Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding
<|reference_start|>Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding: Proteins, as essential biomolecules, play a central role in biological processes, including metabolic reactions and DNA replication. Accurate prediction of their properties and functions is crucial in biological applications. Recent development of protein language models (pLMs) with supervised fine tuning provides a promising solution to this problem. However, the fine-tuned model is tailored for particular downstream prediction task, and achieving general-purpose protein understanding remains a challenge. In this paper, we introduce Structure-Enhanced Protein Instruction Tuning (SEPIT) framework to bridge this gap. Our approach integrates a noval structure-aware module into pLMs to inform them with structural knowledge, and then connects these enhanced pLMs to large language models (LLMs) to generate understanding of proteins. In this framework, we propose a novel two-stage instruction tuning pipeline that first establishes a basic understanding of proteins through caption-based instructions and then refines this understanding using a mixture of experts (MoEs) to learn more complex properties and functional information with the same amount of activated parameters. Moreover, we construct the largest and most comprehensive protein instruction dataset to date, which allows us to train and evaluate the general-purpose protein understanding model. Extensive experimental results on open-ended generation and closed-set answer tasks demonstrate the superior performance of SEPIT over both closed-source general LLMs and open-source LLMs trained with protein knowledge.<|reference_end|>
@article{wu2024structure-enhanced,
    title={Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose
  Protein Understanding},
    author={Wei Wu, Chao Wang, Liyi Chen, Mingze Yin, Yiheng Zhu, Kun Fu, Jieping
  Ye, Hui Xiong, Zheng Wang},
    journal={arXiv preprint arXiv:2410.03553},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03553},
    primaryClass={cs.CL q-bio.BM}
}