Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models

🚀 Introduction (SES-Adapter)

SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of protein language models (PLMs).

We serialized the protein structure and performed cross-modal-attention with PLM embeddings, effectively improving downstream task performance and convergence efficiency.

📑 Results

Paper Results

We conduct evaluation on 9 state-of-the-art baseline models (ESM2, ProtBert, ProtT5, Ankh) across 9 datasets under 4 tasks (Localization, Function, Solubility, Annotation).

Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated training speed by a maximum of 1034% and an average of 362%, the convergence rate is also improved by approximately 2 times.

🛫 Requirement

Conda Enviroment

Please make sure you have installed Anaconda3 or Miniconda3.

conda env create -f environment.yaml
conda activate ses_adapter

Hardware

We recommend a 24GB RTX 3090 or better, but it mainly depends on which PLM you choose.

🧬 Start with SES-Adapter

Dataset Formation

We provide datasets and format references in the dataset folder. We support both JSON and CSV data formats.

• JSON:
  • example: https://github.com/tyang816/SES-Adapter/tree/main/dataset/BP
  • For more JSON dataset, you can found in dataset.
• CSV example:
  • example: https://huggingface.co/datasets/tyang816/DeepLocBinary_AlphaFold2
  • For more CSV dataset, you can found in huggingface.

Config file should be specified ahead.

• JSON config file example.
• CSV config file example.

{
    "dataset": "BP", # Huggingface: tyang816/BP_AlphaFold2
    "pdb_type": "AlphaFold2",
    "train_file": "dataset/BP/AlphaFold2/train.json", # no need for Huggingface
    "valid_file": "dataset/BP/AlphaFold2/valid.json", # no need for Huggingface
    "test_file": "dataset/BP/AlphaFold2/test.json", # no need for Huggingface
    "num_labels": 1943,
    "problem_type": "multi_label_classification",
    "metrics": "f1_max",  # for multiple metrics use ',' to split, example: "accuracy,recall,precision"
    "monitor": "f1_max",
    "normalize": "None"
}

Metrics Support

Metric Name	Full Name	Problem Type
accuracy	Accuracy	single_label_classification/ multi_label_classification
recall	Recall	single_label_classification/ multi_label_classification
precision	Precision	single_label_classification/ multi_label_classification
f1	F1Score	single_label_classification/ multi_label_classification
mcc	MatthewsCorrCoef	single_label_classification/ multi_label_classification
auc	AUROC	single_label_classification/ multi_label_classification
f1_max	F1ScoreMax	multi_label_classification
spearman_corr	SpearmanCorrCoef	regression

Train

See the train.py script for training details. Examples can be found in scripts folder.

🙌 Citation

Please cite our work if you have used our code or data.

@article{tan2024ses-adapter,
  title={Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models},
  author={Tan, Yang and Li, Mingchen and Zhou, Bingxin and Zhong, Bozitao and Zheng, Lirong and Tan, Pan and Zhou, Ziyi and Yu, Huiqun and Fan, Guisheng and Hong, Liang},
  journal={arXiv preprint arXiv:2404.14850},
  year={2024}
}