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Fluorescence strategies for high-throughput quantification of protein interactions.

Hieb AR, D'Arcy S, Kramer MA, White AE, Luger K - Nucleic Acids Res. (2011)

Bottom Line: Advances in high-throughput characterization of protein networks in vivo have resulted in large databases of unexplored protein interactions that occur during normal cell function.We have overcome many of the previous limitations to thermodynamic quantification of protein interactions, by developing a series of in-solution fluorescence-based strategies.In three case studies we demonstrate how fluorescence (de)quenching and fluorescence resonance energy transfer can be used to quantitatively probe various high-affinity protein-DNA and protein-protein interactions.

View Article: PubMed Central - PubMed

Affiliation: Howard Hughes Medical Institute and Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA.

ABSTRACT
Advances in high-throughput characterization of protein networks in vivo have resulted in large databases of unexplored protein interactions that occur during normal cell function. Their further characterization requires quantitative experimental strategies that are easy to implement in laboratories without specialized equipment. We have overcome many of the previous limitations to thermodynamic quantification of protein interactions, by developing a series of in-solution fluorescence-based strategies. These methods have high sensitivity, a broad dynamic range, and can be performed in a high-throughput manner. In three case studies we demonstrate how fluorescence (de)quenching and fluorescence resonance energy transfer can be used to quantitatively probe various high-affinity protein-DNA and protein-protein interactions. We applied these methods to describe the preference of linker histone H1 for nucleosomes over DNA, the ionic dependence of the DNA repair enzyme PARP1 in DNA binding, and the interaction between the histone chaperone Nap1 and the histone H2A-H2B heterodimer.

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(H2A–H2B)–Nap1 competition as a tool for identification of the binding interface. (A) A cartoon representation showing a competition experiment where the interaction between FRET partners is lost upon addition of unlabeled competitor protein. (B) Binding curve obtained from FRET-based binding between H2A–H2BDonor and Nap1Acceptor. Data were fit to a single exponential (Equation 3) with a Hill-coefficient. (C) Representative competition curves of unlabeled wild-type Nap1 (black filled square with solid lines), Nap11–365(blue filled triangles with solid lines), Nap174–417 (red filled circles with solid lines) and Nap174–365 (violet filled inverted triangles with solid lines) to the (H2A–H2BDonor)–Nap1Acceptor complex. H2A–H2BDonor and Nap1Acceptor remained constant at 10 nM and 50 nM, respectively, with the unlabeled Nap1 protein titrated. Points and error bars represent the average and range of two experimental replicates. R2 values for shown meet or exceed 0.94. (D) Raw images of data from the competition experiment between H2A-H2BDonor and Nap1Acceptor with unlabeled Nap1. From top to bottom; Donor (green), FRET (red) and a pseudo-color overlay of Donor (green) and FRET (red) signals obtained from competitive binding between the FRET pair and unlabeled Nap1. (E) Data plotted from a representative competition experiment showing the raw data (black filled circles with solid lines), background corrected data (blue filled squares with solid lines) or Fcorr (red filled triangles with solid lines) values. Little difference is observed in signal intensity after background correction, but a significant change is observed after spectral overlap subtraction. (F) The same data plotted as in (A), but normalized to highlight the impact of not correcting for spectral overlap. The uncorrected data significantly deviates from the Fcorr curve giving a non-normal IC50 curve.
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gkr1045-F4: (H2A–H2B)–Nap1 competition as a tool for identification of the binding interface. (A) A cartoon representation showing a competition experiment where the interaction between FRET partners is lost upon addition of unlabeled competitor protein. (B) Binding curve obtained from FRET-based binding between H2A–H2BDonor and Nap1Acceptor. Data were fit to a single exponential (Equation 3) with a Hill-coefficient. (C) Representative competition curves of unlabeled wild-type Nap1 (black filled square with solid lines), Nap11–365(blue filled triangles with solid lines), Nap174–417 (red filled circles with solid lines) and Nap174–365 (violet filled inverted triangles with solid lines) to the (H2A–H2BDonor)–Nap1Acceptor complex. H2A–H2BDonor and Nap1Acceptor remained constant at 10 nM and 50 nM, respectively, with the unlabeled Nap1 protein titrated. Points and error bars represent the average and range of two experimental replicates. R2 values for shown meet or exceed 0.94. (D) Raw images of data from the competition experiment between H2A-H2BDonor and Nap1Acceptor with unlabeled Nap1. From top to bottom; Donor (green), FRET (red) and a pseudo-color overlay of Donor (green) and FRET (red) signals obtained from competitive binding between the FRET pair and unlabeled Nap1. (E) Data plotted from a representative competition experiment showing the raw data (black filled circles with solid lines), background corrected data (blue filled squares with solid lines) or Fcorr (red filled triangles with solid lines) values. Little difference is observed in signal intensity after background correction, but a significant change is observed after spectral overlap subtraction. (F) The same data plotted as in (A), but normalized to highlight the impact of not correcting for spectral overlap. The uncorrected data significantly deviates from the Fcorr curve giving a non-normal IC50 curve.

