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Methods Mol Biol. 2017;1529:363-373.

Computational Design of Ligand Binding Proteins.

Author information

1
Department of Biochemistry, University of Washington, Seattle, WA, 98109, USA.
2
Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine at Rutgers, Rutgers The State University of New Jersey, Piscataway, NJ, 08854, USA. khare@chem.rutgers.edu.

Abstract

The ability to design novel small-molecule binding sites in proteins is a stringent test of our understanding of the principles of molecular recognition, and would have many practical applications, in synthetic biology and medicine. Here, we describe a computational method in the context of the macromolecular modeling suite Rosetta to designing proteins with sites featuring predetermined interactions to ligands of choice. The required inputs for the method are a model of the small molecule and the desired interactions (e.g., hydrogen bonding, electrostatics, steric packing), and a set of crystallographic structures of proteins containing existing or predicted binding pockets. Constellations of backbones surrounding the putative pocket are searched for compatibility with the desired binding site conception using RosettaMatch and surrounding amino acid side chain identities are optimized using RosettaDesign. Validation of the design is performed using metrics that evaluate the interface energy of the predicted binding pose, the preformation of key binding site features in the apo-state, and the local compatibility of the designed sequence changes with the wild type backbone structure, and top-ranking candidate designs are generated for experimental validation. This approach can allow for the creation of novel binding sites and for the rational tuning of specificity for congeneric ligands by altering the programmed interactions by design, thus offering a general computational protocol for construction and modulation of protein-small molecule interfaces.

KEYWORDS:

Ligand binding; Protein design; Rosetta software; Small molecule binding; Steroid binding

PMID:
27914062
DOI:
10.1007/978-1-4939-6637-0_19
[Indexed for MEDLINE]

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