Constraint Decoupled Latent Diffusion for Protein Backmapping

TL;DR

CODLAD introduces a two-stage latent diffusion framework that effectively reconstructs detailed protein structures from coarse models, balancing accuracy, diversity, and efficiency with strong generalization.

Contribution

The paper presents a novel latent diffusion approach that decouples constraint handling from structure generation for protein backmapping, improving accuracy and diversity.

Findings

Achieves state-of-the-art atomistic accuracy

Demonstrates high conformational diversity

Offers improved computational efficiency

Abstract

Coarse-grained (CG) molecular dynamics simulations enable efficient exploration of protein conformational ensembles. However, reconstructing atomic details from CG structures (backmapping) remains a challenging problem. Current approaches face an inherent trade-off between maintaining atomistic accuracy and exploring diverse conformations, often necessitating complex constraint handling or extensive refinement steps. To address these challenges, we introduce a novel two-stage framework, named CODLAD (COnstraint Decoupled LAtent Diffusion). This framework first compresses atomic structures into discrete latent representations, explicitly embedding structural constraints, thereby decoupling constraint handling from generation. Subsequently, it performs efficient denoising diffusion in this latent space to produce structurally valid and diverse all-atom conformations. Comprehensive evaluations on diverse protein datasets demonstrate that CODLAD achieves state-of-the-art performance in atomistic accuracy, conformational diversity, and computational efficiency while exhibiting strong generalization across different protein systems. Code is available at https://github.com/xiaoxiaokuye/CODLAD.