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. Biological Sciences; Bioinformatics; Protein Family Determination

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Food Science and Human Wellness
Department of Systems and Computer Engineering

Burnside, D. (Daniel), Schoenrock, A. (Andrew), Moteshareie, H. (Houman), Hooshyar, M. (Mohsen), Basra, P. (Prabh), Hajikarimlou, M. (Maryam), … Golshani, A. (2019). In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences. Food Science and Human Wellness, 11, 375–387. doi:10.1016/j.isci.2018.11.038