Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings

TL;DR

This paper introduces LD-FPG, a novel latent diffusion framework that generates full-atom protein conformations directly from MD trajectories, capturing conformational diversity with high structural fidelity.

Contribution

The work presents a new method combining graph neural networks and diffusion models to generate detailed all-atom protein structures from molecular dynamics data.

Findings

High structural fidelity in generated conformations (lDDT ~0.7)

Accurate recovery of dihedral-angle distributions (JS divergence <0.03)

Effective generation of diverse protein conformations from MD trajectories

Abstract

Generating diverse, all-atom conformational ensembles of dynamic proteins such as G-protein-coupled receptors (GPCRs) is critical for understanding their function, yet most generative models simplify atomic detail or ignore conformational diversity altogether. We present latent diffusion for full protein generation (LD-FPG), a framework that constructs complete all-atom protein structures, including every side-chain heavy atom, directly from molecular dynamics (MD) trajectories. LD-FPG employs a Chebyshev graph neural network (ChebNet) to obtain low-dimensional latent embeddings of protein conformations, which are processed using three pooling strategies: blind, sequential and residue-based. A diffusion model trained on these latent representations generates new samples that a decoder, optionally regularized by dihedral-angle losses, maps back to Cartesian coordinates. Using D2R-MD, a 2-microsecond MD trajectory (12 000 frames) of the human dopamine D2 receptor in a membrane environment, the sequential and residue-based pooling strategy reproduces the reference ensemble with high structural fidelity (all-atom lDDT of approximately 0.7; C-alpha-lDDT of approximately 0.8) and recovers backbone and side-chain dihedral-angle distributions with a Jensen-Shannon divergence of less than 0.03 compared to the MD data. LD-FPG thereby offers a practical route to system-specific, all-atom ensemble generation for large proteins, providing a promising tool for structure-based therapeutic design on complex, dynamic targets. The D2R-MD dataset and our implementation are freely available to facilitate further research.