membrane, and how to construct publication-quality figures. The material in this appendix is relevant to Chapter 14 for constructing coarse-grained molecular dynamics simulations using the MARTINI force field. Note that performing CGMD simulations and MD simulations are identical except for the definitions of the bead-particle interactions and simulation time steps used. All the files associated with these tutorials are provided at www.cambridge.org/ engineered-artificial-membranes. Constructing the All-Atom and Coarse-Grained Structure of a Peptide This section illustrates how to construct the all-atom and coarse-grained structure of the antimicrobial PGLa peptide using VMD and PyMOL. Recall that molecular dynamics (MD) and PyMOL were discussed in Chapter 2, and PGLa was discussed in Chapter 10. The goal is to construct the all-atom and coarse-grained structures of PGLa illustrated in Figure B.1. To construct the all-atom coordinate structure of the PGLa peptide the following steps are used. (i)Obtain the amino-acid sequence of interest. For PGLa the amino acid sequence is: GMASKAGAIAGKIAKVALKAL-NH 2 . (ii) Open VMD, and proceed to Extensions → Modeling → Molfacture. Click the “Start Molefacture”, leaving the entry field blank. (iii) In Molefacture, Build→Protein Builder. Enter the amino-acid sequence of PGLa, and select the α -helix secondary structure (this is the expected secondary structure of PGLa in both the surface and transmembrane configuration as discussed in Chapter 14). Now click build. (iv) After pressing build, the all-atom structure of PGLa will appear in the VMD display. In VMD Main, select File→Save Coordinates, and save the all-atom PGLa structure as PGLa.pdb. The file PGLa.pdb contains the all-atom representation of the PGLa peptide in the protein data bank format. Notice that this PGLa.pdb file can be viewed in both VMD and PyMOL. Having constructed the PGLa.pdb coordinate file, we now map the all-atom structure into a coarse-grained structure for use with the MARTINI force field. This mapping operation is performed using the martinize.py python script (available from http://md.chem.rug.nl/cgmartini/index.php/home).

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)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.129
Threshold uncertainty score0.432

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1290.047

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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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
GenreOther

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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