A reward-guided tree search over a frozen protein language model designs diverse sequences that score higher on ESMFold-based self-consistency benchmarks than existing inverse folding methods.
Fast non-autoregressive inverse folding with discrete diffusion
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abstract
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
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ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search
A reward-guided tree search over a frozen protein language model designs diverse sequences that score higher on ESMFold-based self-consistency benchmarks than existing inverse folding methods.