Free-PROTAC molecular dynamics

Explicit-solvent MD of a generated PROTAC on its own, with no target protein and no E3 ligase. Runs in chloroform (a membrane-interior mimic) and in water, and measures the conformational descriptors published work ties to cell permeability: radius of gyration, 3D polar surface area, intramolecular H-bonds, and conformational state populations.

The protocol follows "Linker-Dependent Folding Rationalizes PROTAC Cell Permeability" (J. Med. Chem. 2022, DOI 10.1021/acs.jmedchem.2c00877) and takes its permeability cutoffs from Atilaw et al., ACS Med. Chem. Lett. 2021, 12, 107-114. reference_protocol.md has the settings and the target numbers.

Layout

protac_md.py               MD driver: conformer -> parametrize -> solvate -> replicas
analyze_conformations.py   Rg / RMSD / 3D PSA / H-bonds / clustering dashboard
rg_basins.py               Rg free-energy profiles and basin populations
psa_ensemble.py            3D PSA over a finished ensemble
ternary_span.py            warhead-to-E3 span distribution (water ensembles)
validate_psa.py            PSA code path checked against grazoprevir
psa_method_probe.py        element vs charge-threshold polar-atom selection
plot_results.py            per-replica summary figure
render_3d.py               extended and collapsed conformations from a trajectory
run_candidate.sh           detached driver: chloroform + water + analysis
environment_md.yml         conda environment
protacmd-lock-linux64.txt  pinned environment, linux-64
reference_protocol.md      reference protocol and permeability yardsticks
linker_design.md           linker rules read off this project's own MD runs

PROTAC SMILES are embedded in protac_md.py (CANDIDATES), so no input files are needed; anything else runs through --smiles.

Environment

On linux-64 with a CUDA 12 capable driver (525 or newer), use the lockfile and skip dependency solving:

conda create --name protacmd --file protacmd-lock-linux64.txt
conda activate protacmd
pip install "setuptools<81"

That reproduces the environment: openmm 8.5.2, ambertools 24.8, rdkit 2025.09.5, openff-toolkit 0.16.10, python 3.10.20. The lockfile carries no packmol, which --solvent chloroform needs, so add it afterwards (conda install -c conda-forge packmol) or solve from the yml instead.

Elsewhere, solve from the yml. Check the driver's CUDA version with nvidia-smi first and edit cuda-version in environment_md.yml to match (remove it entirely for CPU-only):

conda env create -f environment_md.yml
conda activate protacmd

The solve takes around ten minutes: ambertools has a large dependency tree and libmamba is single-threaded, so the spinner sits still while one core works. The install itself takes seconds. Keep setuptools<81 in the pip: section rather than as a conda dependency; an upper bound on setuptools triples the solve time.

Activation is not optional. sqm, antechamber and packmol are only on PATH inside the env, and calling <env>/bin/python directly is not enough.

Run

conda activate protacmd

# chloroform: the membrane mimic, the permeability-relevant run
python protac_md.py --solvent chloroform --replicas 3 --prod-ns 50 --gpu 0
# water: the polar reference; the pair shows chameleonic folding
python protac_md.py --solvent water      --replicas 3 --prod-ns 50 --gpu 0

python analyze_conformations.py --dir egfrvhl_rank4/chloroform --name egfrvhl_rank4
python analyze_conformations.py --dir egfrvhl_rank4/water      --name egfrvhl_rank4

Useful flags: --candidate {egfrvhl_rank4,egfrvhl_rank5,brd9vhl_rank1,cdk6crbn_rank2}, --smiles "<custom>" with --name ms39_ref for a reference compound, --platform CPU, --prod-ns, --replicas, --gpu N.

Output lands in <name>/<solvent>/rep<k>/ (*_prod.dcd, *_prod.csv, PDBs), with the dashboard and the cluster centroid PDBs in <name>/<solvent>/.

run_candidate.sh chains both solvents and both analyses for one candidate and reuses the chloroform charge cache for water, so AM1-BCC is paid once.

Smoke test on a new machine first, about two minutes:

python protac_md.py --solvent chloroform --replicas 1 --prod-ns 0.05 \
  --heat-ps 5 --equil-ps 10 --report-ps 1 --min-iters 1000 --name smoke
python analyze_conformations.py --dir smoke/chloroform --name smoke --clusters 4

Comparing finished runs

These take run directories, one per compound. ms39_ref is not bundled: run the pipeline on MS39 yourself with --smiles ... --name ms39_ref. Its components and exact linker are in ../src/protac/ErlotinibVHL.py.

python rg_basins.py egfrvhl_rank4 ms39_ref --solvents chloroform water
python psa_ensemble.py egfrvhl_rank4 ms39_ref --solvent chloroform
python ternary_span.py egfrvhl_rank4 ms39_ref

Hardware and runtime

The system is small, 2500-2800 atoms, so it is GPU-latency-bound rather than memory-bound and fits in under 2 GB of VRAM.

Platform	Speed	50 ns x 3 x 2 solvents
Free A100	~1100 ns/day	~6.5 h
Shared GPU (~75% busy)	~32 ns/day	~9 days
CPU (16 threads)	~28 ns/day	~10 days

A shared GPU is about 35x slower than a free one and no faster than CPU, so use an idle card. Replicas run sequentially in one process; parallelize by launching one process per GPU.

The reference converged within 5-10 ns; 50 ns x 3 leaves a wide margin. Confirm with the RMSD plateau and the replica-mean spread in the dashboard, and extend if either is still drifting.

Known failure modes

ModuleNotFoundError: No module named 'pkg_resources' at SystemGenerator. openff.amber_ff_ports imports pkg_resources, which setuptools 81 removed. Install setuptools<81.
OSError: Packmol not found, chloroform only. pack_box resolves packmol through PATH, so the env has to be activated, and so does sqm / antechamber for AM1-BCC charges.
Charge assignment is slow, one to three minutes on a single CPU core, and happens inside create_system. Wrapping the run in a short timeout kills it mid-charge and leaves a 0-byte ff_cache.json. Charges are cached per molecule in <name>/<solvent>/ff_cache.json; copying a populated cache in skips the step.
Never select the PROTAC by atom name or element. It carries its own Cl, and counterions named Na/Cl slip past naive regexes, which inflated Rg by about 13%. Every script here selects it by residue instead: the non-water residue with more than 5 atoms.
mdtraj mislabels DCD frame times, so traj.time is not trustworthy. Derive time from the frame index times --report-ps, as the scripts do.

Scope

The simulations show that a generated PROTAC is physically sane and describe its conformational ensemble, extended versus folded, along with the folding-linked permeability descriptors. That is the same scope as the reference work, which also simulates the free PROTAC only.

They do not show that a linker works as a degrader. Degradation needs a productive ternary complex of target, PROTAC and E3, which is a different simulation. ternary_span.py describes the span a linker can hold, not whether a complex forms.

Folding propensity only means something against a baseline, so run the same pipeline on a reference PROTAC (--smiles ... --name ms39_ref) alongside each candidate.

Yardsticks, from the 2022 MD paper: a compact minimum at Rg 5.4-5.6 A, and 3D PSA 190-265 A^2 for the permeable compounds against 290-330 A^2 for the poorly permeable one. From Atilaw 2021: passive permeability below Rg 7 A, and low-to-moderate permeability up to roughly 200-210 A^2. The 7 A cutoff on its own does not separate the 2022 compounds; see reference_protocol.md.