# Protein Multimer Structure Prediction via Prompt Learning

arXiv: https://arxiv.org/abs/2402.18813 · HTML: https://mlsanity.com/paper/2402.18813/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 11 of 11)

- [Parameter-Efficient Fine-Tuning with Discrete Fourier Transform](https://mlsanity.com/paper/2405.03003/) (2024)
- [GLBench: A Comprehensive Benchmark for Graph with Large Language Models](https://mlsanity.com/paper/2407.07457/) (2024)
- [ProG: A Graph Prompt Learning Benchmark](https://mlsanity.com/paper/2406.05346/) (2024)
- [RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning](https://mlsanity.com/paper/2408.03195/) (2024)
- [PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches](https://mlsanity.com/paper/2410.10870/) (2024)
- [Memory Limitations of Prompt Tuning in Transformers](https://mlsanity.com/paper/2509.00421/) (2025)
- [InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization](https://mlsanity.com/paper/2503.01488/) (2025)
- [Biological Sequence with Language Model Prompting: A Survey](https://mlsanity.com/paper/2503.04135/) (2025)
- [Demystify Protein Generation with Hierarchical Conditional Diffusion Models](https://mlsanity.com/paper/2507.18603/) (2025)
- [From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions](https://mlsanity.com/paper/2603.18505/) (2026)
- [Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach](https://mlsanity.com/paper/2605.09964/) (2026)

## Abstract (arXiv)

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 textitpoor 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 textbftextscPromptMSP, 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 urlhttps://github.com/zqgao22/PromptMSP.