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Energy Transfer

Energy Transfer is the process by which energy is conveyed from one molecular, atomic, or subatomic particle to another.
This can occur through various mechanisms, such as radiation, conduction, or resonance energy transfer.
Understanding energy transfer is crucial in fields like photosynthesis, bioluminescence, and nanoscale device development.
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Most cited protocols related to «Energy Transfer»

N- or C-terminal NanoLuc/Kinase fusions were encoded in pFN31K or pFC32K expression vectors (Promega), including flexible Gly-Ser-Ser-Gly linkers between Nluc and each full-length kinase. Optimal orientations for each construct are described in Table S1. For cellular BRET target engagement experiments, HEK-293 or HeLa cells were transfected with NLuc/target fusion constructs using FuGENE HD (Promega) according to the manufacturer’s protocol. Briefly, Nluc/target fusion constructs were diluted into Transfection Carrier DNA (Promega) at a mass ratio of 1:10 (mass/mass), after which FuGENE HD was added at a ratio of 1:3 (μg DNA: μL FuGENE HD). 1 part (vol) of FuGENE HD complexes thus formed were combined with 20 parts (vol) of HEK-293 cells suspended at a density of 2 x 105 per mL, followed by incubation in a humidified, 37°C/5% CO2 incubator for 20 hr. For broad kinase profiling experiments, kinase transfections were performed in 96-well plates using plasmid DNAs arrayed based on energy transfer probe affinity. To simplify the work flow, energy transfer probes were binned based on their optimal concentrations for each kinase target. Based on these groupings, the work flow could be simplified and 178 individual kinases could be queried by a single person, in a single day. The analysis can be performed without any automated liquid handling instruments or robotics. Following transfection, cells were washed and resuspended in Opti-MEM. BRET assays were performed in white, 96-well plates (Corning) at a density of 2 x 104 cells/well. All chemical inhibitors were prepared as concentrated stock solutions in DMSO (Sigma-Aldrich) and diluted in Opti-MEM (unless otherwise noted) to prepare working stocks. Cells were equilibrated for 2 hr with energy transfer probes and test compound prior to BRET measurements. Energy transfer probes were prepared at a working concentration of 20X in tracer dilution buffer (12.5 mM HEPES, 31.25% PEG-400, pH 7.5). For target engagement analysis, the energy transfer probes were added to the cells at concentrations optimized for each target, as described in Table S1. For analysis of DDR1 and DDR2 with compound 6j, energy transfer probe 6 was used at a concentration of 300 nM and 330 nM, respectively. To measure BRET, NanoBRET NanoGlo Substrate and Extracellular NanoLuc Inhibitor (Promega) were added according to the manufacturer’s recommended protocol, and filtered luminescence was measured on a GloMax Discover luminometer equipped with 450 nm BP filter (donor) and 600 nm LP filter (acceptor), using 0.5 s integration time. Milli-BRET units (mBU) are calculated by multiplying the raw BRET values by 1000. Apparent tracer affinity values (EC50) were determined using the sigmoidal dose-response (variable slope) equation available in GraphPad Prism (Equation 1);
Competitive displacement data were then plotted with GraphPad Prism software and data were fit to Equation 1 to determine the IC50 value.
For fractional occupancy determination in kinase profiling experiments, the following equation (Equation 2) was used; where X = BRET in the presence of the test compound and energy transfer probe, Y = BRET in the presence of only energy transfer probe, and Z = BRET in the absence of the energy transfer probe and test compound. Predicted biochemical occupancy at the chosen drug dose for kinase profiling experiments was determined from the Kd value reported previously (Davis et al., 2011 (link)) using a variation of the Langmuir Isotherm (Hulme and Trevethick, 2010 (link)) (Equation 3);
For all BRET data shown, no individual data points were omitted.
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Publication 2018
Biological Assay Buffers Cells Cloning Vectors DNA Energy Transfer FuGene HEK293 Cells HeLa Cells HEPES inhibitors Luminescence Mental Orientation nanoluc Pharmaceutical Preparations Phosphotransferases Plasmids polyethylene glycol 400 prisma Promega Sulfoxide, Dimethyl Technique, Dilution Tissue Donors Transfection
The
test on small molecule free energies of transfer from a vacuum to
water was done on a data set of 504 neutral organic small molecules51 (link) taken from David Mobley’s group. The
solvation energies of all of these molecules have been experimentally
determined, with the range from −11.95 to 3.16 kcal/mol.
Solvation energy Gsol has two components,
polar and nonpolar: where Gpolar indicates
the polar (electrostatic) term and Gnonpolar denotes the nonpolar term.
