Protein Multimer Structure Prediction via Prompt Learning
<|reference_start|>Protein Multimer Structure Prediction via Prompt Learning: Understanding the 3D structures of protein multimers is crucial, as they play a vital role in regulating various cellular processes. It has been empirically confirmed that the multimer structure prediction~(MSP) can be well handled in a step-wise assembly fashion using provided dimer structures and predicted protein-protein interactions~(PPIs). However, due to the biological gap in the formation of dimers and larger multimers, directly applying PPI prediction techniques can often cause a \textit{poor generalization} to the MSP task. To address this challenge, we aim to extend the PPI knowledge to multimers of different scales~(i.e., chain numbers). Specifically, we propose \textbf{\textsc{PromptMSP}}, a pre-training and \textbf{Prompt} tuning framework for \textbf{M}ultimer \textbf{S}tructure \textbf{P}rediction. First, we tailor the source and target tasks for effective PPI knowledge learning and efficient inference, respectively. We design PPI-inspired prompt learning to narrow the gaps of two task formats and generalize the PPI knowledge to multimers of different scales. We provide a meta-learning strategy to learn a reliable initialization of the prompt model, enabling our prompting framework to effectively adapt to limited data for large-scale multimers. Empirically, we achieve both significant accuracy (RMSD and TM-Score) and efficiency improvements compared to advanced MSP models. The code, data and checkpoints are released at \url{https://github.com/zqgao22/PromptMSP}.<|reference_end|>
@article{gao2024protein,
    title={Protein Multimer Structure Prediction via Prompt Learning},
    author={Ziqi Gao, Xiangguo Sun, Zijing Liu, Yu Li, Hong Cheng, Jia Li},
    journal={arXiv preprint arXiv:2402.18813},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18813},
    primaryClass={cs.CE}
}