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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 .Experimental values of  (continuous black) and  (dashed black), extracted from the dataset of 1500 NMR ensembles. Values of  predicted on the same dataset by the  (dashed red);  (continuous red);  (dashed blue);  (continuous blue).
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pcbi-1003209-g002: Comparison of the experimental and predicted values of the apparent stiffness .Experimental values of (continuous black) and (dashed black), extracted from the dataset of 1500 NMR ensembles. Values of predicted on the same dataset by the (dashed red); (continuous red); (dashed blue); (continuous blue).

Mentions: The influence of the distance separating two residues on the cooperativity of their motions can be investigated by considering amino acid types indistinctively in eq. 2. Interestingly, follows approximately a power law, with an exponent of about −2.5 (Figure 2). Finer details include a first maximal value occurring for – distances between 5 and 5.5 Å, i.e. the separation between hydrogen-bonded residues within regular secondary structure elements, and a second around 9 Å, which corresponds to indirect, second neighbor, interactions. The high level of cooperativity in residue motions is well illustrated by the comparison of and its uncorrelated counterpart . Indeed, these two functions would take identical values if the variability of the distance between two residues could be explained solely by the extent of their individual fluctuations. In a mean protein environment, however, is about two orders of magnitude larger than at short-range, and the difference remains quite important up to about 30–40 Å.


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 .Experimental values of  (continuous black) and  (dashed black), extracted from the dataset of 1500 NMR ensembles. Values of  predicted on the same dataset by the  (dashed red);  (continuous red);  (dashed blue);  (continuous blue).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003209-g002: Comparison of the experimental and predicted values of the apparent stiffness .Experimental values of (continuous black) and (dashed black), extracted from the dataset of 1500 NMR ensembles. Values of predicted on the same dataset by the (dashed red); (continuous red); (dashed blue); (continuous blue).
Mentions: The influence of the distance separating two residues on the cooperativity of their motions can be investigated by considering amino acid types indistinctively in eq. 2. Interestingly, follows approximately a power law, with an exponent of about −2.5 (Figure 2). Finer details include a first maximal value occurring for – distances between 5 and 5.5 Å, i.e. the separation between hydrogen-bonded residues within regular secondary structure elements, and a second around 9 Å, which corresponds to indirect, second neighbor, interactions. The high level of cooperativity in residue motions is well illustrated by the comparison of and its uncorrelated counterpart . Indeed, these two functions would take identical values if the variability of the distance between two residues could be explained solely by the extent of their individual fluctuations. In a mean protein environment, however, is about two orders of magnitude larger than at short-range, and the difference remains quite important up to about 30–40 Å.

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