The polar component of solvation
energy was calculated as the grid
energy difference of the system in water and in a vacuum:
The above grid energies were calculated keeping the corresponding
small molecule at the same grid position to cancel the grid artifacts.
Specific considerations were made for the calculations in a vacuum
since one has to define the molecular surface in this case (the border
between molecule and vacuum). Note that in our approach the dielectric
function is continuous and runs throughout the entire space and is
designed to describe dielectric properties of the molecule in water.
Here, we assume that the properties of molecules are unchanged as
they are moved from water to a vacuum. Thus, following the strategy
implemented in ZAP,40 the molecular surface
of molecules is defined by applying a specific cutoff for the dielectric
constant, εcutoff. The cutoff was varied in the protocol
to obtain the best fit against experimental data.
The nonpolar
term of solvation energy Gnonpolar is
calculated via the accessible surface area method:52 (link) where γ
and b are constants
and SA denotes the solvent accessible surface area, which is calculated
using Naccess2.1.1 (http://www.bioinf.manchester.ac.uk/naccess/).
The force field used in the calculations was AM1-BCC,53 which is part of general AMBER force field (GAFF).50 (link) In order to optimize the parameters, reference
εin was varied from 0.1 to 4.0, the value of normalized
variance σi was varied from 0.80
to 1.40, and the value of epsilon cutoff εbnd was
varied from 4.0 to 60.0. For each combination of the σi and εbnd values, the least-squares
method was used to obtain the optimized γ and b constants. The best parameters are shown in the Results section (note that because of the different nature
of the process, these values are not expected to be the same as those
obtained in pKa calculations. See Discussion for details).
Publication 2013
Amber Electrostatics Energy Transfer Solvents Vacuum
An in-depth description of the fluorescence instrumentation is described in the previous article (Schaaf et al. 2016a) and in the supplemental material (Figure). For lifetime mode, the observed fluorescence waveform was convolved with the instrument response function, and the average energy transfer efficiency (E = 1− τDAD) was calculated from the average lifetimes of donor τD and donor-acceptor, τDA, FRET cell lines. The structural correlates for FRET were modeled as previously described 17 (link)–19 (link) assessing the nanosecond time dependence of the TR-FRET waveforms according to (Eq. 1)–(Eq. 6):
FD(t)=i=12Aiexp(-tτi)
FDA(t)=j=12Xj·Tj(t)
F(t)=xDFD(t)+xDAFDA(t)
Tj(t)=-ρj(R)·i=13Aiexp(-tτi·[1+(R0iR)6])dR
ρj(R)=1σj2πexp(-[R-Rj]22σj2)
σj=FWHMj/(22ln2), where FD is the time-resolved fluorescence decay function of the GFP donor (Eq. 1), best-fit by a two exponential decay (Figure S6). FDA (Eq. 2) is the time-resolved fluorescence decay function of the GFP donor-acceptor FRET sample. FD and FDA were fit to a linear combination of mole fractions of xD and xDA and xD equals zero for the intramolecular FRET sensor (Eq. 3). FDA is a linear combination with molar fraction Xj of two FRET-affected fluorescence decays Tj(t) (Eq. 4). ρj is the probability of each distance distribution, determined by least-squares minimization of the distance (nm) R associated with each donor-acceptor lifetime species τ1 (Eq. 5). (σj ) Gaussian interprobe distance distributions centered at Rj = 5.5 nm and 10.2 nm with distribution widths defined by the standard deviation and full-width half-maximum (Eq. 6). The Förster distance (R0 ) for the eGFP and tagRFP FRET pair is 5.8 nm. The parameters in this system of equations were optimized utilizing simultaneous least-squares minimization to waveforms from donor-only and donor-acceptor cell lines. The best-fit model was indicated by minimized χ2 and by evaluation of the parameter error surface as described in our previous publications 17 (link)–19 (link).
For spectral detection, the observed fluorescence emission spectrum was fitted by least-squares minimization to a linear combination of component spectra:
FFit(λ)=aFD(λ)+bFA(λ)+cFC(λ)+dFW(λ) where D is donor, A is acceptor, C is cell autofluorescence, and W is water Raman, and a, b, c, d are the coefficients determined from the fit.
For an intramolecular FRET sensor, having both donor D and acceptor A, FRET was determined from (Eq. 8), where QR is the ratio of quantum yields (QD/QA) in the absence of FRET, AR is the ratio of molar absorptivities (εAD), both obtained from reported values.20 QR is corrected for spectrograph sensitivity at the appropriate wavelength (Figure S5). The only experimental observable in (Eq. 8) was FR.
