  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}.
