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Identification of DNA-binding protein target sequences by physical effective energy functions: free energy analysis of lambda repressor-DNA complexes.

Moroni E, Caselle M, Fogolari F - BMC Struct. Biol. (2007)

Bottom Line: The effect of conformational sampling by Molecular Dynamics simulations on the computed binding energy is assessed; results show that this effect is in general negative and the reproducibility of the experimental values decreases with the increase of simulation time considered.As a results of these analyses, we propose a protocol for the prediction of DNA-binding target sequences.This study supports the conclusion that physics-based methods can offer a completely complementary methodology to sequence-based methods for the identification of DNA-binding protein target sequences.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dipartimento di Fisica Teorica, Universià di Torino and INFN, Via P, Giuria 1, 10125 Torino, Italy. moroni@to.infn.it

ABSTRACT

Background: Specific binding of proteins to DNA is one of the most common ways gene expression is controlled. Although general rules for the DNA-protein recognition can be derived, the ambiguous and complex nature of this mechanism precludes a simple recognition code, therefore the prediction of DNA target sequences is not straightforward. DNA-protein interactions can be studied using computational methods which can complement the current experimental methods and offer some advantages. In the present work we use physical effective potentials to evaluate the DNA-protein binding affinities for the lambda repressor-DNA complex for which structural and thermodynamic experimental data are available.

Results: The binding free energy of two molecules can be expressed as the sum of an intermolecular energy (evaluated using a molecular mechanics forcefield), a solvation free energy term and an entropic term. Different solvation models are used including distance dependent dielectric constants, solvent accessible surface tension models and the Generalized Born model. The effect of conformational sampling by Molecular Dynamics simulations on the computed binding energy is assessed; results show that this effect is in general negative and the reproducibility of the experimental values decreases with the increase of simulation time considered. The free energy of binding for non-specific complexes, estimated using the best energetic model, agrees with earlier theoretical suggestions. As a results of these analyses, we propose a protocol for the prediction of DNA-binding target sequences. The possibility of searching regulatory elements within the bacteriophage lambda genome using this protocol is explored. Our analysis shows good prediction capabilities, even in absence of any thermodynamic data and information on the naturally recognized sequence.

Conclusion: This study supports the conclusion that physics-based methods can offer a completely complementary methodology to sequence-based methods for the identification of DNA-binding protein target sequences.

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Related in: MedlinePlus

Computed binding free energies (MM/DDDC-OONS(+HB) model) versus experimental measurements, using a distance dependent dielectric constant (ε = 1r, 2r, 4r, 8r). The correlation coefficients between calculated and experimental values are 0.543, 0.667, 0.703 and 0.701 for ε = 1r, 2r, 4r, 8r, respectively.
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Figure 1: Computed binding free energies (MM/DDDC-OONS(+HB) model) versus experimental measurements, using a distance dependent dielectric constant (ε = 1r, 2r, 4r, 8r). The correlation coefficients between calculated and experimental values are 0.543, 0.667, 0.703 and 0.701 for ε = 1r, 2r, 4r, 8r, respectively.

Mentions: The best correlation coefficient (r = 0.703 for MM/DDDC-OONS(+HB) model) has been obtained for ε = 4r, although values of ε = 2r and ε = 8r gave very similar results for both MM/DDDC-OONS and MM/DDDC-OONS(+HB) models. Except for the MM/DDDC-OONS(+HB) model with ε = 1r, the F-statistic shows that the model is significant (p < 0.001). The dielectric constant ε = 1r, which gives the worst results tends in many cases to overestimate binding free energy changes lower than 1.0 kcal/mol whereas binding free energy changes greater than 2 kcal/mol are underestimated. A similar behaviour has been observed for ε = 2r, 4r, 8r, even if these models are able to better reproduce binding free energy changes, in particular improvements have been obtained for values lower than 1.0 kcal/mol (Figure 1). The analysis of the best scaling coefficients is not straightforward because there is a strong correlation between the energy terms. For instance, for all ε models the electrostatic term is strongly anticorrelated with the OONS solvation term.


