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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Improving Binding Affinity and Selectivity of Computationally Designed Ligand-Binding Proteins Using Experiments.

Christine E Tinberg1,2, Sagar D Khare3,4

  • 1Department of Biochemistry, University of Washington, Seattle, WA, 98109, USA. c.tinberg@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)
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Summary
This summary is machine-generated.

This study presents a computational and experimental method to design novel proteins that bind small molecules. The approach successfully created a digoxigenin-binding protein with high affinity and selectivity.

Keywords:
Affinity optimizationBinding selectivityComputational designProtein-small molecule interactionsRosetta macromolecular modelingSteroid binding

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Area of Science:

  • Protein engineering
  • Synthetic biology
  • Biochemistry

Background:

  • Designing de novo proteins for small molecule binding is crucial for synthetic biology and medicine.
  • Computational protein design combined with high-throughput screening offers a powerful strategy for creating custom binding proteins.

Purpose of the Study:

  • To describe the experimental methodology for designing small molecule-binding proteins using computational tools and high-throughput screening.
  • To engineer proteins with programmable binding affinities, modes, and selectivities for specific ligands.

Main Methods:

  • Utilized the Rosetta software suite for computational protein design.
  • Employed fluorescence-activated cell sorting and high-throughput yeast surface display for protein engineering and affinity maturation.
  • Applied deep sequencing to analyze mutagenic libraries and understand binding landscapes.

Main Results:

  • Successfully designed a protein with a preprogrammed binding site for a small molecule of choice.
  • Engineered a selective digoxigenin (DIG)-binding protein with picomolar affinity after affinity maturation.
  • Demonstrated high selectivity for DIG over structurally similar steroids.

Conclusions:

  • The combined computational-experimental approach enables the de novo design and optimization of proteins for specific small molecule binding.
  • This method allows for precise control over binding affinity, selectivity, and binding mode.
  • The developed strategy has significant potential for applications in synthetic biology and therapeutic development.