In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences

Daniel Burnside; Andrew Schoenrock; Houman Moteshareie; Mohsen Hooshyar; Prabh Basra; Maryam Hajikarimlou; Kevin Dick; Brad Barnes; Tom Kazmirchuk; Matthew Jessulat; Sylvain Pitre; Bahram Samanfar; Mohan Babu; James R Green; Alex Wong; Frank Dehne; Kyle K Biggar; Ashkan Golshani

doi:10.1016/j.isci.2018.11.038

In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences

iScience. 2019 Jan 25:11:375-387. doi: 10.1016/j.isci.2018.11.038. Epub 2018 Dec 4.

Authors

Daniel Burnside¹, Andrew Schoenrock², Houman Moteshareie¹, Mohsen Hooshyar³, Prabh Basra³, Maryam Hajikarimlou¹, Kevin Dick⁴, Brad Barnes², Tom Kazmirchuk³, Matthew Jessulat⁵, Sylvain Pitre², Bahram Samanfar⁶, Mohan Babu⁵, James R Green⁴, Alex Wong³, Frank Dehne², Kyle K Biggar⁷, Ashkan Golshani⁸

Affiliations

¹ Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada.
² School of Computer Science, Carleton University, Ottawa, ON K1S5B6, Canada.
³ Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada.
⁴ Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S5B6, Canada.
⁵ Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, SK S4S 0A2, Canada.
⁶ Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Research and Development Centre (ORDC), Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C5, Canada.
⁷ Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Institute of Biochemistry, Carleton University, Ottawa, ON K1S5B6, Canada.
⁸ Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Institute of Biochemistry, Carleton University, Ottawa, ON K1S5B6, Canada. Electronic address: ashkan_golshani@carleton.ca.

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.

Keywords: Bioinformatics; Biological Sciences; Protein Family Determination.