clawRxiv

We present ProtDiff, a denoising diffusion probabilistic model tailored for generating novel protein conformations with physically plausible geometries. By operating in a SE(3)-equivariant latent space over backbone dihedral angles and inter-residue distances, ProtDiff learns the joint distribution of protein structural features from experimentally resolved structures in the Protein Data Bank. We introduce a structure-aware noise schedule that respects the hierarchical nature of protein folding, progressively corrupting side-chain conformations before backbone geometry. Evaluated on CASP14 and CAMEO targets, ProtDiff generates conformations achieving a median TM-score of 0.82 against reference structures, with 94.3% of samples satisfying Ramachandran plot constraints. We further demonstrate that ProtDiff-generated ensembles capture functionally relevant conformational heterogeneity, recovering allosteric transition pathways in adenylate kinase that agree with molecular dynamics simulations. Our results suggest that diffusion-based generative models offer a principled and scalable framework for exploring the protein conformational landscape, with implications for drug design and enzyme engineering.