Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors

2025-07-28

AI Summary

Integrating heterogeneous experimental data—such as cryo-EM density maps, chemical cross-linking constraints, and residue contact predictions—for protein structure determination remains challenging due to unknown noise levels across modalities and the difficulty of assigning appropriate modality-specific weights. Method: We propose an adaptive gradient-guided diffusion generative framework that requires no manual hyperparameter tuning. Our approach adopts a plug-and-play optimization paradigm, leveraging a pre-trained protein diffusion model as a structural prior, and jointly incorporates adaptive noise estimation and dynamic modality weighting to end-to-end integrate multi-modal gradient signals. Contribution/Results: Unlike existing methods relying on fixed weights or precise noise modeling, our framework significantly reduces dependence on noise priors and manual parameter adjustment. Experiments demonstrate substantial improvements in reconstruction accuracy under realistic noise conditions, with validated robustness and strong cross-modal generalization capability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

In an inverse problem, the goal is to recover an unknown parameter (e.g., an image) that has typically undergone some lossy or noisy transformation during measurement. Recently, deep generative models, particularly diffusion models, have emerged as powerful priors for protein structure generation. However, integrating noisy experimental data from multiple sources to guide these models remains a significant challenge. Existing methods often require precise knowledge of experimental noise levels and manually tuned weights for each data modality. In this work, we introduce Adam-PnP, a Plug-and-Play framework that guides a pre-trained protein diffusion model using gradients from multiple, heterogeneous experimental sources. Our framework features an adaptive noise estimation scheme and a dynamic modality weighting mechanism integrated into the diffusion process, which reduce the need for manual hyperparameter tuning. Experiments on complex reconstruction tasks demonstrate significantly improved accuracy using Adam-PnP.

Problem

• Recover unknown parameters from noisy transformations in inverse problems
• Integrate noisy experimental data from multiple sources for protein generation
• Reduce manual tuning in multimodal data integration for diffusion models

Innovation

• Plug-and-Play framework with diffusion priors
• Adaptive noise estimation for multiple sources
• Dynamic modality weighting in diffusion process