In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences.
iScience 2018;
11:375-387. [PMID:
30660105 PMCID:
PMC6348295 DOI:
10.1016/j.isci.2018.11.038]
[Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 10/19/2018] [Accepted: 11/28/2018] [Indexed: 12/29/2022] Open
Abstract
Synthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS) was developed to design synthetic binding proteins (SBPs) that bind pre-determined targets while minimizing off-target interactions. InSiPS is a genetic algorithm that refines a pool of random sequences over hundreds of generations of mutation and selection to produce SBPs with pre-specified binding characteristics. As a proof of concept, we design SBPs against three yeast proteins and demonstrate binding and functional inhibition of two of three targets in vivo. Peptide SPOT arrays confirm binding sites, and a permutation array demonstrates target specificity. Our foundational approach will support the field of de novo design of small binding polypeptide motifs and has robust applicability while offering potential advantages over the limited number of techniques currently available.
InSiPS engineers synthetic binding proteins (SBPs) using primary protein sequence
SBPs are designed to a bind a target protein and avoid “off-target” interactions
Binding and functional inhibition of two of three target proteins in yeast is demonstrated
Our new approach offers advantages over alternative tools that rely on 3D models
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