Computational redesign of a fluorogen activating protein with Rosetta
The use of unnatural fluorogenic molecules widely expands the pallet of available genetically encoded fluorescent imaging tools through the design of fluorogen activating proteins (FAPs). While there is already a handful of such probes available, each of them went through laborious cycles of in vitr...
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creator | Bozhanova, Nina G Harp, Joel M Bender, Brian J Gavrikov, Alexey S Gorbachev, Dmitry A Baranov, Mikhail S Mercado, Christina B Zhang, Xuan Lukyanov, Konstantin A Mishin, Alexander S Meiler, Jens |
description | The use of unnatural fluorogenic molecules widely expands the pallet of available genetically encoded fluorescent imaging tools through the design of fluorogen activating proteins (FAPs). While there is already a handful of such probes available, each of them went through laborious cycles of in vitro screening and selection. Computational modeling approaches are evolving incredibly fast right now and are demonstrating great results in many applications, including de novo protein design. It suggests that the easier task of fine-tuning the fluorogen-binding properties of an already functional protein in silico should be readily achievable. To test this hypothesis, we used Rosetta for computational ligand docking followed by protein binding pocket redesign to further improve the previously described FAP DiB1 that is capable of binding to a BODIPY-like dye M739. Despite an inaccurate initial docking of the chromophore, the incorporated mutations nevertheless improved multiple photophysical parameters as well as the overall performance of the tag. The designed protein, DiB-RM, shows higher brightness, localization precision, and apparent photostability in protein-PAINT super-resolution imaging compared to its parental variant DiB1. Moreover, DiB-RM can be cleaved to obtain an efficient split system with enhanced performance compared to a parental DiB-split system. The possible reasons for the inaccurate ligand binding pose prediction and its consequence on the outcome of the design experiment are further discussed. |
doi_str_mv | 10.1371/journal.pcbi.1009555 |
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While there is already a handful of such probes available, each of them went through laborious cycles of in vitro screening and selection. Computational modeling approaches are evolving incredibly fast right now and are demonstrating great results in many applications, including de novo protein design. It suggests that the easier task of fine-tuning the fluorogen-binding properties of an already functional protein in silico should be readily achievable. To test this hypothesis, we used Rosetta for computational ligand docking followed by protein binding pocket redesign to further improve the previously described FAP DiB1 that is capable of binding to a BODIPY-like dye M739. Despite an inaccurate initial docking of the chromophore, the incorporated mutations nevertheless improved multiple photophysical parameters as well as the overall performance of the tag. The designed protein, DiB-RM, shows higher brightness, localization precision, and apparent photostability in protein-PAINT super-resolution imaging compared to its parental variant DiB1. Moreover, DiB-RM can be cleaved to obtain an efficient split system with enhanced performance compared to a parental DiB-split system. The possible reasons for the inaccurate ligand binding pose prediction and its consequence on the outcome of the design experiment are further discussed.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1009555</identifier><identifier>PMID: 34748541</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Amino Acid Sequence ; Amino acids ; Binding ; Binding proteins ; Binding sites ; Biology and Life Sciences ; Boron Compounds - chemistry ; Chromophores ; Computational Biology ; Computer applications ; Crystallography, X-Ray ; Design ; Docking ; Drug Design ; Fluorescence ; Fluorescence microscopy ; Fluorescent Dyes - chemistry ; Fluorescent proteins ; Fluoroscopic imaging ; Genetic code ; HEK293 Cells ; Humans ; Image resolution ; Ligands ; Localization ; Luminescent Proteins - chemistry ; Luminescent Proteins - genetics ; Methods ; Microscopy, Fluorescence ; Models, Molecular ; Molecular Docking Simulation ; Mutation ; Performance enhancement ; Physical Sciences ; Protein Conformation ; Protein Engineering - methods ; Protein Engineering - statistics & numerical data ; Proteins ; Recombinant Proteins - chemistry ; Recombinant Proteins - genetics ; Redesign ; Research and Analysis Methods ; Software</subject><ispartof>PLoS computational biology, 2021-11, Vol.17 (11), p.e1009555-e1009555</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Bozhanova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Bozhanova et al 2021 Bozhanova et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c610t-18a0db36f518134287bae54020869d59889cdc6cce8b80a22ba86ec8fda20c483</cites><orcidid>0000-0001-5163-0668 ; 0000-0001-9251-9480 ; 0000-0002-2164-5698 ; 0000-0001-8945-193X ; 0000-0002-4935-7030 ; 0000-0002-5696-594X ; 0000-0002-9116-5606 ; 0000-0001-9962-817X ; 0000-0001-9845-2088 ; 0000-0001-6124-2511</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601599/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601599/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34748541$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Punta, Marco</contributor><creatorcontrib>Bozhanova, Nina