Full derivation of (Eqs. 7–. 9)- can be found in the supplementary material.
Publication 2016
Cell Lines Cells Energy Transfer Fluorescence Fluorescence Resonance Energy Transfer Hypersensitivity Molar Nevus Tissue Donors
The original version of our PPM program was modified to optimize positions of molecules in the lipid bilayer with the new solvation model. The computation requires only energy functions and parameters described in equations (142) and Tables S1, S3, S4. A solute molecule was considered as a rigid body whose spatial position was defined by three independent variables: two rotation angles and one translation along the bilayer normal (φ, τ and d, respectively). Transfer energy (equation 19) was optimized by combining grid scan and Davidon-Fletcher-Powell method for local energy minimization. Derivatives of transfer energy with respect to rigid body variables of the molecule were analytically calculated, as described previously22 (link). Derivatives of energy with respect to z were calculated as finite differences with step of 0.01 Å. The hydrophobic thickness of TM proteins was optimized by grid scan for location of the hydrocarbon boundary (ZHDC in eq. 2) with step of 0.05 Å. All other peaks of the lipid and water distributions were shifted accordingly during the optimization to remain at the same distance from the hydrocarbon boundary as in DOPC.
The program uses as input only a set of coordinate files in the PDB format. Unlike the previous version, it allows an automatic determination of transmembrane secondary structures without using any external software. The dipole moments and standard pKa values of different groups are included in the library of amino acid residues or directly in the PDB files for small molecules.
Publication 2011
1,2-oleoylphosphatidylcholine Amino Acids cDNA Library derivatives Energy Transfer Human Body Hydrocarbons Lipid Bilayers Lipids Muscle Rigidity Proteins Radionuclide Imaging
The PerMM method combines the heterogeneous solubility–diffusion theory10 (link) and the anisotropic solvent model of the lipid bilayer characterized by transbilayer profiles of dielectric and hydrogen-bonding capacity parameters.22 (link) PerMM calculates the membrane binding energies (ΔGbind) and the transfer energy profiles (ΔGtransf(z)) of permeants in membranes and obtains their optimal spatial positions and conformations during rotational and translational motion along the membrane normal. The membrane-bound state of a permeant is defined as its conformation and the spatial position in membrane with the lowest transfer energy from water. The integration of free energy profile over the permeation pathway allows evaluation of permeability coefficients of molecules through artificial (BLM) and natural membranes (BBB and Caco-2/MDCK) based on the following equations (all details are provided in the accompanying paper46 (link)):
log PΣBLM=log(ASAd/2d/2dzK(z))
log PcalcBLM=1.063log PΣBLM+3.669
log P0calcBBB=0.375log PΣBLM1.600
log P0calcCaco2/MDCK=0.272log PΣBLM2.541
where K(z) are the local partition coefficients, ASA is the accessible surface area to account for the size-dependence of the diffusivity, log PΣBLM is the integral of the Gibbs free energy of a molecule over the hydrophobic thickness d (30 Å) of the dioleoyl-phosphatidylcholine (DOPC) bilayer. The dependency of the partition coefficient K(z) on the Gibbs free energy (ΔGtransf(z)) of a solute along the membrane normal is calculated as
K(z)=eΔGtransf(z)/RT
The integral of the transbilayer energy profile (eq 1) is calculated in the interval from −15 to +15 Å distance from the membrane center with the step of 1 Å.
Publication 2019
1,2-oleoylphosphatidylcholine Anisotropy Diffusion Energy Transfer Genetic Heterogeneity Lipid Bilayers Permeability Protein Biosynthesis Solvents Tissue, Membrane

Most recents protocols related to «Energy Transfer»

We now consider an alternate estimate of energy associated with burrowing. Consider a cylindrical burrow perpendicular to the sediment–water interface in which the sediment is excavated from the substrate and redeposited at the sediment–water interface. At a minimum, burrow excavation does work against gravity by lifting particles toward the sediment–water interface:
Egr=mgz,
where Egr is the change in gravitational energy [J], m is the mass of sediment moved [M], g is the gravitational acceleration [L] [t−2], and z is the vertical distance [L]. The (buoyant?) mass of some portion of the cylindrical burrow with a vertical thickness of Δz is
m=(1ϕ)(ρsρw)πb2Δz,
where ϕ is the porosity of the sediment, ρs and ρw are the densities of sediment and water, respectively, b is the burrow radius, and z is the distance from the center-of-mass to the sediment–water interface. The work of lifting n segments is
i=1nEgr=i=1n(1ϕ)(ρsρw)πb2Δzgzi.
