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Add scVelo RNA velocity analysis workflow and IQ-TREE reference documentation

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- Introduced a comprehensive RNA velocity analysis pipeline using scVelo, including data loading, preprocessing, velocity estimation, and visualization.
- Added a script for running RNA velocity analysis with customizable parameters and output options.
- Created detailed documentation for IQ-TREE 2 phylogenetic inference, covering command syntax, model selection, bootstrapping methods, and output interpretation.
- Included references for velocity models and their mathematical framework, along with a comparison of different models.
- Enhanced the scVelo skill documentation with installation instructions, use cases, and best practices for RNA velocity analysis.
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---
name: molecular-dynamics
description: Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.
license: MIT
metadata:
skill-author: Kuan-lin Huang
---
# Molecular Dynamics
## Overview
Molecular dynamics (MD) simulation computationally models the time evolution of molecular systems by integrating Newton's equations of motion. This skill covers two complementary tools:
- **OpenMM** (https://openmm.org/): High-performance MD simulation engine with GPU support, Python API, and flexible force field support
- **MDAnalysis** (https://mdanalysis.org/): Python library for reading, writing, and analyzing MD trajectories from all major simulation packages
**Installation:**
```bash
conda install -c conda-forge openmm mdanalysis nglview
# or
pip install openmm mdanalysis
```
## When to Use This Skill
Use molecular dynamics when:
- **Protein stability analysis**: How does a mutation affect protein dynamics?
- **Drug binding simulations**: Characterize binding mode and residence time of a ligand
- **Conformational sampling**: Explore protein flexibility and conformational changes
- **Protein-protein interaction**: Model interface dynamics and binding energetics
- **RMSD/RMSF analysis**: Quantify structural fluctuations from a reference structure
- **Free energy estimation**: Compute binding free energy or conformational free energy
- **Membrane simulations**: Model proteins in lipid bilayers
- **Intrinsically disordered proteins**: Study IDR conformational ensembles
## Core Workflow: OpenMM Simulation
### 1. System Preparation
```python
from openmm.app import *
from openmm import *
from openmm.unit import *
import sys
def prepare_system_from_pdb(pdb_file, forcefield_name="amber14-all.xml",
water_model="amber14/tip3pfb.xml"):
"""
Prepare an OpenMM system from a PDB file.
Args:
pdb_file: Path to cleaned PDB file (use PDBFixer for raw PDB files)
forcefield_name: Force field XML file
water_model: Water model XML file
Returns:
pdb, forcefield, system, topology
"""
# Load PDB
pdb = PDBFile(pdb_file)
# Load force field
forcefield = ForceField(forcefield_name, water_model)
# Add hydrogens and solvate
modeller = Modeller(pdb.topology, pdb.positions)
modeller.addHydrogens(forcefield)
# Add solvent box (10 Å padding, 150 mM NaCl)
modeller.addSolvent(
forcefield,
model='tip3p',
padding=10*angstroms,
ionicStrength=0.15*molar
)
print(f"System: {modeller.topology.getNumAtoms()} atoms, "
f"{modeller.topology.getNumResidues()} residues")
# Create system
system = forcefield.createSystem(
modeller.topology,
nonbondedMethod=PME, # Particle Mesh Ewald for long-range electrostatics
nonbondedCutoff=1.0*nanometer,
constraints=HBonds, # Constrain hydrogen bonds (allows 2 fs timestep)
rigidWater=True,
ewaldErrorTolerance=0.0005
)
return modeller, system
```
### 2. Energy Minimization
```python
from openmm.app import *
from openmm import *
from openmm.unit import *
def minimize_energy(modeller, system, output_pdb="minimized.pdb",
max_iterations=1000, tolerance=10.0):
"""
Energy minimize the system to remove steric clashes.
