5/🚀 ...and when prompted by function, PLAID learns sequence motifs at active sites & directly outputs sidechain positions, which backbone-only methods such as RFDiffusion can't do out-of-the-box.

The residues aren't directly adjacent, suggesting that the model isn't simply memorizing training data:

4/ On unconditional generation, PLAID generates high quality and diverse structures, especially at longer sequence lengths where previous methods underperform...

3/ I was pretty stuck until building out the CHEAP (bit.ly/cheap-proteins) autoencoders that compressed & smoothed out the latent space: interestingly, gradual noise added to the ESMFold latent space doesn't actually corrupt the sequence and structure until the final forward diffusion timesteps 🤔

2/💡Co-generating sequence and structure is hard. A key insight is that to get embeddings of the ESMFold latent space during training, we only need sequence inputs.

For inference, we can sample latent embeddings & use frozen sequence/structure decoders to get all-atom structure:

1/🧬 Excited to share PLAID, our new approach for co-generating sequence and all-atom protein structures by sampling from the latent space of ESMFold. This requires only sequences during training, which unlocks more data and annotations:

bit.ly/plaid-proteins
🧵