SaProt: Protein Language Modeling with Structure-aware Vocabulary

The repository is an official implementation of SaProt: Protein Language Modeling with Structure-aware Vocabulary.

If you have any question about the paper or the code, feel free to raise an issue! Saprot should outperform ESM-2 in most tasks under fair evaluation settings.

The laboratory is hiring research assistants, interns, doctoral students, and postdoctoral researchers. Please contact the corresponding author for details.

实验室招聘科研助理，实习生，博士生和博士后，请联系通讯作者

News

• 2024/03/08: We uploaded a simple function to make zero-shot prediction of mutational effect (see example below).
• 2024/01/17: Our paper has been accepted as ICLR 2024 spotlight 🎉🎉🎉!
• 2023/10/30: We release a pre-trained SaProt 35M model and a 35M residue-sequence-only version of SaProt (for comparison)! The residue-sequence-only SaProt (without 3Di token) performs highly similar to the official ESM-2 35M model. (see Results below).
• 2023/10/30: We released the results by using ESMFold structures. See Table below

Overview

We propose a structure-aware vocabulary for protein language modeling. The vocabulary is constructed by encoding the protein structure into discrete 3D tokens by using the foldseek. We combine the residue tokens and the structure tokens to form a structure-aware sequence. Through large-scale pre-training, our model, i.e. SaProt, can learn the relationship between the structure and the sequence. For more details, please refer to our paper https://www.biorxiv.org/content/10.1101/2023.10.01.560349v2.

Environment installation

Create a virtual environment

conda create -n SaProt python=3.10
conda activate SaProt

Install packages

bash environment.sh  

Prepare the SaProt model

We provide two ways to use SaProt, including through huggingface class and through the same way in esm github. Users can choose either one to use.

Model checkpoints

New experimental results

Some experimental results are listed below. For more details, please refer to our paper.

35M Model

650M Model

AlphaFold2 vs. ESMFold

We compare structures predicted by AF2 or ESMFold, which is shown below:

Load SaProt

Hugging Face model

The following code shows how to load the model based on huggingface class.

from transformers import EsmTokenizer, EsmForMaskedLM

model_path = "/your/path/to/SaProt_650M_AF2"
tokenizer = EsmTokenizer.from_pretrained(model_path)
model = EsmForMaskedLM.from_pretrained(model_path)

#################### Example ####################
device = "cuda"
model.to(device)

seq = "MdEvVpQpLrVyQdYaKv"
tokens = tokenizer.tokenize(seq)
print(tokens)

inputs = tokenizer(seq, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}

outputs = model(**inputs)
print(outputs.logits.shape)

"""
['Md', 'Ev', 'Vp', 'Qp', 'Lr', 'Vy', 'Qd', 'Ya', 'Kv']
torch.Size([1, 11, 446])
"""

Load SaProt using esm repository

User could also load SaProt by esm implementation. The checkpoint is stored in the same huggingface folder, named SaProt_650M_AF2.pt. We provide a function to load the model.

from utils.esm_loader import load_esm_saprot

model_path = "/your/path/to/SaProt_650M_AF2.pt"
model, alphabet = load_esm_saprot(model_path)

Convert protein structure into structure-aware sequence

We provide a function to convert a protein structure into a structure-aware sequence. The function calls the foldseek binary file to encode the structure. You can download the binary file from here and place it in the bin folder . The following code shows how to use it.

from utils.foldseek_util import get_struc_seq
pdb_path = "example/8ac8.cif"

# Extract the "A" chain from the pdb file and encode it into a struc_seq
# pLDDT is used to mask low-confidence regions if "plddt_path" is provided
parsed_seqs = get_struc_seq("bin/foldseek", pdb_path, ["A"])["A"]
seq, foldseek_seq, combined_seq = parsed_seqs

print(f"seq: {seq}")
print(f"foldseek_seq: {foldseek_seq}")
print(f"combined_seq: {combined_seq}")

Predict mutational effect

We provide a function to predict the mutational effect of a protein sequence. The example below shows how to predict the mutational effect at a specific position.

from model.esm.esm_foldseek_mutation_model import EsmFoldseekMutationModel


config = {
    "foldseek_path": None,
    "config_path": "/you/path/to/SaProt_650M_AF2",
    "load_pretrained": True,
}
model = EsmFoldseekMutationModel(**config)
tokenizer = model.tokenizer

device = "cuda"
model.eval()
model.to(device)

seq = "MdEvVpQpLrVyQdYaKv"