Args:
modeller: Modeller object with topology and positions
system: OpenMM System
output_pdb: Path to save minimized structure
max_iterations: Maximum minimization steps
tolerance: Convergence criterion in kJ/mol/nm
Returns:
simulation object with minimized positions
"""
# Set up integrator (doesn't matter for minimization)
integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds)
# Create simulation
# Use GPU if available (CUDA or OpenCL), fall back to CPU
try:
platform = Platform.getPlatformByName('CUDA')
properties = {'DeviceIndex': '0', 'Precision': 'mixed'}
except Exception:
try:
platform = Platform.getPlatformByName('OpenCL')
properties = {}
except Exception:
platform = Platform.getPlatformByName('CPU')
properties = {}
simulation = Simulation(
modeller.topology, system, integrator,
platform, properties
)
simulation.context.setPositions(modeller.positions)
# Check initial energy
state = simulation.context.getState(getEnergy=True)
print(f"Initial energy: {state.getPotentialEnergy()}")
# Minimize
simulation.minimizeEnergy(
tolerance=tolerance*kilojoules_per_mole/nanometer,
maxIterations=max_iterations
)
state = simulation.context.getState(getEnergy=True, getPositions=True)
print(f"Minimized energy: {state.getPotentialEnergy()}")
# Save minimized structure
with open(output_pdb, 'w') as f:
PDBFile.writeFile(simulation.topology, state.getPositions(), f)
return simulation
```
### 3. NVT Equilibration
```python
from openmm.app import *
from openmm import *
from openmm.unit import *
def run_nvt_equilibration(simulation, n_steps=50000, temperature=300,
report_interval=1000, output_prefix="nvt"):
"""
NVT equilibration: constant N, V, T.
Equilibrate velocities to target temperature.
Args:
simulation: OpenMM Simulation (after minimization)
n_steps: Number of MD steps (50000 × 2fs = 100 ps)
temperature: Temperature in Kelvin
report_interval: Steps between data reports
output_prefix: File prefix for trajectory and log
"""
# Add position restraints for backbone during NVT
# (Optional: restraint heavy atoms)
# Set temperature
simulation.context.setVelocitiesToTemperature(temperature*kelvin)
# Add reporters
simulation.reporters = []
# Log file
simulation.reporters.append(
StateDataReporter(
f"{output_prefix}_log.txt",
report_interval,
step=True,
potentialEnergy=True,
kineticEnergy=True,
temperature=True,
volume=True,
speed=True
)
)
# DCD trajectory (compact binary format)
simulation.reporters.append(
DCDReporter(f"{output_prefix}_traj.dcd", report_interval)
)
print(f"Running NVT equilibration: {n_steps} steps ({n_steps*2/1000:.1f} ps)")
simulation.step(n_steps)
print("NVT equilibration complete")
return simulation
```
### 4. NPT Equilibration and Production
```python
def run_npt_production(simulation, n_steps=500000, temperature=300, pressure=1.0,
report_interval=5000, output_prefix="npt"):
"""
NPT production run: constant N, P, T.
Args:
n_steps: Production steps (500000 × 2fs = 1 ns)
temperature: Temperature in Kelvin
pressure: Pressure in bar
report_interval: Steps between reports
"""
# Add Monte Carlo barostat for pressure control
system = simulation.context.getSystem()
system.addForce(MonteCarloBarostat(pressure*bar, temperature*kelvin, 25))
simulation.context.reinitialize(preserveState=True)
# Update reporters
simulation.reporters = []
simulation.reporters.append(
StateDataReporter(
f"{output_prefix}_log.txt",
report_interval,
step=True,
potentialEnergy=True,
temperature=True,
density=True,
speed=True
)
)
simulation.reporters.append(
DCDReporter(f"{output_prefix}_traj.dcd", report_interval)
)
# Save checkpoints
simulation.reporters.append(
CheckpointReporter(f"{output_prefix}_checkpoint.chk", 50000)
)
print(f"Running NPT production: {n_steps} steps ({n_steps*2/1000000:.2f} ns)")
simulation.step(n_steps)
print("Production MD complete")
return simulation
```
## Trajectory Analysis with MDAnalysis
### 1. Load Trajectory
```python
import MDAnalysis as mda
from MDAnalysis.analysis import rms, align, contacts
import numpy as np
import matplotlib.pyplot as plt
def load_trajectory(topology_file, trajectory_file):
"""
Load an MD trajectory with MDAnalysis.
Args:
topology_file: PDB, PSF, or other topology file
trajectory_file: DCD, XTC, TRR, or other trajectory
"""
u = mda.Universe(topology_file, trajectory_file)
print(f"Universe: {u.atoms.n_atoms} atoms, {u.trajectory.n_frames} frames")
print(f"Time range: 0 to {u.trajectory.totaltime:.0f} ps")
return u
```
### 2. RMSD Analysis
```python
def compute_rmsd(u, selection="backbone", reference_frame=0):
"""
Compute RMSD of selected atoms relative to reference frame.
