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Binding and sensing diverse small molecules using shape-complementary pseudocycles.

Linna An1,2, Meerit Said1,2, Long Tran2,3,4

  • 1Department of Biochemistry, University of Washington, Seattle, WA, USA.

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|July 18, 2024
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Summary
This summary is machine-generated.

We developed a deep learning method to design small molecule-binding proteins for sensing applications. This approach creates high-affinity binders for diverse molecules, enabling new sensor technologies.

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Area of Science:

  • Protein engineering
  • Biotechnology
  • Computational biology

Background:

  • Designing proteins with specific small molecule-binding capabilities is challenging.
  • Existing methods often lack the versatility for diverse molecular targets.

Purpose of the Study:

  • To develop a novel computational approach for designing high-affinity small molecule-binding proteins.
  • To create proteins suitable for downstream applications like sensing and chemically induced dimerization.

Main Methods:

  • Utilized deep learning to generate protein pseudocycles with tunable binding pocket shapes.
  • Employed computational docking to identify complementary protein designs for target small molecules.
  • Optimized interaction surfaces for high binding affinity and performed experimental screening.

Main Results:

  • Successfully designed and validated high-affinity binders for four distinct small molecules, including methotrexate and thyroxine.
  • Demonstrated the modularity of the designed proteins by creating chemically induced dimerization systems.
  • Engineered low-noise nanopore sensors utilizing the designed protein domains.

Conclusions:

  • The deep learning-based pseudocycle design approach is effective for creating high-affinity small molecule binders.
  • The modular protein designs facilitate the development of advanced biosensing and molecular control systems.
  • This method offers a versatile platform for protein engineering with broad biotechnological potential.