Mentions: H2A–H2BDonor (10 nM) was combined with wild-type Nap1Acceptor (50 nM), and increasing amounts of unlabeled wild-type or mutant Nap1 was added to compete with the (H2A-H2BDonor)–Nap1Acceptor complex. The competition was monitored by a loss of FRET between wild-type Nap1Acceptor and H2A–H2BDonor (Figure 4A). The Fcorr values were fit to extract the IC50 value. The IC50 is the concentration of competitor needed to reduce the amount of (H2A–H2BDonor)–Nap1Acceptor complex signal by 50%, and is proportional to the Nap1Acceptor concentration and the competitor's binding affinity, as described in ‘Methods’ section (16). Labeled Nap1 is kept >5-fold over the KD to ensure that the IC50 is proportional to the KD; however, this is not absolutely necessary (16). H2A–H2BDonor concentration was kept 5-fold below the Nap1Acceptor concentration to eliminate the possibility of free H2A–H2BDonor in the system.Figure 4.


Fluorescence strategies for high-throughput quantification of protein interactions.

Hieb AR, D'Arcy S, Kramer MA, White AE, Luger K - Nucleic Acids Res. (2011)

(H2A–H2B)–Nap1 competition as a tool for identification of the binding interface. (A) A cartoon representation showing a competition experiment where the interaction between FRET partners is lost upon addition of unlabeled competitor protein. (B) Binding curve obtained from FRET-based binding between H2A–H2BDonor and Nap1Acceptor. Data were fit to a single exponential (Equation 3) with a Hill-coefficient. (C) Representative competition curves of unlabeled wild-type Nap1 (black filled square with solid lines), Nap11–365(blue filled triangles with solid lines), Nap174–417 (red filled circles with solid lines) and Nap174–365 (violet filled inverted triangles with solid lines) to the (H2A–H2BDonor)–Nap1Acceptor complex. H2A–H2BDonor and Nap1Acceptor remained constant at 10 nM and 50 nM, respectively, with the unlabeled Nap1 protein titrated. Points and error bars represent the average and range of two experimental replicates. R2 values for shown meet or exceed 0.94. (D) Raw images of data from the competition experiment between H2A-H2BDonor and Nap1Acceptor with unlabeled Nap1. From top to bottom; Donor (green), FRET (red) and a pseudo-color overlay of Donor (green) and FRET (red) signals obtained from competitive binding between the FRET pair and unlabeled Nap1. (E) Data plotted from a representative competition experiment showing the raw data (black filled circles with solid lines), background corrected data (blue filled squares with solid lines) or Fcorr (red filled triangles with solid lines) values. Little difference is observed in signal intensity after background correction, but a significant change is observed after spectral overlap subtraction. (F) The same data plotted as in (A), but normalized to highlight the impact of not correcting for spectral overlap. The uncorrected data significantly deviates from the Fcorr curve giving a non-normal IC50 curve.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3299996&req=5

