Cloud Based Jupyter Tutorial: Recording Data Provenance with aiida-gromacs - Coarse-grained Simulation of membrane embedded GPCR

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Tutorial with pre-loaded user environments for using aiida-gromacs to produce provenance workflows for biomolecular simulations. This Jupyter Notebook (.ipynb) tutorial follows the steps to embed a receptor protein in a lipid membrane. This example will guide an experienced user through the process of downloading, tidying up, orientating, coarse-graining, embedding, and solvating a receptor protein, ready for simulation. Each step will be wrapped in aiida-gromacs commands to capture its inputs and outputs.

This resource is part of the BioSim (Biomolecular Simulations) Data Resources resource theme.

Creators

James Gebbie-Rayet & Jas Kalayan

Qualified Attribution

Publisher

PSDI

Access

Restricted Access

License

MIT

Contact

support@psdi.ac.uk

Citation

Please cite: James Gebbie-Rayet and Jas Kalayan. Cloud Based Jupyter Tutorial: Recording Data Provenance with aiida-gromacs - Coarse-grained Simulation of membrane embedded GPCR. Online. 23 May 2024. Available from: https://resources.psdi.ac.uk/guidance/fc34eacc-04c4-44dc-b4c9-28c3e3192d42. [accessed YYYY-MM-DD].

Keywords and Subjects

PSDI-pathfinder
molecular dynamics
biomolecular simulation
trajectory
gromacs
protein dynamics
drug discovery
pdb
biomolecules
proteins
dna
membranes
provenance
enhanced sampling
metadynamics
free energy
alchemical free energy
protein folding
alphafold
data storage
structural biology
computational biology
computational biochemistry
computational chemistry
aiida-plugin
aiida-gromacs
simulation provenance
data provenance