Taking the limit as n goes to infinity and Δz goes to zero, the work for excavating a cylindrical burrow that extends to some depth d below the sediment–water interface is
Egr=0d(1ϕ)(ρsρw)πb2dzgz=12(1ϕ)(ρsρw)πb2gd2.
The result in Eq. 19 is the same as if we had used the entire mass of the burrow in Eq. 17 and raised the center-of-mass from half the burrow depth to the surface.
We now consider the work done in excavating n identical burrows:
Egr=n12(1ϕ)(ρsρw)πb2gd2
If we approximate the rate at which new burrows are created as a continuous process, then
Egrdt=dndt12(1ϕ)(ρsρw)πb2gd2.
Under this approximation, the rate at which new burrows are created is related to the linear burrowing velocity by
dndt=1ddzdt.
Using Eq. 14, the relationship to the volumetric burrowing rate reported by (22 ) is
dndt=Rπb2d.
Substituting Eq. 23 into Eq. 21, we arrive at
dEgrdt=12(1ϕ)(ρsρw)Rgd.
Dimensional analysis shows that Eq. 24 has units of [J] [L−2] [t−1]. The interpretation of these units is the same as for Eq. 15 in the preceding section: a rate of energy transfer per unit area of the sea floor.
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Publication 2023
Acceleration Energy Transfer Gravitation Gravity Radius
Experimental work by ref. 38 (link) shows that burrowing in cohesive substrates can be modeled as propagating a fracture tip. Under these conditions, the work done on the substrate is
Ecr=Gcwz,
where Ecr is the work [J] required to propagate the crack, z is the distance [L] the crack propagates, Gc [J] [L−2] is the fracture toughness, and w [L] is the width of the crack, which is assumed to be equal to the diameter of the burrow.
If the fracture tip propagates at some linear velocity dzdt , then the rate of working on the sediments is
dEcrdt=Gcwdzdt.
Further interpretation of Eq. 13 requires information about how burrowing rates are measured and reported in the literature. The values used here were reported by ref. 22 as a volume of sediment [L3] reworked by a population of organisms with a certain density at or below the seafloor [individuals] [L−2]. This quantity, which we refer to as the volumetric reworking rate R, has units of [L3] [L−2] [t−1].
To adapt Eq. 13 to the volumetric reworking rate R, we need information regarding body size, specifically the cross-sectional area of the organism which sets the burrow width w. A common method is to approximate an animal’s body by fitting an ellipsoid with a circular cross-section (59 (link)). The relationship between the linear burrowing rate dzdt and the volumetric reworking rate R is then
dzdt=Rπb2,
where the burrow radius, b is half the burrow width w in Eq. 13. Combining Eqs. 13 and 14 casts dEcrdt in terms of the volumetric reworking rate R:
dEcrdt=2GcRπb.
Dimensional analysis of Eq. 15 shows that dEcrdt has units of [J] [L−2] [t−1]. These units represent a rate of energy transfer per unit surface area, which we interpret as the rate at which animals transfer energy across the sediment–water interface.
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Publication 2023
Animals Body Size CD3EAP protein, human Energy Transfer Fracture, Bone Human Body Radius
All BRET assays were performed in white, tissue-culture treated 96-well plates (Corning #3917) using adherent HEK-293 cells at a density of 2 × 104 cells per well. All chemical inhibitors were prepared as concentrated stock solutions in DMSO (Sigma-Aldrich) and diluted in Opti-MEM to prepare working stocks. Cells were equilibrated for 2 h with the appropriate energy transfer probe and test compound prior to BRET measurements. Energy transfer probes were prepared at a working concentration of 20× in Tracer dilution buffer (12.5 mM HEPES, 31.25% PEG-400, pH 7.5). Individual kinase NanoBRET assays used the following energy transfer probes and concentrations: PLK1, probe 11 (0.2 μM); PLK2, probe 11 (1.0 μM); PLK3, probe 11 (1.0 μM); WEE1, tracer K10 (Promega, 0.13 μM). To measure BRET, NanoBRET NanoGlo Substrate and Extracellular NLuc Inhibitor (Promega) were added according to the manufacturer’s recommended protocol, and filtered luminescence was measured on a GloMax Discover luminometer equipped with 450 nm BP filter (donor) and 600 nm LP filter (acceptor), using 0.5 s integration time. BRET ratios are calculated by dividing the acceptor luminescence by the donor luminescence. Milli-BRET (mBRET) units (mBU) are calculated by multiplying the raw BRET ratios by 1,000. Broad spectrum profiling on 192 kinases was performed using the K192 NanoBRET Target Engagement assay (Promega) with tracer K10 using the published protocol [24 (link)].
