Bioemu

BioEmu — biomolecular equilibrium ensemble emulator

What this is

BioEmu is a DiG-style score-based diffusion model that, given a protein monomer's amino acid sequence, samples from an approximation of its equilibrium (Boltzmann) distribution of backbone structures. The output is not a single predicted structure — it is an ensemble (samples.xtc) of many backbone-frame conformations. It is the ensemble / MD counterpart to AF3-class predictors:

You want…	Use
One best structure for a sequence (monomer / complex / ligand / nucleic acid)	boltz, chai-lab, protenix, esm-biohub (ESMFold2), fair-esm (ESMFold)
The ensemble of structures — conformational changes, cryptic pockets, folding/unfolding, free-energy landscape — for a monomer	BioEmu
Conformational sampling of an interface / ligand / multimer	Real MD (GROMACS/AMBER), or hybrid: ensemble for each monomer from BioEmu + dock with placer
Designed-binder validation (one shot)	boltz / chai-lab / protenix (and BindCraft already does AF2 reprediction)
Designed-binder conformational stability (does it stay folded? is the binding mode the only mode?)	BioEmu on the designed binder sequence

Trained on AFDB + ~216 ms of MD + ~1.3M experimental folding ΔG measurements, with Property-Prediction Fine-Tuning (PPFT) to match experimental stabilities. Validated against millisecond-MD distributions with ~0.9 kcal/mol MAE on free-energy errors and ~83% / 70-82% / 55-88% coverage of domain-motion / local-unfolding / cryptic-pocket reference states.

Hard limitations (read first)

• Monomers only. No multi-chain proteins, no ligands, no ions, no DNA, no RNA. The README explicitly says the "linker trick" for multimers has not worked well.
• Backbone-frame output. Side chains must be reconstructed separately (bioemu.sidechain_relax → HPacker).
• Linux + pip only. No Windows, no Docker image upstream.
• GPU required in practice. CPU works but is unusably slow.
• No new sequences. BioEmu does not design — it samples conformations for a fixed sequence.
• Inherits AF2 + MD biases. The training data is AFDB (AF2 predictions) + simulated MD + experiment.

Installation

# Base (CPU-only JAX for MSA / embedding extraction)
pip install bioemu

# Recommended: CUDA-accelerated MSA extraction
pip install bioemu[cuda]      # adds jax[cuda12]==0.4.35 + nvidia-cuda-nvcc-cu12

# Optional: side-chain reconstruction + MD relax (HPacker via conda, OpenMM)
pip install bioemu[md]        # adds openmm==8.4.0, openmm-cuda-12==8.4.0

Linux + Python ≥ 3.10. First sampling run downloads:

• AlphaFold2 weights (~3.5 GB) for the bundled inlined ColabFold MSA / embedding step → ~/.cache/colabfold/.
• BioEmu checkpoint (bioemu-v1.0, v1.1, or v1.2) from huggingface.co/microsoft/bioemu.
• HPacker (first call to sidechain_relax) into a separate virtualenv — needs conda on PATH and CUDA 12.

No separate ColabFold / AlphaFold install — both are vendored (src/_vendor/alphafold/ + src/bioemu/colabfold_inline/).

Full hardware/version notes → references/installation.md.

Quickstart — chignolin in 30 seconds

python -m bioemu.sample \
    --sequence GYDPETGTWG \
    --num_samples 10 \
    --output_dir ~/test-chignolin

Or via Python:

from bioemu.sample import main as sample

sample(sequence='GYDPETGTWG', num_samples=10, output_dir='~/test_chignolin')

Outputs land in ~/test-chignolin/:

sequence.fasta        # the input sequence
topology.pdb          # backbone topology
samples.xtc           # the ensemble (≤ num_samples frames; some may be filtered)
batch_*.npz           # per-batch raw outputs (kept for resumability)

By default, unphysical samples (steric clashes, chain breaks) are filtered out — so samples.xtc may have fewer frames than num_samples. For long / disordered sequences you can lose a lot this way; use --filter_samples=False to keep everything, or enable physical steering (next section) to reduce unphysical samples at the source.

