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

AI Article Synopsis

  • Despite advancements in deep learning for protein design, creating proteins that bind to small molecules remains challenging.
  • The researchers combined deep learning with physics-based methods to create proteins with versatile pocket shapes, enabling them to design binders for unique small-molecule targets.
  • The resulting proteins showed strong binding abilities and were used to develop a sensitive biosensor for cortisol, illustrating the potential for this approach in various fields.

Article Abstract

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 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635051PMC
http://dx.doi.org/10.1101/2023.11.01.565201DOI Listing

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