Protein Multimer Structure Prediction via Prompt Learning
ICLR 2024(2024)
摘要
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 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
PromptMSP, a pre-training and Prompt tuning
framework for Multimer Structure Prediction. 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 .
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关键词
docking path prediction,protein complex structure,prompt learning
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