The five things you actually tune

Knob	CLI flag	Python kwarg	When to change
Sequence	--sequence	sequence=	The protein, as a string, FASTA path, or A3M file. A3M = bring-your-own MSA.
Sample count	--num_samples	num_samples=	Typically 100–1000 for a meaningful ensemble; 10000+ for free-energy work.
Output dir	--output_dir	output_dir=	Re-running with the same dir resumes — existing batches are detected and only the missing ones are sampled.
Batch size	--batch_size_100	batch_size_100=	Batch size for an L=100 sequence. Real batch = batch_size_100 × (100/L)² (quadratic in length). Default 10; tune down to 5/3/1 if you OOM.
Model	--model_name	model_name=	bioemu-v1.1 (default, Science paper) / bioemu-v1.0 (preprint) / bioemu-v1.2 (extended MD + ΔG).

Steering — physical realism + collective-variable targeting

Steering applies potential energy functions during denoising. Two algorithms are built in:

Algorithm	Module path	When
SMC (Sequential Monte Carlo)	bioemu.steering.dpm_smc.dpm_solver_smc	Default for physical steering (chain-break / clash avoidance). Candidates are denoised unbiased, then resampled by potential favorability.
FKC (Feynman–Kac Corrector)	bioemu.steering.dpm_fkc.dpm_solver_fkc	Default for collective-variable targeting (e.g. RMSD to a reference). Importance-weighted with optional ESS-based resampling; samples are denoised with bias from the reward.

Configured via a single Hydra YAML passed as --denoiser_config (it replaces the default denoiser entirely — no separate flag for the sampler). Two configs ship:

# Physical realism (default SMC) — drastically fewer clashes / chain breaks
python -m bioemu.sample \
    --sequence GYDPETGTWG --num_samples 100 \
    --output_dir ~/steered-samples \
    --denoiser_config src/bioemu/config/steering/physical_steering.yaml

# Target a collective variable (RMSD to reference) — FKC
python -m bioemu.sample \
    --sequence GYDPETGTWG --num_samples 100 \
    --output_dir ~/cv-steered \
    --denoiser_config src/bioemu/config/steering/cv_steer.yaml \
    +denoiser_config.fk_potentials.0.cv.reference_pdb=/path/to/ref.pdb

The README's empirical guidance: 3 to 10 steering particles per output sample is the sweet spot. Wall-clock scales linearly with num_particles; physical steering is almost always worth the cost on sequences > ~100 aa.

Available building blocks (compose your own steering YAML):

Kind	Class	Used for
Potential	bioemu.steering.UmbrellaPotential	Soft target with flat-bottom + slope — used for CaCaDistance and PairwiseClash
Potential	bioemu.steering.LinearPotential	Linear reward / penalty — used for CV targeting (RMSD)
CV	bioemu.steering.CaCaDistance	Cα–Cα bond distance (prevent chain breaks)
CV	bioemu.steering.PairwiseClash	Non-neighbour atom distances (prevent clashes)
CV	bioemu.steering.RMSD	RMSD vs a reference PDB

Steering keys in the YAML's steering_config: block: num_particles, ess_threshold, start / end (diffusion time range — note reverse process goes 1→0). Full YAML schema and tuning → references/steering.md.

Side chains and MD relax

BioEmu's core output is backbone frames only. To get all-heavy-atom structures, run the bundled sidechain_relax module:

python -m bioemu.sidechain_relax \
    --pdb-path  ~/test-chignolin/topology.pdb \
    --xtc-path  ~/test-chignolin/samples.xtc \
    --outpath   ~/test-chignolin/relaxed

This produces (under --outpath, with prefix samples by default):

• samples_sidechain_rec.{pdb,xtc} — heavy-atom structures with HPacker-rebuilt side chains.
• samples_md_equil.{pdb,xtc} — same, after a short OpenMM local minimization (local_minimization, default) or NVT equilibration (--md-protocol md_equil, ~0.1 ns).

Options worth knowing:

Flag	Default	Meaning
--md-equil / --no-md-equil	--md-equil	Whether to run MD equil after side-chain reconstruction
--md-protocol	local_minimization	local_minimization (fast, fixes clashes) or md_equil (slower, fixes worse issues; runs NVT then NPT, with backbone-constrained equilibration)
--simtime-ns	0	Free-MD time after equilibration (requires md_equil)
--prefix	samples	Output file prefix

This module is per-frame — its wall-clock scales linearly with num_samples and quadratically with sequence length. Run it on a subsample (e.g. 10–50 frames) rather than the full ensemble unless you specifically need every frame relaxed. Full reference → references/sidechain-relax.md.

