  Generating protein sequences that fold into a intended 3D structure is a
fundamental step in de novo protein design. De facto methods utilize
autoregressive generation, but this eschews higher order interactions that
could be exploited to improve inference speed. We describe a non-autoregressive
alternative that performs inference using a constant number of calls resulting
in a 23 times speed up without a loss in performance on the CATH benchmark.
Conditioned on the 3D structure, we fine-tune ProteinMPNN to perform discrete
diffusion with a purity prior over the index sampling order. Our approach gives
the flexibility in trading off inference speed and accuracy by modulating the
diffusion speed. Code: https://github.com/johnyang101/pmpnndiff

Biomolecules;Machine Learning