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Methods Mol Biol. 2016;1414:155-71. doi: 10.1007/978-1-4939-3569-7_9.

Improving Binding Affinity and Selectivity of Computationally Designed Ligand-Binding Proteins Using Experiments.

Author information

1
Department of Biochemistry, University of Washington, Seattle, WA, 98109, USA. c.tinberg@gmail.com.
2
Amgen, South San Francisco, CA, 94080, USA. c.tinberg@gmail.com.
3
Department of Chemistry and Chemical Biology, Rutgers State University of New Jersey, Piscataway, NJ, 08854, USA.
4
Center for Integrative Proteomics Research, Rutgers State University of New Jersey, Piscataway, NJ, 08854, USA.

Abstract

The ability to de novo design proteins that can bind small molecules has wide implications for synthetic biology and medicine. Combining computational protein design with the high-throughput screening of mutagenic libraries of computationally designed proteins is emerging as a general approach for creating binding proteins with programmable binding modes, affinities, and selectivities. The computational step enables the creation of a binding site in a protein that otherwise does not (measurably) bind the intended ligand, and targeted mutagenic screening allows for validation and refinement of the computational model as well as provides orders-of-magnitude increases in the binding affinity. Deep sequencing of mutagenic libraries can provide insights into the mutagenic binding landscape and enable further affinity improvements. Moreover, in such a combined computational-experimental approach where the binding mode is preprogrammed and iteratively refined, selectivity can be achieved (and modulated) by the placement of specified amino acid side chain groups around the ligand in defined orientations. Here, we describe the experimental aspects of a combined computational-experimental approach for designing-using the software suite Rosetta-proteins that bind a small molecule of choice and engineering, using fluorescence-activated cell sorting and high-throughput yeast surface display, high affinity and ligand selectivity. We illustrated the utility of this approach by performing the design of a selective digoxigenin (DIG)-binding protein that, after affinity maturation, binds DIG with picomolar affinity and high selectivity over structurally related steroids.

KEYWORDS:

Affinity optimization; Binding selectivity; Computational design; Protein-small molecule interactions; Rosetta macromolecular modeling; Steroid binding

PMID:
27094290
DOI:
10.1007/978-1-4939-3569-7_9
[Indexed for MEDLINE]

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