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Effective harmonic potentials: insights into the internal cooperativity and sequence-specificity of protein dynamics.

Dehouck Y, Mikhailov AS - PLoS Comput. Biol. (2013)

Bottom Line: In particular, the elastic network model, in which residue motions are subjected to pairwise harmonic potentials, is known to capture essential aspects of conformational dynamics in proteins, but has so far remained mostly phenomenological, and unable to account for the chemical specificities of amino acids.These potentials constitute dynamical counterparts to the mean-force statistical potentials commonly used for static analyses of protein structures.In the context of the elastic network model, they yield a strongly improved description of the cooperative aspects of residue motions, and give the opportunity to systematically explore the influence of sequence details on protein dynamics.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Chemistry, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany. ydehouck@ulb.ac.be

ABSTRACT
The proper biological functioning of proteins often relies on the occurrence of coordinated fluctuations around their native structure, or on their ability to perform wider and sometimes highly elaborated motions. Hence, there is considerable interest in the definition of accurate coarse-grained descriptions of protein dynamics, as an alternative to more computationally expensive approaches. In particular, the elastic network model, in which residue motions are subjected to pairwise harmonic potentials, is known to capture essential aspects of conformational dynamics in proteins, but has so far remained mostly phenomenological, and unable to account for the chemical specificities of amino acids. We propose, for the first time, a method to derive residue- and distance-specific effective harmonic potentials from the statistical analysis of an extensive dataset of NMR conformational ensembles. These potentials constitute dynamical counterparts to the mean-force statistical potentials commonly used for static analyses of protein structures. In the context of the elastic network model, they yield a strongly improved description of the cooperative aspects of residue motions, and give the opportunity to systematically explore the influence of sequence details on protein dynamics.

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Comparison of the experimental and predicted values of the apparent stiffness .For each amino acid, the median value of  over the 20 possible partners is given in units of , along with the maximal, minimal,  and  quartile values. Outliers from these distributions are depicted as circles. (A) Experimental values of , extracted from the dataset of 1500 NMR ensembles. (B) Experimental values of , extracted from the same dataset. (C) Values of  predicted by the , on the same dataset. (D) Values of  predicted by the , on the same dataset.
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pcbi-1003209-g003: Comparison of the experimental and predicted values of the apparent stiffness .For each amino acid, the median value of over the 20 possible partners is given in units of , along with the maximal, minimal, and quartile values. Outliers from these distributions are depicted as circles. (A) Experimental values of , extracted from the dataset of 1500 NMR ensembles. (B) Experimental values of , extracted from the same dataset. (C) Values of predicted by the , on the same dataset. (D) Values of predicted by the , on the same dataset.

Mentions: The apparent stiffness is computed for each type of amino acid pair using eq. 2, by considering only residue pairs separated by less than 10 Å. As shown in Figure 3A, the chemical nature of the interacting residues is a major determinant of their dynamical behavior. Unsurprisingly, Glycine and Proline appear as the most effective ingredients of flexibility. Pairs involving hydrophobic and aromatic amino acids tend to be considerably more rigid, with values up to 6 times larger. These differences originate in part in the individual propensities of different amino acids to be located in more or less flexible regions (e.g. hydrophobic core vs. exposed surface loops). However, there is only a limited agreement between and (Figure 3A–B): the correlation coefficient is equal to 0.71, and spans a much wider range of values. Beyond individual amino acid preferences, the specifics of residue-residue interactions play thus a significant role in determining the extent of cooperativity in residue motions.


Effective harmonic potentials: insights into the internal cooperativity and sequence-specificity of protein dynamics.

Dehouck Y, Mikhailov AS - PLoS Comput. Biol. (2013)

Comparison of the experimental and predicted values of the apparent stiffness .For each amino acid, the median value of  over the 20 possible partners is given in units of , along with the maximal, minimal,  and  quartile values. Outliers from these distributions are depicted as circles. (A) Experimental values of , extracted from the dataset of 1500 NMR ensembles. (B) Experimental values of , extracted from the same dataset. (C) Values of  predicted by the , on the same dataset. (D) Values of  predicted by the , on the same dataset.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3757084&req=5

pcbi-1003209-g003: Comparison of the experimental and predicted values of the apparent stiffness .For each amino acid, the median value of over the 20 possible partners is given in units of , along with the maximal, minimal, and quartile values. Outliers from these distributions are depicted as circles. (A) Experimental values of , extracted from the dataset of 1500 NMR ensembles. (B) Experimental values of , extracted from the same dataset. (C) Values of predicted by the , on the same dataset. (D) Values of predicted by the , on the same dataset.
Mentions: The apparent stiffness is computed for each type of amino acid pair using eq. 2, by considering only residue pairs separated by less than 10 Å. As shown in Figure 3A, the chemical nature of the interacting residues is a major determinant of their dynamical behavior. Unsurprisingly, Glycine and Proline appear as the most effective ingredients of flexibility. Pairs involving hydrophobic and aromatic amino acids tend to be considerably more rigid, with values up to 6 times larger. These differences originate in part in the individual propensities of different amino acids to be located in more or less flexible regions (e.g. hydrophobic core vs. exposed surface loops). However, there is only a limited agreement between and (Figure 3A–B): the correlation coefficient is equal to 0.71, and spans a much wider range of values. Beyond individual amino acid preferences, the specifics of residue-residue interactions play thus a significant role in determining the extent of cooperativity in residue motions.

Bottom Line: In particular, the elastic network model, in which residue motions are subjected to pairwise harmonic potentials, is known to capture essential aspects of conformational dynamics in proteins, but has so far remained mostly phenomenological, and unable to account for the chemical specificities of amino acids.These potentials constitute dynamical counterparts to the mean-force statistical potentials commonly used for static analyses of protein structures.In the context of the elastic network model, they yield a strongly improved description of the cooperative aspects of residue motions, and give the opportunity to systematically explore the influence of sequence details on protein dynamics.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Chemistry, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany. ydehouck@ulb.ac.be

ABSTRACT
The proper biological functioning of proteins often relies on the occurrence of coordinated fluctuations around their native structure, or on their ability to perform wider and sometimes highly elaborated motions. Hence, there is considerable interest in the definition of accurate coarse-grained descriptions of protein dynamics, as an alternative to more computationally expensive approaches. In particular, the elastic network model, in which residue motions are subjected to pairwise harmonic potentials, is known to capture essential aspects of conformational dynamics in proteins, but has so far remained mostly phenomenological, and unable to account for the chemical specificities of amino acids. We propose, for the first time, a method to derive residue- and distance-specific effective harmonic potentials from the statistical analysis of an extensive dataset of NMR conformational ensembles. These potentials constitute dynamical counterparts to the mean-force statistical potentials commonly used for static analyses of protein structures. In the context of the elastic network model, they yield a strongly improved description of the cooperative aspects of residue motions, and give the opportunity to systematically explore the influence of sequence details on protein dynamics.

Show MeSH
Related in: MedlinePlus