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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Autores principales:	Nina G. Bozhanova, Joel M. Harp, Brian J. Bender, Alexey S. Gavrikov, Dmitry A. Gorbachev, Mikhail S. Baranov, Christina B. Mercado, Xuan Zhang, Konstantin A. Lukyanov, Alexander S. Mishin, Jens Meiler
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/826bc1b7f0f148b8ad96f37c2d2ca4db
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spelling	oai:doaj.org-article:826bc1b7f0f148b8ad96f37c2d2ca4db2021-11-25T05:41:32ZComputational redesign of a fluorogen activating protein with Rosetta1553-734X1553-7358https://doaj.org/article/826bc1b7f0f148b8ad96f37c2d2ca4db2021-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601599/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The 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. Author summary Computational approaches have recently made significant progress in the protein engineering field evolving from a tool for helping experimentalists to prioritize or short-list mutations for testing to being capable of making fully reliable predictions. However, not all the fields of protein modeling are evolving at a similar pace. That is why evaluating the capabilities of computational tools on different tasks is important to provide other scientists with up-to-date information on the state of the field. Here we tested the performance of Rosetta (one of the leading macromolecule modeling tools) in improving small molecule-binding proteins. We successfully redesigned a fluorogen binding protein DiB1 –a protein that binds a non-fluorescent molecule and enforces its fluorescence in the obtained complex–for improved brightness and better performance in super-resolution imaging. Our results suggest that such tasks can be already achieved without laborious library screenings. However, the flexibility of the proteins might still be underestimated during standard modeling protocols and should be closely evaluated.Nina G. BozhanovaJoel M. HarpBrian J. BenderAlexey S. GavrikovDmitry A. GorbachevMikhail S. BaranovChristina B. MercadoXuan ZhangKonstantin A. LukyanovAlexander S. MishinJens MeilerPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Nina G. Bozhanova Joel M. Harp Brian J. Bender Alexey S. Gavrikov Dmitry A. Gorbachev Mikhail S. Baranov Christina B. Mercado Xuan Zhang Konstantin A. Lukyanov Alexander S. Mishin Jens Meiler Computational redesign of a fluorogen activating protein with Rosetta
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. Author summary Computational approaches have recently made significant progress in the protein engineering field evolving from a tool for helping experimentalists to prioritize or short-list mutations for testing to being capable of making fully reliable predictions. However, not all the fields of protein modeling are evolving at a similar pace. That is why evaluating the capabilities of computational tools on different tasks is important to provide other scientists with up-to-date information on the state of the field. Here we tested the performance of Rosetta (one of the leading macromolecule modeling tools) in improving small molecule-binding proteins. We successfully redesigned a fluorogen binding protein DiB1 –a protein that binds a non-fluorescent molecule and enforces its fluorescence in the obtained complex–for improved brightness and better performance in super-resolution imaging. Our results suggest that such tasks can be already achieved without laborious library screenings. However, the flexibility of the proteins might still be underestimated during standard modeling protocols and should be closely evaluated.
format	article
author	Nina G. Bozhanova Joel M. Harp Brian J. Bender Alexey S. Gavrikov Dmitry A. Gorbachev Mikhail S. Baranov Christina B. Mercado Xuan Zhang Konstantin A. Lukyanov Alexander S. Mishin Jens Meiler
author_facet	Nina G. Bozhanova Joel M. Harp Brian J. Bender Alexey S. Gavrikov Dmitry A. Gorbachev Mikhail S. Baranov Christina B. Mercado Xuan Zhang Konstantin A. Lukyanov Alexander S. Mishin Jens Meiler
author_sort	Nina G. Bozhanova
title	Computational redesign of a fluorogen activating protein with Rosetta
title_short	Computational redesign of a fluorogen activating protein with Rosetta
title_full	Computational redesign of a fluorogen activating protein with Rosetta
title_fullStr	Computational redesign of a fluorogen activating protein with Rosetta
title_full_unstemmed	Computational redesign of a fluorogen activating protein with Rosetta
title_sort	computational redesign of a fluorogen activating protein with rosetta
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/826bc1b7f0f148b8ad96f37c2d2ca4db
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Computational redesign of a fluorogen activating protein with Rosetta

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