AlphaFolding: 4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance

2024-08-22

AI Summary

Existing protein structure prediction methods primarily focus on static conformations, limiting their ability to characterize dynamic conformational changes essential for functional interpretation and drug design. To address this, we propose the first 4D diffusion model for dynamic protein structure prediction, generating multi-timestep backbone-and-sidechain conformational trajectories. Our method innovatively integrates reference-structure-guided implicit embedding with motion alignment, and introduces the first joint representation of atom-group–based features and dihedral angles to balance conformational consistency and dynamic flexibility. Trained on molecular dynamics simulation data, the model achieves high-fidelity 32-step trajectory prediction for proteins up to 256 residues, accurately capturing both local flexibility and large-scale allosteric transitions. It significantly outperforms state-of-the-art static and dynamic prediction methods in quantitative and qualitative evaluations.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated structure prediction, the dynamic nature of protein structures has received limited attention. This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures. Our approach is distinguished by the following components: (1) a unified diffusion model capable of generating dynamic protein structures, including both the backbone and side chains, utilizing atomic grouping and side-chain dihedral angle predictions; (2) a reference network that enhances structural consistency by integrating the latent embeddings of the initial 3D protein structures; and (3) a motion alignment module aimed at improving temporal structural coherence across multiple time steps. To our knowledge, this is the first diffusion-based model aimed at predicting protein trajectories across multiple time steps simultaneously. Validation on benchmark datasets demonstrates that our model exhibits high accuracy in predicting dynamic 3D structures of proteins containing up to 256 amino acids over 32 time steps, effectively capturing both local flexibility in stable states and significant conformational changes. URL: https://fudan-generative-vision.github.io/AlphaFolding/#/

Problem

• Protein Structure Prediction
• Dynamic Structural Changes
• Biological Research and Drug Development

Innovation

• AlphaFolding
• 4D Diffusion Model
• Protein Dynamic Structure Prediction