# Predict the effect of mutating the 3rd amino acid to A
mut_info = "V3A"
mut_value = model.predict_mut(seq, mut_info)
print(mut_value)

# Predict all effects of mutations at 3rd position
mut_pos = 3
mut_dict = model.predict_pos_mut(seq, mut_pos)
print(mut_dict)

# Predict probabilities of all amino acids at 3rd position
mut_pos = 3
mut_dict = model.predict_pos_prob(seq, mut_pos)
print(mut_dict)

"""
0.7908501625061035

{'V3A': 0.7908501625061035, 'V3C': -0.9117952585220337, 'V3D': 2.7700226306915283, 'V3E': 2.3255627155303955, 'V3F': 0.2094242423772812, 'V3G': 2.699633836746216, 'V3H': 1.240191102027893, 'V3I': 0.10231903940439224, 'V3K': 1.804598093032837,
'V3L': 1.3324960470199585, 'V3M': -0.18938277661800385, 'V3N': 2.8249857425689697, 'V3P': 0.40185314416885376, 'V3Q': 1.8361762762069702, 'V3R': 1.1899691820144653, 'V3S': 2.2159857749938965, 'V3T': 0.8813426494598389, 'V3V': 0.0, 'V3W': 0.5853186249732971, 'V3Y': 0.17449656128883362}

{'A': 0.021275954321026802, 'C': 0.0038764977362006903, 'D': 0.15396881103515625, 'E': 0.0987202599644661, 'F': 0.011895398609340191, 'G': 0.14350374042987823, 'H': 0.03334535285830498, 'I': 0.010687196627259254, 'K': 0.058634623885154724, 'L': 0.03656982257962227, 'M': 0.00798324216157198, 'N': 0.16266827285289764, 'P': 0.014419485814869404, 'Q': 0.06051575019955635, 'R': 0.03171204403042793, 'S': 0.08847439289093018, 'T': 0.023291070014238358, 'V': 0.009647775441408157, 'W': 0.017323188483715057, 'Y': 0.011487090960144997}
"""

Prepare dataset

Pre-training dataset

We provide the dataset for pre-training SaProt. The dataset can be downloaded from here.

Downstream tasks

We provide datasets that are used in the paper. Datasets can be downloaded from here.

Once downloaded, the datasets need to be decompressed and placed in the LMDB folder for supervised fine-tuning.

Fine-tune SaProt

We provide a script to fine-tune SaProt on the datasets. The following code shows how to fine-tune SaProt on specific downstream tasks. Before running the code, please make sure that the datasets are placed in the LMDB folder and the huggingface version of SaProt 650M model is placed in the weights/PLMs folder. Note that the default training setting is not as same as in the paper because of the hardware limitation for different users. We recommend users to modify the yaml file flexibly based on their own conditions (i.e. batch_size, devices and accumulate_grad_batches).

# Fine-tune SaProt on the Thermostability task
python scripts/training.py -c config/Thermostability/saprot.yaml

# Fine-tune ESM-2 on the Thermostability task
python scripts/training.py -c config/Thermostability/esm2.yaml

Record the training process (optional)

If you want to record the training process using wandb, you could modify the config file and set Trainer.logger = True and then paste your wandb API key in the config key setting.os_environ.WANDB_API_KEY.

Evaluate zero-shot performance

We provide a script to evaluate the zero-shot performance of models (foldseek binary file is required to be placed in the bin folder):

# Evaluate the zero-shot performance of SaProt on the ProteinGym benchmark
python scripts/mutation_zeroshot.py -c config/ProteinGym/saprot.yaml

# Evaluate the zero-shot performance of ESM-2 on the ProteinGym benchmark
python scripts/mutation_zeroshot.py -c config/ProteinGym/esm2.yaml

The results will be saved in the output/ProteinGym folder.

For ClinVar benchmark, you can use the following script to calculate the AUC metric:

# Evaluate the zero-shot performance of SaProt on the ClinVar benchmark
python scripts/mutation_zeroshot.py -c config/ClinVar/saprot.yaml
python scripts/compute_clinvar_auc.py -c config/ClinVar/saprot.yaml

Citation

If you find this repository useful, please cite our paper:

@article{su2023saprot,
  title={SaProt: Protein Language Modeling with Structure-aware Vocabulary},
  author={Su, Jin and Han, Chenchen and Zhou, Yuyang and Shan, Junjie and Zhou, Xibin and Yuan, Fajie},
  journal={bioRxiv},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}