All-Atom Protein Generation with Latent Diffusion

Abstract

While generative models hold immense promise for protein design, existing models are typically backbone-only, despite the indispensable role that sidechain atoms play in mediating function. As prerequisite knowledge, all-atom 3D structure generation requires the discrete sequence to specify sidechain identities, which poses a multimodal generation problem. We propose **PLAID** (**P**rotein **La**tent **I**nduced **D**iffusion), which samples from the *latent space* of a pre-trained sequence-to-structure predictor, ESMFold. The sampled latent embedding is then decoded with frozen decoders into the sequence and all-atom structure. Importantly, **PLAID only requires sequence input during training**, thus augmenting the dataset size by 2-4 orders of magnitude compared to the Protein Data Bank. It also makes more annotations available for functional control. As a demonstration of annotation-based prompting, we perform compositional conditioning on function and taxonomy using classifier-free guidance. Intriguingly, function-conditioned generations learn active site residue identities, despite them being non-adjacent on the sequence, *and* can correctly place the sidechain atoms. We further show that PLAID can generate transmembrane proteins with expected hydrophobicity patterns, perform motif scaffolding, and improve unconditional sample quality for long sequences. Links to model weights and training code are publicly available at `[redacted]`.