gkr1045-F4: (H2A–H2B)–Nap1 competition as a tool for identification of the binding interface. (A) A cartoon representation showing a competition experiment where the interaction between FRET partners is lost upon addition of unlabeled competitor protein. (B) Binding curve obtained from FRET-based binding between H2A–H2BDonor and Nap1Acceptor. Data were fit to a single exponential (Equation 3) with a Hill-coefficient. (C) Representative competition curves of unlabeled wild-type Nap1 (black filled square with solid lines), Nap11–365(blue filled triangles with solid lines), Nap174–417 (red filled circles with solid lines) and Nap174–365 (violet filled inverted triangles with solid lines) to the (H2A–H2BDonor)–Nap1Acceptor complex. H2A–H2BDonor and Nap1Acceptor remained constant at 10 nM and 50 nM, respectively, with the unlabeled Nap1 protein titrated. Points and error bars represent the average and range of two experimental replicates. R2 values for shown meet or exceed 0.94. (D) Raw images of data from the competition experiment between H2A-H2BDonor and Nap1Acceptor with unlabeled Nap1. From top to bottom; Donor (green), FRET (red) and a pseudo-color overlay of Donor (green) and FRET (red) signals obtained from competitive binding between the FRET pair and unlabeled Nap1. (E) Data plotted from a representative competition experiment showing the raw data (black filled circles with solid lines), background corrected data (blue filled squares with solid lines) or Fcorr (red filled triangles with solid lines) values. Little difference is observed in signal intensity after background correction, but a significant change is observed after spectral overlap subtraction. (F) The same data plotted as in (A), but normalized to highlight the impact of not correcting for spectral overlap. The uncorrected data significantly deviates from the Fcorr curve giving a non-normal IC50 curve.
Mentions: H2A–H2BDonor (10 nM) was combined with wild-type Nap1Acceptor (50 nM), and increasing amounts of unlabeled wild-type or mutant Nap1 was added to compete with the (H2A-H2BDonor)–Nap1Acceptor complex. The competition was monitored by a loss of FRET between wild-type Nap1Acceptor and H2A–H2BDonor (Figure 4A). The Fcorr values were fit to extract the IC50 value. The IC50 is the concentration of competitor needed to reduce the amount of (H2A–H2BDonor)–Nap1Acceptor complex signal by 50%, and is proportional to the Nap1Acceptor concentration and the competitor's binding affinity, as described in ‘Methods’ section (16). Labeled Nap1 is kept >5-fold over the KD to ensure that the IC50 is proportional to the KD; however, this is not absolutely necessary (16). H2A–H2BDonor concentration was kept 5-fold below the Nap1Acceptor concentration to eliminate the possibility of free H2A–H2BDonor in the system.Figure 4.

Bottom Line: Advances in high-throughput characterization of protein networks in vivo have resulted in large databases of unexplored protein interactions that occur during normal cell function.We have overcome many of the previous limitations to thermodynamic quantification of protein interactions, by developing a series of in-solution fluorescence-based strategies.In three case studies we demonstrate how fluorescence (de)quenching and fluorescence resonance energy transfer can be used to quantitatively probe various high-affinity protein-DNA and protein-protein interactions.

View Article: PubMed Central - PubMed

Affiliation: Howard Hughes Medical Institute and Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA.

ABSTRACT
Advances in high-throughput characterization of protein networks in vivo have resulted in large databases of unexplored protein interactions that occur during normal cell function. Their further characterization requires quantitative experimental strategies that are easy to implement in laboratories without specialized equipment. We have overcome many of the previous limitations to thermodynamic quantification of protein interactions, by developing a series of in-solution fluorescence-based strategies. These methods have high sensitivity, a broad dynamic range, and can be performed in a high-throughput manner. In three case studies we demonstrate how fluorescence (de)quenching and fluorescence resonance energy transfer can be used to quantitatively probe various high-affinity protein-DNA and protein-protein interactions. We applied these methods to describe the preference of linker histone H1 for nucleosomes over DNA, the ionic dependence of the DNA repair enzyme PARP1 in DNA binding, and the interaction between the histone chaperone Nap1 and the histone H2A-H2B heterodimer.

Show MeSH
Related in: MedlinePlus