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Small-molecule binding and sensing with a designed protein family.

Gyu Rie LeeSamuel J PellockChristoffer NornDoug TischerJustas DauparasDavid BakerJaron A M MercerAlex KangAsim BeraHannah NguyenInna GoreshnikDionne VafeadosNicole RoullierHannah L HanBrian CoventryHugh K HaddoxDavid R LiuAndy Hsien-Wei YehDavid Baker
Published in: bioRxiv : the preprint server for biology (2023)
Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.
Keyphrases
  • small molecule
  • deep learning
  • protein protein
  • binding protein
  • risk assessment
  • gold nanoparticles
  • oxidative stress
  • high glucose
  • sensitive detection
  • drug induced
  • diabetic rats
  • endothelial cells
  • liquid chromatography