Development and Characterization of Flavin-Binding Fluorescent Proteins, Part II: Advanced Characterization

Bitzenhofer, Nora Lisa; Hilgers, Fabienne; Bosio, Gabriela N.; Torra, Joaquim; Casini, Giorgia; Beinlich, Felix; Knieps-Grünhagen, Esther; Gordeliy, Valentin; Jaeger, Karl‐Erich; Nonell, Santi; Krauß, Ulrich; Gensch, Thomas; Drepper, Thomas

doi:10.1007/978-1-0716-2667-2_7

Cited by 3 publications

(4 citation statements)

References 47 publications

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“…Subsequently, the samples were illuminated for 1 min with a blue-light LED (λ = 440 nm, 2.6 mW cm –2 Luxeon Lumileds, Philips; Aachen, Germany), and the light-state spectrum of the protein was recorded. Protein concentration and chromophore loading was estimated from the dark-state spectrum . Chromophore loading can hereby be determined using the following equations loading false( % false) = ( ( Ab normals 450 ε FMN 450 × d ) ( Ab s 280 − Ab s FMN 280 ε Protein 280 × d ) ) × 100 with Abs 450 being the measured absorption at 450 nm, ε FMN450 the molar extinction coefficient of FMN at 450 nm, Abs 280 the measured absorption at 280 nm, Abs FMN280 the contribution of the flavin to the absorption at 280 nm (determined using eq ), and ε Protein280 the molar extinction coefficient of the protein determined based on the amino acid sequence and d being the path length. A b normals F M N 280 = ( ε F M N 280 × [ A b normals 450 ε FMN 450 × d ] × d ) The extinction coefficients of FMN at 280 and 450 nm were determined experimentally to be ε FMN280 = 18,107 M –1 cm –1 and ε FMN450 = 11,765 M –1 cm –1 .…”

Section: Methodsmentioning

confidence: 99%

“…Protein concentration and chromophore loading was estimated from the dark-state spectrum. 65 Chromophore loading can hereby be determined using the following equations with Abs 450 being the measured absorption at 450 nm, ε FMN450 the molar extinction coefficient of FMN at 450 nm, Abs 280 the measured absorption at 280 nm, Abs FMN280 the contribution of the flavin to the absorption at 280 nm (determined using eq 2 ), and ε Protein280 the molar extinction coefficient of the protein determined based on the amino acid sequence and d being the path length. The extinction coefficients of FMN at 280 and 450 nm were determined experimentally to be ε FMN280 = 18,107 M –1 cm –1 and ε FMN450 = 11,765 M –1 cm –1 .…”

Section: Methodsmentioning

confidence: 99%

“…Protein concentration and chromophore loading was estimated from the dark-state spectrum. 65 Chromophore loading can hereby be determined using the following equations i k j j j j j j j j j j j j y…”

Section: Uv−vis Spectroscopy To Determine Sample Quality Photocycling...mentioning

confidence: 99%

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Machine Learning-Assisted Engineering of Light, Oxygen, Voltage Photoreceptor Adduct Lifetime

Hemmer,

Siedhoff,

Werner

et al. 2023

JACS Au

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Naturally occurring and engineered flavin-binding, blue-light-sensing, light, oxygen, voltage (LOV) photoreceptor domains have been used widely to design fluorescent reporters, optogenetic tools, and photosensitizers for the visualization and control of biological processes. In addition, natural LOV photoreceptors with engineered properties were recently employed for optimizing plant biomass production in the framework of a plant-based bioeconomy. Here, the understanding and fine-tuning of LOV photoreceptor (kinetic) properties is instrumental for application. In response to blue-light illumination, LOV domains undergo a cascade of photophysical and photochemical events that yield a transient covalent FMN-cysteine adduct, allowing for signaling. The rate-limiting step of the LOV photocycle is the dark-recovery process, which involves adduct scission and can take between seconds and days. Rational engineering of LOV domains with fine-tuned dark recovery has been challenging due to the lack of a mechanistic model, the long time scale of the process, which hampers atomistic simulations, and a gigantic protein sequence space covering known mutations (combinatorial challenge). To address these issues, we used machine learning (ML) trained on scarce literature data and iteratively generated and implemented experimental data to design LOV variants with faster and slower dark recovery. Over the three prediction–validation cycles, LOV domain variants were successfully predicted, whose adduct-state lifetimes spanned 7 orders of magnitude, yielding optimized tools for synthetic (opto)biology. In summary, our results demonstrate ML as a viable method to guide the design of proteins even with limited experimental data and when no mechanistic model of the underlying physical principles is available.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Machine Learning-Assisted Engineering of Light, Oxygen, Voltage Photoreceptor Adduct Lifetime

Hemmer,

Siedhoff,

Werner

et al. 2023

JACS Au

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“…Determination of advanced properties and fluorescence microscopy imaging follow the procedures outlined in Ref. (49).…”

Section: Methodsmentioning

confidence: 99%

Fine spectral tuning of a flavin-binding fluorescent protein for multicolor imaging

Николаев

Yudenko

Smolentseva

et al. 2022

Preprint

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Flavin-binding fluorescent proteins (FbFPs) are promising genetically encoded tags for microscopy. However, spectral properties of their chromophores (riboflavin, flavin mononucleotide and flavin adenine dinucleotide) are notoriously similar even between different protein families, which limits applications of flavoproteins in multicolor imaging. Here, we present a palette of twenty-two finely tuned fluorescent tags based on the thermostable LOV domain from Chloroflexus aggregans (CagFbFP). We performed site saturation mutagenesis of three amino acid positions in the flavin-binding pocket, including the photoactive cysteine, to obtain variants with fluorescence emission maxima uniformly covering the wavelength range from 486 to 512 nm. We demonstrate three-color imaging based on spectral separation and two-color fluorescence lifetime imaging using the proteins from the palette. These results highlight the possibility of fine spectral tuning of flavoproteins and pave the way for further applications of FbFPs in fluorescence microscopy.

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Fine spectral tuning of a flavin-binding fluorescent protein for multicolor imaging

Николаев

Yudenko²,

Smolentseva³

et al. 2023

Journal of Biological Chemistry

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Development and Characterization of Flavin-Binding Fluorescent Proteins, Part II: Advanced Characterization

Cited by 3 publications

References 47 publications

Machine Learning-Assisted Engineering of Light, Oxygen, Voltage Photoreceptor Adduct Lifetime

Machine Learning-Assisted Engineering of Light, Oxygen, Voltage Photoreceptor Adduct Lifetime

Fine spectral tuning of a flavin-binding fluorescent protein for multicolor imaging

Fine spectral tuning of a flavin-binding fluorescent protein for multicolor imaging

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