Args:
u: MDAnalysis Universe
selection: Atom selection string (MDAnalysis syntax)
reference_frame: Frame index for reference structure
Returns:
numpy array of (time, rmsd) values
"""
# Align trajectory to minimize RMSD
aligner = align.AlignTraj(u, u, select=selection, in_memory=True)
aligner.run()
# Compute RMSD
R = rms.RMSD(u, select=selection, ref_frame=reference_frame)
R.run()
rmsd_data = R.results.rmsd # columns: frame, time, RMSD
return rmsd_data
def plot_rmsd(rmsd_data, title="RMSD over time", output_file="rmsd.png"):
"""Plot RMSD over simulation time."""
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(rmsd_data[:, 1] / 1000, rmsd_data[:, 2], 'b-', linewidth=0.5)
ax.set_xlabel("Time (ns)")
ax.set_ylabel("RMSD (Å)")
ax.set_title(title)
ax.axhline(rmsd_data[:, 2].mean(), color='r', linestyle='--',
label=f'Mean: {rmsd_data[:, 2].mean():.2f} Å')
ax.legend()
plt.tight_layout()
plt.savefig(output_file, dpi=150)
return fig
```
### 3. RMSF Analysis (Per-Residue Flexibility)
```python
def compute_rmsf(u, selection="backbone", start_frame=0):
"""
Compute per-residue RMSF (flexibility).
Returns:
resids, rmsf_values arrays
"""
# Select atoms
atoms = u.select_atoms(selection)
# Compute RMSF
R = rms.RMSF(atoms)
R.run(start=start_frame)
# Average by residue
resids = []
rmsf_per_res = []
for res in u.select_atoms(selection).residues:
res_atoms = res.atoms.intersection(atoms)
if len(res_atoms) > 0:
resids.append(res.resid)
rmsf_per_res.append(R.results.rmsf[res_atoms.indices].mean())
return np.array(resids), np.array(rmsf_per_res)
```
### 4. Protein-Ligand Contacts
```python
def analyze_contacts(u, protein_sel="protein", ligand_sel="resname LIG",
radius=4.5, start_frame=0):
"""
Track protein-ligand contacts over trajectory.
Args:
radius: Contact distance cutoff in Angstroms
"""
protein = u.select_atoms(protein_sel)
ligand = u.select_atoms(ligand_sel)
contact_frames = []
for ts in u.trajectory[start_frame:]:
# Find protein atoms within radius of ligand
distances = contacts.contact_matrix(
protein.positions, ligand.positions, radius
)
contact_residues = set()
for i in range(distances.shape[0]):
if distances[i].any():
contact_residues.add(protein.atoms[i].resid)
contact_frames.append(contact_residues)
return contact_frames
```
## Force Field Selection Guide
| System | Recommended Force Field | Water Model |
|--------|------------------------|-------------|
| Standard proteins | AMBER14 (`amber14-all.xml`) | TIP3P-FB |
| Proteins + small molecules | AMBER14 + GAFF2 | TIP3P-FB |
| Membrane proteins | CHARMM36m | TIP3P |
| Nucleic acids | AMBER99-bsc1 or AMBER14 | TIP3P |
| Disordered proteins | ff19SB or CHARMM36m | TIP3P |
## System Preparation Tools
### PDBFixer (for raw PDB files)
```python
from pdbfixer import PDBFixer
from openmm.app import PDBFile
def fix_pdb(input_pdb, output_pdb, ph=7.0):
"""Fix common PDB issues: missing residues, atoms, add H, standardize."""
fixer = PDBFixer(filename=input_pdb)
fixer.findMissingResidues()
fixer.findNonstandardResidues()
fixer.replaceNonstandardResidues()
fixer.removeHeterogens(True) # Remove water/ligands
fixer.findMissingAtoms()
fixer.addMissingAtoms()
fixer.addMissingHydrogens(ph)
with open(output_pdb, 'w') as f:
PDBFile.writeFile(fixer.topology, fixer.positions, f)
return output_pdb
```
### GAFF2 for Small Molecules (via OpenFF Toolkit)
```python
# For ligand parameterization, use OpenFF toolkit or ACPYPE
# pip install openff-toolkit
from openff.toolkit import Molecule, ForceField as OFFForceField
from openff.interchange import Interchange
def parameterize_ligand(smiles, ff_name="openff-2.0.0.offxml"):
"""Generate GAFF2/OpenFF parameters for a small molecule."""
