forcefield_selection_skill

This skill recommends force fields and water models for diverse molecular systems in MD simulations, guiding users to optimal parameter choices and validation
  • Python

0

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill charleshahn/duivyagent --skill forcefield_selection

  • SKILL.md7.8 KB

Overview

This skill recommends suitable force fields and water models for molecular dynamics (MD) simulations across diverse systems. It covers proteins, nucleic acids, lipids, organic small molecules, carbohydrates, inorganic ions, and coarse-grained systems. Use it to get concise, validated pairings and practical guidance for initial simulation setup.

How this skill works

Given a description of your system (molecule types, scale, required accuracy, and computational budget), the skill returns recommended force fields and matching water models. It highlights common versions, when to prefer coarse-grained vs all-atom approaches, and compatibility caveats. It also suggests a short validation workflow to confirm the choice with quick test simulations.

When to use it

  • Selecting a force field for a new MD project (protein, lipid, small molecule, carbohydrate, or ion solution).
  • Choosing a water model that pairs well with your chosen force field and goals.
  • Deciding between all-atom and coarse-grained models for large or long-timescale simulations.
  • Preparing drug–protein binding studies or membrane protein simulations.
  • Validating force field choices against literature or experimental data.

Best practices

  • Match force fields and water models that are known to be compatible (e.g., CHARMM36 + TIP3P, AMBER + TIP3P/TIP4P).
  • Prefer force fields validated in literature for the same system type and similar conditions.
  • For small molecules, use parameter sets derived for your main force field (GAFF for AMBER, CGenFF for CHARMM, OPLS parameters for OPLS-AA).
  • Run short test simulations (energy minimization + 1–10 ns equilibration) to check stability before full production runs.
  • Consider computational cost: use Martini coarse-graining for large-scale or long-timescale membrane/assembly simulations; use all-atom for detailed interactions.

Example use cases

  • Protein folding/dynamics: AMBER14SB/19SB with TIP3P or TIP4P for higher water accuracy.
  • Membrane protein in bilayer: CHARMM36 for lipids + TIP3P for water and CHARMM-compatible lipid parameters.
  • Drug–protein binding: OPLS-AA or AMBER+GAFF for small molecules, TIP3P water, and validate ligand parameters.
  • Carbohydrate-rich system: GLYCAM for sugars or CHARMM36 when sugars interact with proteins/membranes.
  • Ion solutions or inorganic salts: CL&P or JC force fields with SPC/E or TIP4P water for accurate ion hydration.

FAQ

Mixing force fields is generally discouraged. If mixing is unavoidable, ensure compatibility, document adjustments, and validate with test simulations against known properties.

Which water model should I choose by default?

TIP3P is the most commonly used default for biomolecular simulations. Use TIP4P/TIP5P for higher water accuracy or SPC/E for ionic solutions when literature supports it.

How do I validate a chosen force field?

Check literature for similar systems, run short equilibration runs to monitor structural stability and energy behavior, and compare key observables to experimental data or published simulations.

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