G</creatorcontrib><creatorcontrib>Harp, Joel M</creatorcontrib><creatorcontrib>Bender, Brian J</creatorcontrib><creatorcontrib>Gavrikov, Alexey S</creatorcontrib><creatorcontrib>Gorbachev, Dmitry A</creatorcontrib><creatorcontrib>Baranov, Mikhail S</creatorcontrib><creatorcontrib>Mercado, Christina B</creatorcontrib><creatorcontrib>Zhang, Xuan</creatorcontrib><creatorcontrib>Lukyanov, Konstantin A</creatorcontrib><creatorcontrib>Mishin, Alexander S</creatorcontrib><creatorcontrib>Meiler, Jens</creatorcontrib><title>Computational redesign of a fluorogen activating protein with Rosetta</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>The use of unnatural fluorogenic molecules widely expands the pallet of available genetically encoded fluorescent imaging tools through the design of fluorogen activating proteins (FAPs). While there is already a handful of such probes available, each of them went through laborious cycles of in vitro screening and selection. Computational modeling approaches are evolving incredibly fast right now and are demonstrating great results in many applications, including de novo protein design. It suggests that the easier task of fine-tuning the fluorogen-binding properties of an already functional protein in silico should be readily achievable. To test this hypothesis, we used Rosetta for computational ligand docking followed by protein binding pocket redesign to further improve the previously described FAP DiB1 that is capable of binding to a BODIPY-like dye M739. Despite an inaccurate initial docking of the chromophore, the incorporated mutations nevertheless improved multiple photophysical parameters as well as the overall performance of the tag. The designed protein, DiB-RM, shows higher brightness, localization precision, and apparent photostability in protein-PAINT super-resolution imaging compared to its parental variant DiB1. Moreover, DiB-RM can be cleaved to obtain an efficient split system with enhanced performance compared to a parental DiB-split system. 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chemistry</topic><topic>Chromophores</topic><topic>Computational Biology</topic><topic>Computer applications</topic><topic>Crystallography, X-Ray</topic><topic>Design</topic><topic>Docking</topic><topic>Drug Design</topic><topic>Fluorescence</topic><topic>Fluorescence microscopy</topic><topic>Fluorescent Dyes - chemistry</topic><topic>Fluorescent proteins</topic><topic>Fluoroscopic imaging</topic><topic>Genetic code</topic><topic>HEK293 Cells</topic><topic>Humans</topic><topic>Image resolution</topic><topic>Ligands</topic><topic>Localization</topic><topic>Luminescent Proteins - chemistry</topic><topic>Luminescent Proteins - genetics</topic><topic>Methods</topic><topic>Microscopy, Fluorescence</topic><topic>Models, Molecular</topic><topic>Molecular Docking Simulation</topic><topic>Mutation</topic><topic>Performance enhancement</topic><topic>Physical Sciences</topic><topic>Protein Conformation</topic><topic>Protein Engineering - methods</topic><topic>Protein Engineering - statistics & numerical data</topic><topic>Proteins</topic><topic>Recombinant Proteins - 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While there is already a handful of such probes available, each of them went through laborious cycles of in vitro screening and selection. Computational modeling approaches are evolving incredibly fast right now and are demonstrating great results in many applications, including de novo protein design. It suggests that the easier task of fine-tuning the fluorogen-binding properties of an already functional protein in silico should be readily achievable. To test this hypothesis, we used Rosetta for computational ligand docking followed by protein binding pocket redesign to further improve the previously described FAP DiB1 that is capable of binding to a BODIPY-like dye M739. Despite an inaccurate initial docking of the chromophore, the incorporated mutations nevertheless improved multiple photophysical parameters as well as the overall performance of the tag. The designed protein, DiB-RM, shows higher brightness, localization precision, and apparent photostability in protein-PAINT super-resolution imaging compared to its parental variant DiB1. Moreover, DiB-RM can be cleaved to obtain an efficient split system with enhanced performance compared to a parental DiB-split system. 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subjects | Amino Acid Sequence Amino acids Binding Binding proteins Binding sites Biology and Life Sciences Boron Compounds - chemistry Chromophores Computational Biology Computer applications Crystallography, X-Ray Design Docking Drug Design Fluorescence Fluorescence microscopy Fluorescent Dyes - chemistry Fluorescent proteins Fluoroscopic imaging Genetic code HEK293 Cells Humans Image resolution Ligands Localization Luminescent Proteins - chemistry Luminescent Proteins - genetics Methods Microscopy, Fluorescence Models, Molecular Molecular Docking Simulation Mutation Performance enhancement Physical Sciences Protein Conformation Protein Engineering - methods Protein Engineering - statistics & numerical data Proteins Recombinant Proteins - chemistry Recombinant Proteins - genetics Redesign Research and Analysis Methods Software |
title | Computational redesign of a fluorogen activating protein with Rosetta |
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