Reading TEA leaves for de novo protein design

Lorenzo Pantolini, Janani Durairaj

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Track: long paper (4–8 pages excluding references)

Keywords: Monte Carlo sampling, protein design, protein language models

TL;DR: We leveraged a discrete structural proxy derived from protein language models, enabling random mutagenesis MCMC to rapidly navigate the protein landscape.

Abstract:

De novo protein design expands the functional protein universe beyond natural evolution, offering vast therapeutic and industrial potential.
Monte Carlo sampling in protein design is under-explored due to the typically long simulation times required or prohibitive time requirements of current structure prediction oracles. Here we make use of a 20-letter structure-inspired alphabet derived from protein language model embeddings to score random mutagenesis-based Metropolis sampling of amino acid sequences. This facilitates fast template-guided and unconditional design, generating sequences that satisfy in silico designability criteria without known homologues. Ultimately, this unlocks a new path to fast and de novo protein design.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 49