Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models
<|reference_start|>Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models: Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing Parameter-Efficient Fine-Tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is non-trivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark datasets across distinct downstream tasks. 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. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.<|reference_end|>
@article{tan2024simple,,
    title={Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein
  Language Models},
    author={Yang Tan, Mingchen Li, Bingxin Zhou, Bozitao Zhong, Lirong Zheng, Pan
  Tan, Ziyi Zhou, Huiqun Yu, Guisheng Fan, Liang Hong},
    journal={arXiv preprint arXiv:2404.14850},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14850},
    primaryClass={cs.CL cs.LG q-bio.BM}
}