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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 require the discrete sequence to specify sidechain identities, which poses a multimodal generation problem. We propose PLAID (Protein Latent Induced Diffusion), which samples from the latent space of a pre-trained sequence-tostructure 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 sidechains 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 github.com/amyxlu/plaid.
Competing Interest Statement
AXL, SAR, SK, VG, KC, RB, and NCF are employees of Genentech Inc., a member of the Roche Group.
Footnotes
* Title and formatting update: evaluation figures updated, motif scaffolding figure moved to main text, various other changes to content ordering