Identification of DNA-binding protein target sequences by physical effective energy functions: free energy analysis of lambda repressor-DNA complexes.

Moroni E, Caselle M, Fogolari F - BMC Struct. Biol. (2007)

Computed binding free energies (MM/DDDC-OONS(+HB) model) versus experimental measurements, using a distance dependent dielectric constant (ε = 1r, 2r, 4r, 8r). The correlation coefficients between calculated and experimental values are 0.543, 0.667, 0.703 and 0.701 for ε = 1r, 2r, 4r, 8r, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2194778&req=5

Figure 1: Computed binding free energies (MM/DDDC-OONS(+HB) model) versus experimental measurements, using a distance dependent dielectric constant (ε = 1r, 2r, 4r, 8r). The correlation coefficients between calculated and experimental values are 0.543, 0.667, 0.703 and 0.701 for ε = 1r, 2r, 4r, 8r, respectively.
Mentions: The best correlation coefficient (r = 0.703 for MM/DDDC-OONS(+HB) model) has been obtained for ε = 4r, although values of ε = 2r and ε = 8r gave very similar results for both MM/DDDC-OONS and MM/DDDC-OONS(+HB) models. Except for the MM/DDDC-OONS(+HB) model with ε = 1r, the F-statistic shows that the model is significant (p < 0.001). The dielectric constant ε = 1r, which gives the worst results tends in many cases to overestimate binding free energy changes lower than 1.0 kcal/mol whereas binding free energy changes greater than 2 kcal/mol are underestimated. A similar behaviour has been observed for ε = 2r, 4r, 8r, even if these models are able to better reproduce binding free energy changes, in particular improvements have been obtained for values lower than 1.0 kcal/mol (Figure 1). The analysis of the best scaling coefficients is not straightforward because there is a strong correlation between the energy terms. For instance, for all ε models the electrostatic term is strongly anticorrelated with the OONS solvation term.

Bottom Line: The effect of conformational sampling by Molecular Dynamics simulations on the computed binding energy is assessed; results show that this effect is in general negative and the reproducibility of the experimental values decreases with the increase of simulation time considered.As a results of these analyses, we propose a protocol for the prediction of DNA-binding target sequences.This study supports the conclusion that physics-based methods can offer a completely complementary methodology to sequence-based methods for the identification of DNA-binding protein target sequences.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dipartimento di Fisica Teorica, Universià di Torino and INFN, Via P, Giuria 1, 10125 Torino, Italy. moroni@to.infn.it

ABSTRACT

Background: Specific binding of proteins to DNA is one of the most common ways gene expression is controlled. Although general rules for the DNA-protein recognition can be derived, the ambiguous and complex nature of this mechanism precludes a simple recognition code, therefore the prediction of DNA target sequences is not straightforward. DNA-protein interactions can be studied using computational methods which can complement the current experimental methods and offer some advantages. In the present work we use physical effective potentials to evaluate the DNA-protein binding affinities for the lambda repressor-DNA complex for which structural and thermodynamic experimental data are available.

Results: The binding free energy of two molecules can be expressed as the sum of an intermolecular energy (evaluated using a molecular mechanics forcefield), a solvation free energy term and an entropic term. Different solvation models are used including distance dependent dielectric constants, solvent accessible surface tension models and the Generalized Born model. The effect of conformational sampling by Molecular Dynamics simulations on the computed binding energy is assessed; results show that this effect is in general negative and the reproducibility of the experimental values decreases with the increase of simulation time considered. The free energy of binding for non-specific complexes, estimated using the best energetic model, agrees with earlier theoretical suggestions. As a results of these analyses, we propose a protocol for the prediction of DNA-binding target sequences. The possibility of searching regulatory elements within the bacteriophage lambda genome using this protocol is explored. Our analysis shows good prediction capabilities, even in absence of any thermodynamic data and information on the naturally recognized sequence.

Conclusion: This study supports the conclusion that physics-based methods can offer a completely complementary methodology to sequence-based methods for the identification of DNA-binding protein target sequences.

Show MeSH
Related in: MedlinePlus