Output layout

<output_dir>/
├── sequence.fasta              # the input sequence
├── topology.pdb                # backbone topology (single frame, used as .xtc top)
├── samples.xtc                 # the ensemble — up to num_samples frames (may be fewer if filter_samples=True)
└── batch_*.npz                 # per-batch raw outputs; kept so re-running resumes

After sidechain_relax (using --outpath and default prefix=samples):

<outpath>/
├── samples_sidechain_rec.pdb   # single-frame topology with side chains
├── samples_sidechain_rec.xtc   # full ensemble with side chains
├── samples_md_equil.pdb        # single-frame topology after MD equil
├── samples_md_equil.xtc        # full ensemble after MD equil
└── frame*_md_top.pdb           # per-frame MD topologies (only if simtime_ns > 0)

Sampling-time budget (A100 80 GB, default batch_size_100=20, 1000 samples)

Sequence length	Wall-clock
100	~4 min
300	~40 min
600	~150 min

Quadratic in length (memory and time). For longer proteins, lower batch_size_100 and budget more time.

Key gotchas

1. Monomers only. Don't try multimers / ligands / nucleic acids. The model has no embeddings for them.
2. samples.xtc may have fewer frames than num_samples when filter_samples=True (default). For long / disordered chains, the loss can be large — turn on physical steering instead of disabling the filter.
3. batch_size_100 is a scaling constant, not the actual batch size. Real batch = batch_size_100 × (100/L)². Default 10 → actual batch = 10 at L=100, ≈ 1 at L=300, ≈ 0 (= 1) at L=600. On small GPUs at long lengths, drop batch_size_100 to 3–5 to be safe.
4. Re-running resumes. Same output_dir = continue. Existing batch_*.npz files are detected and only the missing ones are sampled. To start from scratch, delete the batches first.
5. base_seed is set from system time by default. For reproducible ensembles, pin base_seed=<int>. Each batch uses base_seed + start_idx.
6. A3M input bypasses the ColabFold server. Pass an .a3m path to --sequence to use your own MSA; msa_host_url is ignored.
7. HPacker first-call setup needs conda + CUDA 12. If you don't have conda on PATH, sidechain_relax will fail at first invocation — set HPACKER_PYTHONBIN=/path/to/python to bypass the auto-setup.
8. Steering YAMLs are self-contained denoiser configs. Passing --denoiser_config <steering.yaml> replaces the entire denoiser — you don't also pass --denoiser_type. The default dpm.yaml / heun.yaml are unsteered denoiser configs; the steering YAMLs wrap dpm_solver_smc / dpm_solver_fkc instead.
9. cv_steer.yaml requires reference_pdb. The shipped file has ??? as a Hydra placeholder — override on the CLI with +denoiser_config.fk_potentials.0.cv.reference_pdb=/abs/path/ref.pdb or copy the YAML and edit the field.
10. The bioemu-v1.1 checkpoint is the default. Use v1.0 only for reproducing the preprint numbers; use v1.2 for the most-recent ΔG predictions (1.3 M training measurements + extra residue-type embeddings).

Reference index

• references/installation.md — pip flavours, vendored ColabFold / AlphaFold2, model + weight caches, hardware notes
• references/cli.md — every bioemu.sample flag + Python kwarg
• references/sampling.md — model versions, denoisers, batch sizing, MSA / A3M, ColabFold server overrides, output filtering, resumability
• references/steering.md — SMC vs FKC, the YAML schema, the available CVs + potentials, when to use each, how to author your own
• references/sidechain-relax.md — HPacker integration, OpenMM protocols, scaling notes
• references/outputs.md — file layout, frame indexing, npz schema
• references/troubleshooting.md — OOM, ColabFold MSA failures, HPacker install, monomer-only errors
• examples/chignolin_quickstart.sh — runnable 30-second test
• examples/recipes.md — common patterns (large ensemble, steering for long proteins, ΔG prediction, cryptic-pocket discovery, side-chain relax)

Citation

Lewis, S., Hempel, T., Jiménez-Luna, J., Gastegger, M., Xie, Y., Foong, A.Y.K., Satorras, V.G., Abdin, O., Veeling, B.S., Zaporozhets, I., Chen, Y., Yang, S., Foster, A.E., Schneuing, A., Nigam, J., Barbero, F., Stimper, V., Campbell, A., Yim, J., Lienen, M., Shi, Y., Zheng, S., Schulz, H., Munir, U., Sordillo, R., Tomioka, R., Clementi, C., Noé, F. (2025). Scalable emulation of protein equilibrium ensembles with generative deep learning. Science, eadv9817. https://doi.org/10.1126/science.adv9817