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

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 $2\mu\text{s}$ MD trajectory (12 000 frames) of the human dopamine D$2$ receptor in a membrane environment, the sequential and residue-based pooling strategies reproduce the reference ensemble with high structural fidelity (all atom lDDT \~ $0.7$; $C\alpha$-lDDT \~ $0.8$) and recovers backbone and side chain dihedral angle distributions with a Jensen-Shannon divergence $