mol = Molecule.from_smiles(smiles)
mol.generate_conformers(n_conformers=1)
off_ff = OFFForceField(ff_name)
interchange = off_ff.create_interchange(mol.to_topology())
return interchange
```
## Best Practices
- **Always minimize before MD**: Raw PDB structures have steric clashes
- **Equilibrate before production**: NVT (50–100 ps) → NPT (100–500 ps) → Production
- **Use GPU**: Simulations are 10–100× faster on GPU (CUDA/OpenCL)
- **2 fs timestep with HBonds constraints**: Standard; use 4 fs with HMR (hydrogen mass repartitioning)
- **Analyze only equilibrated trajectory**: Discard first 20–50% as equilibration
- **Save checkpoints**: MD runs can fail; checkpoints allow restart
- **Periodic boundary conditions**: Required for solvated systems
- **PME for electrostatics**: More accurate than cutoff methods for charged systems
## Additional Resources
- **OpenMM documentation**: https://openmm.org/documentation.html
- **MDAnalysis user guide**: https://docs.mdanalysis.org/
- **GROMACS** (alternative MD engine): https://manual.gromacs.org/
- **NAMD** (alternative): https://www.ks.uiuc.edu/Research/namd/
- **CHARMM-GUI** (web-based system builder): https://charmm-gui.org/
- **AmberTools** (free Amber tools): https://ambermd.org/AmberTools.php
- **OpenMM paper**: Eastman P et al. (2017) PLOS Computational Biology. PMID: 28278240
- **MDAnalysis paper**: Michaud-Agrawal N et al. (2011) J Computational Chemistry. PMID: 21500218

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# MDAnalysis Analysis Reference
## MDAnalysis Universe and AtomGroup
```python
import MDAnalysis as mda
# Load Universe
u = mda.Universe("topology.pdb", "trajectory.dcd")
# or for single structure:
u = mda.Universe("structure.pdb")
# Key attributes
print(u.atoms.n_atoms) # Total atoms
print(u.residues.n_residues) # Total residues
print(u.trajectory.n_frames) # Number of frames
print(u.trajectory.dt) # Time step in ps
print(u.trajectory.totaltime) # Total simulation time in ps
```
## Atom Selection Language
MDAnalysis uses a rich selection language:
```python
# Basic selections
protein = u.select_atoms("protein")
backbone = u.select_atoms("backbone") # CA, N, C, O
calpha = u.select_atoms("name CA")
water = u.select_atoms("resname WAT or resname HOH or resname TIP3")
ligand = u.select_atoms("resname LIG")
# By residue number
region = u.select_atoms("resid 10:50")
specific = u.select_atoms("resid 45 and name CA")
# By proximity
near_ligand = u.select_atoms("protein and around 5.0 resname LIG")
# By property
charged = u.select_atoms("resname ARG LYS ASP GLU")
hydrophobic = u.select_atoms("resname ALA VAL LEU ILE PRO PHE TRP MET")
# Boolean combinations
active_site = u.select_atoms("(resid 100 102 145 200) and protein")
# Inverse
not_water = u.select_atoms("not (resname WAT HOH)")
```
## Common Analysis Modules
### RMSD and RMSF
```python
from MDAnalysis.analysis import rms, align
# Align trajectory to first frame
align.AlignTraj(u, u, select='backbone', in_memory=True).run()
# RMSD
R = rms.RMSD(u, u, select='backbone', groupselections=['name CA'])
R.run()
# R.results.rmsd: shape (n_frames, 3) = [frame, time, RMSD]
# RMSF (per-atom fluctuations)
from MDAnalysis.analysis.rms import RMSF
rmsf = RMSF(u.select_atoms('backbone')).run()
# rmsf.results.rmsf: per-atom RMSF values in Angstroms
```
### Radius of Gyration
```python
rg = []
for ts in u.trajectory:
