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

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 p...

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Autores principales: Burnside, Daniel, Schoenrock, Andrew, Moteshareie, Houman, Hooshyar, Mohsen, Basra, Prabh, Hajikarimlou, Maryam, Dick, Kevin, Barnes, Brad, Kazmirchuk, Tom, Jessulat, Matthew, Pitre, Sylvain, Samanfar, Bahram, Babu, Mohan, Green, James R., Wong, Alex, Dehne, Frank, Biggar, Kyle K., Golshani, Ashkan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6348295/
https://www.ncbi.nlm.nih.gov/pubmed/30660105
http://dx.doi.org/10.1016/j.isci.2018.11.038
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author Burnside, Daniel
Schoenrock, Andrew
Moteshareie, Houman
Hooshyar, Mohsen
Basra, Prabh
Hajikarimlou, Maryam
Dick, Kevin
Barnes, Brad
Kazmirchuk, Tom
Jessulat, Matthew
Pitre, Sylvain
Samanfar, Bahram
Babu, Mohan
Green, James R.
Wong, Alex
Dehne, Frank
Biggar, Kyle K.
Golshani, Ashkan
author_facet Burnside, Daniel
Schoenrock, Andrew
Moteshareie, Houman
Hooshyar, Mohsen
Basra, Prabh
Hajikarimlou, Maryam
Dick, Kevin
Barnes, Brad
Kazmirchuk, Tom
Jessulat, Matthew
Pitre, Sylvain
Samanfar, Bahram
Babu, Mohan
Green, James R.
Wong, Alex
Dehne, Frank
Biggar, Kyle K.
Golshani, Ashkan
author_sort Burnside, Daniel
collection PubMed
description 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.
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spelling pubmed-63482952019-02-05 In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences Burnside, Daniel Schoenrock, Andrew Moteshareie, Houman Hooshyar, Mohsen Basra, Prabh Hajikarimlou, Maryam Dick, Kevin Barnes, Brad Kazmirchuk, Tom Jessulat, Matthew Pitre, Sylvain Samanfar, Bahram Babu, Mohan Green, James R. Wong, Alex Dehne, Frank Biggar, Kyle K. Golshani, Ashkan iScience Article 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. Elsevier 2018-12-04 /pmc/articles/PMC6348295/ /pubmed/30660105 http://dx.doi.org/10.1016/j.isci.2018.11.038 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Burnside, Daniel
Schoenrock, Andrew
Moteshareie, Houman
Hooshyar, Mohsen
Basra, Prabh
Hajikarimlou, Maryam
Dick, Kevin
Barnes, Brad
Kazmirchuk, Tom
Jessulat, Matthew
Pitre, Sylvain
Samanfar, Bahram
Babu, Mohan
Green, James R.
Wong, Alex
Dehne, Frank
Biggar, Kyle K.
Golshani, Ashkan
In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences
title In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences
title_full In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences
title_fullStr In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences
title_full_unstemmed In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences
title_short In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences
title_sort in silico engineering of synthetic binding proteins from random amino acid sequences
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6348295/
https://www.ncbi.nlm.nih.gov/pubmed/30660105
http://dx.doi.org/10.1016/j.isci.2018.11.038
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