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Publication Preprint 2023
Biological Assay Buffers Cells Energy Transfer HEK293 Cells HEPES inhibitors Luminescence Phosphotransferases PLK1 protein, human PLK3 protein, human polyethylene glycol 400 Promega Sulfoxide, Dimethyl Technique, Dilution Tissue Donors Tissues
To measure the dissociation of Gαβγ heterotrimer directly, we applied the BRET2 assay system as reported before21 (link). In brief, HEK293T cells were plated in a 6-well plate. After 2 h, cells were transiently co-transfected with plasmids encoding WT or mutated GRR20 together with Gi BRET probe (Gαi1-RLuc8, Gβ3, Gγ9-GFP2) using Lipofectamine 2000 reagent (Life Technologies). Adenosine A2A receptor (A2AR) that does not couple to Gi proteins was used as a negative control, Apelin receptor (APJ) that couples to Gi proteins was used as a positive control for the Gi BRET assay. 24 h after transfection, cells were distributed into a 96-well microplate (30,000–50,000 cells per well) and incubated for additional 24 h at 37 °C. For the constitutive activity measurement, white backings (Perkin Elmer) were applied to the plate bottoms, the transfected cells were washed once with HBSS and supplemented with 100 µL of 5 µM coelenterazine 400a (Nanolight Technologies). Plates were read in EnVision plate reader (Perkin Elmer) with 410 nm (RLuc8) and 515 nm (GFP2) emission filters with an integration time of 1 s per well. The GFP2 emission to RLuc8 emission ratio was used to compute the BRET2 ratios. ΔBRET represent the change of bioluminescence resonance energy transfer value. ΔBRET = BRET ratio (GPCR with G protein sensor) - BRET ratio (only G protein sensor).
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Publication 2023
Adenosine A2A Receptor Apelin Receptors Biological Assay Cells coelenterazine Energy Transfer GTP-Binding Proteins Hemoglobin, Sickle lipofectamine 2000 Plasmids Proteins Strains Transfection Vibration
Thylakoid membranes were suspended in a buffer containing 40 mM HEPES (pH 7.6), 10 mM NaCl, 5 mM MgCl2 and 0.4 M sucrose. The samples were frozen in liquid nitrogen and measured using a spectrofluorometer Jobin Yvon JY3 (Division Instruments S.A., Longjumeau, France) equipped with a red-sensitive photomultiplier and a low-temperature device. The fluorescence emission spectra were measured after excitation with 436 nm (for Chl a) or/and 472 nm (for Chl b). We calculated the chlorophyll emission ratios F735/F685 (for estimation of the energy redistribution between the two photosystems) and F695/F685 (for the energy transfer between chlorophyll–protein complexes in the LHCII–PSII supercomplex [64 ]. Gaussian decomposition of the fluorescence emission spectra was made as in Andreeva et al. [65 (link)].
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Publication 2023
Buffers Chlorophyll Chlorophyll Binding Proteins Cold Temperature Energy Transfer Fluorescence Freezing HEPES Magnesium Chloride Medical Devices Nitrogen Sodium Chloride Sucrose Thylakoid Membrane

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More about "Energy Transfer"

Energy transfer is a fundamental process in various scientific fields, including photosynthesis, bioluminescence, and nanoscale device development.
This process involves the conveyance of energy from one molecular, atomic, or subatomic particle to another, through mechanisms such as radiation, conduction, or resonance energy transfer.
Understanding energy transfer is crucial for advancing research and discoveries in these areas.
PubCompare.ai, a powerful AI-driven platform, simplifies the process of researching energy transfer protocols by comparing data from literature, preprints, and patents.
This allows researchers to identify the most accurate and reproducible methods, ultimately enhancing their scientific findings.
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These advanced instruments and reagents enable precise measurements, data analysis, and visualization, further supporting the exploration of energy transfer mechanisms.
Additionally, the use of an EMCCD camera can provide high-sensitivity imaging, which is crucial for observing and studying energy transfer processes at the nanoscale level.
By leveraging these cutting-edge technologies, researchers can unlock new levels of scientific discovery in the field of energy transfer.
In summary, energy transfer is a fascinating and multifaceted topic that intersects with various scientific disciplines.
PubCompare.ai's AI-driven protocol comparisons, combined with the utilization of specialized equipment and tools, empower researchers to advance their understanding and push the boundaries of scientific knowledge in this important area of study.