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## Abstract
Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary information, but they are unable to explicitly acquire protein functions, which is the end goal of protein representation learning. Fortunately, for many proteins, their textual property descriptions are available, where their various functions are also described. Motivated by this fact, we first build the ProtDescribe dataset to augment protein sequences with text descriptions of their functions and other important properties. Based on this dataset, we propose the [ProtST framework](https://arxiv.org/abs/2301.12040) to enhance Protein Sequence pre-training and understanding by biomedical Texts. During pre-training, we design three types of tasks, i.e., unimodal mask prediction, multimodal representation alignment and multimodal mask prediction, to enhance a PLM with protein property information with different granularities and, at the same time, preserve the PLM’s original representation power. On downstream tasks, ProtST enables both supervised learning and zeroshot prediction. We verify the superiority of ProtST-induced PLMs over previous ones on diverse representation learning benchmarks. Under the zero-shot setting, we show the effectiveness of ProtST on zero-shot protein classification, and ProtST also enables functional protein retrieval from a large-scale database without any function annotation. Source code and model weights are available at [https://github.com/DeepGraphLearning/ProtST](https://github.com/DeepGraphLearning/ProtST).
## Example
The following script shows how to run ProtST with [optimum-intel](https://github.com/huggingface/optimum-intel) optimization on zero-shot classification task.
```python
import logging
import functools
from tqdm import tqdm
import torch
from datasets import load_dataset
from transformers import AutoModel, AutoTokenizer, AutoConfig
logger = logging.getLogger(__name__)
def tokenize_protein(example, protein_tokenizer=None, padding=None):
protein_seqs = example["prot_seq"]
protein_inputs = protein_tokenizer(protein_seqs, padding=padding, add_special_tokens=True)
example["protein_input_ids"] = protein_inputs.input_ids
example["protein_attention_mask"] = protein_inputs.attention_mask
return example
def label_embedding(labels, text_tokenizer, text_model, device):
# embed label descriptions
label_feature = []
with torch.inference_mode():
for label in labels:
label_input_ids = text_tokenizer.encode(label, max_length=128,
truncation=True, add_special_tokens=False)
label_input_ids = [text_tokenizer.cls_token_id] + label_input_ids
label_input_ids = torch.tensor(label_input_ids, dtype=torch.long, device=device).unsqueeze(0)
attention_mask = label_input_ids != text_tokenizer.pad_token_id
attention_mask = attention_mask.to(device)
text_outputs = text_model(label_input_ids, attention_mask=attention_mask)
label_feature.append(text_outputs["text_feature"])
label_feature = torch.cat(label_feature, dim=0)
label_feature = label_feature / label_feature.norm(dim=-1, keepdim=True)
return label_feature
def zero_shot_eval(logger, device,
test_dataset, target_field, protein_model, logit_scale, label_feature):
# get prediction and target
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)
preds, targets = [], []
with torch.inference_mode():
for data in tqdm(test_dataloader):
target = data[target_field]
targets.append(target)
protein_input_ids = torch.tensor(data["protein_input_ids"], dtype=torch.long, device=device).unsqueeze(0)
attention_mask = torch.tensor(data["protein_attention_mask"], dtype=torch.long, device=device).unsqueeze(0)
protein_outputs = protein_model(protein_input_ids, attention_mask=attention_mask)
protein_feature = protein_outputs["protein_feature"]
protein_feature = protein_feature / protein_feature.norm(dim=-1, keepdim=True)
pred = logit_scale * protein_feature @ label_feature.t()
preds.append(pred)
preds = torch.cat(preds, dim=0)
targets = torch.tensor(targets, dtype=torch.long, device=device)
accuracy = (preds.argmax(dim=-1) == targets).float().mean().item()
logger.warning("Zero-shot accuracy: %.6f" % accuracy)
if __name__ == "__main__":
# get datasets
raw_datasets = load_dataset("Jiqing/ProtST-SubcellularLocalization", cache_dir="~/.cache/huggingface/datasets", split='test') # cache_dir defaults to "~/.cache/huggingface/datasets"
#device = torch.device("cuda:0")
device = torch.device("cpu")
protst_model = AutoModel.from_pretrained("Jiqing/ProtST-esm1b", trust_remote_code=True, torch_dtype=torch.bfloat16).to(device)
protein_model = protst_model.protein_model
+ import intel_extension_for_pytorch as ipex
+ from optimum.intel.generation.modeling import jit_trace
+ protein_model = ipex.optimize(protein_model, dtype=torch.bfloat16, inplace=True)
+ protein_model = jit_trace(protein_model, "sequence-classification")
text_model = protst_model.text_model
logit_scale = protst_model.logit_scale
logit_scale.requires_grad = False
logit_scale = logit_scale.to(device)
logit_scale = logit_scale.exp()
protein_tokenizer = AutoTokenizer.from_pretrained("facebook/esm1b_t33_650M_UR50S")
text_tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")
func_tokenize_protein = functools.partial(tokenize_protein, protein_tokenizer=protein_tokenizer, padding=False)
test_dataset = raw_datasets.map(
func_tokenize_protein, batched=False,
remove_columns=["prot_seq"],
desc="Running tokenize_proteins on dataset",
)
labels = load_dataset("Jiqing/subloc_template", cache_dir="~/.cache/huggingface/datasets")["train"]["name"]
text_tokenizer.encode(labels[0], max_length=128, truncation=True, add_special_tokens=False)
label_feature = label_embedding(labels, text_tokenizer, text_model, device)
zero_shot_eval(logger, device, test_dataset, "localization",
protein_model, logit_scale, label_feature)
``` |