rg.append(u.select_atoms("protein").radius_of_gyration())
import numpy as np
print(f"Mean Rg: {np.mean(rg):.2f} Å")
```
### Secondary Structure Analysis
```python
from MDAnalysis.analysis.dssp import DSSP
# DSSP secondary structure assignment per frame
dssp = DSSP(u).run()
# dssp.results.dssp: per-residue per-frame secondary structure codes
# H = alpha-helix, E = beta-strand, C = coil
```
### Hydrogen Bonds
```python
from MDAnalysis.analysis.hydrogenbonds import HydrogenBondAnalysis
hbonds = HydrogenBondAnalysis(
u,
donors_sel="protein and name N",
acceptors_sel="protein and name O",
d_h_cutoff=1.2, # donor-H distance (Å)
d_a_cutoff=3.0, # donor-acceptor distance (Å)
d_h_a_angle_cutoff=150 # D-H-A angle (degrees)
)
hbonds.run()
# Count H-bonds per frame
import pandas as pd
df = pd.DataFrame(hbonds.results.hbonds,
columns=['frame', 'donor_ix', 'hydrogen_ix', 'acceptor_ix',
'DA_dist', 'DHA_angle'])
```
### Principal Component Analysis (PCA)
```python
from MDAnalysis.analysis import pca
pca_analysis = pca.PCA(u, select='backbone', align=True).run()
# PC variances
print(pca_analysis.results.variance[:5]) # % variance of first 5 PCs
# Project trajectory onto PCs
projected = pca_analysis.transform(u.select_atoms('backbone'), n_components=3)
# Shape: (n_frames, n_components)
```
### Free Energy Surface (FES)
```python
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
def plot_free_energy_surface(x, y, bins=50, T=300, xlabel="PC1", ylabel="PC2",
output="fes.png"):
"""
Compute 2D free energy surface from two order parameters.
FES = -kT * ln(P(x,y))
"""
kB = 0.0083144621 # kJ/mol/K
kT = kB * T
# 2D histogram
H, xedges, yedges = np.histogram2d(x, y, bins=bins, density=True)
H = H.T
# Free energy
H_safe = np.where(H > 0, H, np.nan)
fes = -kT * np.log(H_safe)
fes -= np.nanmin(fes) # Shift minimum to 0
# Plot
fig, ax = plt.subplots(figsize=(8, 6))
im = ax.contourf(xedges[:-1], yedges[:-1], fes, levels=20, cmap='RdYlBu_r')
plt.colorbar(im, ax=ax, label='Free Energy (kJ/mol)')
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
plt.savefig(output, dpi=150, bbox_inches='tight')
return fig
```
## Trajectory Formats Supported
| Format | Extension | Notes |
|--------|-----------|-------|
| DCD | `.dcd` | CHARMM/NAMD binary; widely used |
| XTC | `.xtc` | GROMACS compressed |
| TRR | `.trr` | GROMACS full precision |
| NetCDF | `.nc` | AMBER format |
| LAMMPS | `.lammpstrj` | LAMMPS dump |
| HDF5 | `.h5md` | H5MD standard |
| PDB | `.pdb` | Multi-model PDB |
## MDAnalysis Interoperability
```python
# Convert to numpy
positions = u.atoms.positions # Current frame: shape (N, 3)
# Write to PDB
with mda.Writer("frame_10.pdb", u.atoms.n_atoms) as W:
u.trajectory[10] # Move to frame 10
W.write(u.atoms)
# Write trajectory subset
with mda.Writer("protein_traj.dcd", u.select_atoms("protein").n_atoms) as W:
for ts in u.trajectory:
W.write(u.select_atoms("protein"))
# Convert to MDTraj (for compatibility)
# import mdtraj as md
# traj = md.load("trajectory.dcd", top="topology.pdb")
```
## Performance Tips
- **Use `in_memory=True`** for AlignTraj when RAM allows (much faster iteration)
- **Select minimal atoms** before analysis to reduce memory/compute
- **Use multiprocessing** for independent frame analyses
- **Process in chunks** for very long trajectories using `start`/`stop`/`step` parameters:
```python
# Analyze every 10th frame from frame 100 to 1000
R.run(start=100, stop=1000, step=10)
```