A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while the discriminative model guides a search for sequences with high fitness. Given its broad success in conditional sampling, classifier-guided diffusion modeling is a promising foundation for protein design, leading many to develop guided diffusion models for structure with inverse folding to recover sequences. In this work, we propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models that follows gradients in the hidden states of the denoising network. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods, including scarce data and challenging inverse design. Moreover, we use NOS to generalize LaMBO, a Bayesian optimization procedure for sequence design that facilitates multiple objectives and edit-based constraints. The resulting method, LaMBO-2, enables discrete diffusions and stronger performance with limited edits through a novel application of saliency maps. We apply LaMBO-2 to a real-world protein design task, optimizing antibodies for higher expression yield and binding affinity to a therapeutic target under locality and liability constraints, with 97% expression rate and 25% binding rate in exploratory in vitro experiments.
pip install -r requirements.txt
To install ANARCI for sequence alignment, follow the instructions in the official repo. If you wish to recreate the DiffAb and RFDiffusion comparisons, these repos also contain their own dependencies.
Preprocessed training and validation datasets are available in the data directory. To recreate the datasets from scratch, you can use the following instructions.
In order to obtain many SASA labels, we use IgFold's archive of pre-computed structures on paired OAS (pOAS). We extract the sequences and structures and labeled them by running the labeling script:
PYTHONPATH="." python scripts/data/process_igfold_poas.py
Finally, we choose random test sequences, and remove any sequences with an overlapping heavy or light chain from the training dataset:
PYTHONPATH="." python scripts/data/make_splits.py
For infilling-based sampling, our scripts expect space separated sequences with "[MASK]" denoting the infilling locations. An example can be found in the test infill seed file.
To train a sequence diffusion model without a discriminative head, you can run
PYTHONPATH="." python scripts/train_seq_model.py \
model=[MODEL TYPE] \
model.optimizer.lr=[MODEL LR] \
data_dir=[DATASET DIRECTORY] \
train_fn=[TRAINING CSV FILE] \
val_fn=[VALIDATION CSV FILE] \
vocab_file=[VOCAB FILE IN THIS REPO'S BASE DIR] \
log_dir=[LOGGING DIRECTORY]
For the Gaussian corruption process (i.e. model="gaussian"), the additional argument should be set carefully:
model.noise_schedule.noise_scale=[NOISE SCALE] \
This parameter effects the variance of the noise applied to the token embeddings. The noise schedule is unchanged, but the variance at each forward step, and the corresponding prior, is scaled multiplicatively. Reasonable defaults are in the range [2, 10].
To train a model with a discriminative head for K objectives (K=1 in our experiments), the following additional arguments are necessary:
'target_cols=[[OBJECTIVE NAME 1], ..., [OBJECTIVE NAME K]]' \
model.network.target_channels=[K] \
discr_batch_ratio=[RATIO OF GENERATIVE LOSS UPDATES TO DISCRIMINATIVE] \
To perform basic sampling from a model, you can run
PYTHONPATH="." python scripts/sample.py
To recreate the infilling experiments in section 5.1, there is a script that wraps the vanilla sampling code, creating seed files with "[MASK]"s for the desired CDRs and CDR numbering method. For example, given a train model checkpoint, one can run
PYTHONPATH="." python scripts/infill/run_diffusion.py \
model=[MODEL TYPE] \
ckpt_path=[CKPT PATH] \
+seeds_fn=[PATH TO poas_seeds.csv] \
+results_dir=[RESULTS DIR] \
We also obtained infills from DiffAb and RFDiffusion. After cloning these repos into this directory, the provide wrapper scripts for DiffAb and RFDiffusion can be used to sample infills and extract them into a consistent format.
To recreate the experiments in section 5.2, we also provide a wrapper script. As an example, guided infilling with continuous corruptions, optimizing for SASA, can be run with
PYTHONPATH="." python scripts/control/sample_diffusion.py \
model=gaussian \
model.network.target_channels=1 \
ckpt_path=[CKPT PATH, MODEL TRAINED FOR SASA] \
+guidance_kwargs.step_size=1.0 \
+guidance_kwargs.stability_coef=0.01 \
+guidance_kwargs.num_steps=10 \
+seeds_fn=[PATH TO poas_seeds.csv] \
+results_dir=[RESULTS DIR] \
For guided infilling with discrete corruptions, optimizing for percentage of beta sheets, try running
PYTHONPATH="." python scripts/control/sample_diffusion.py \
model=mlm \
model.network.target_channels=1 \
ckpt_path=[CKPT PATH, MODEL TRAINED FOR BETA SHEETS] \
+guidance_kwargs.step_size=1.0 \
+guidance_kwargs.stability_coef=0.01 \
+guidance_kwargs.num_steps=10 \
+seeds_fn=[PATH TO poas_seeds.csv] \
+results_dir=[RESULTS DIR] \
We also provide our approximate PPLM implementation and the wrapper script used for creating infills with PPLM. For the RFDiffusion and DiffAb diversification baselines we use samples obtained from the infilling wrapper scripts described above.