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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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Performances of the sdENM on individual proteins.The accuracy of the estimation of pairwise residue fluctuations by different ENM variants is illustrated on the basis of two individual proteins. For each protein, 20 randomly selected residue pairs (10 with Å, and 10 with Å) are connected by solid lines. A green line indicates that the amplitude of the fluctuations of the interresidue distance is well estimated by the model. A red (blue) line indicates that the amplitude of the fluctuations of the interresidue distance is largely overestimated (underestimated) by the model. Values larger than 100% or lower than −100% are assimilated to 100% and −100%, respectively. In addition, for each protein and each ENM variant, we report the error  on the estimation of pairwise fluctuations (eq. 15), which accounts for all pairs of residues in the protein. (A,C,E) High quality structural ensemble of ubiquitin, obtained by combining NMR information with molecular dynamics simulations (PDB: 1xqq) [38]. (B,D,F) NMR structural ensemble of periplasmic chaperone FimC (PDB: 1bf8). The relatively rigid orientation of the two domains is ensured by specific interdomain interactions [39]. (A–B) . (C–D) . (E–F) .
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pcbi-1003209-g006: Performances of the sdENM on individual proteins.The accuracy of the estimation of pairwise residue fluctuations by different ENM variants is illustrated on the basis of two individual proteins. For each protein, 20 randomly selected residue pairs (10 with Å, and 10 with Å) are connected by solid lines. A green line indicates that the amplitude of the fluctuations of the interresidue distance is well estimated by the model. A red (blue) line indicates that the amplitude of the fluctuations of the interresidue distance is largely overestimated (underestimated) by the model. Values larger than 100% or lower than −100% are assimilated to 100% and −100%, respectively. In addition, for each protein and each ENM variant, we report the error on the estimation of pairwise fluctuations (eq. 15), which accounts for all pairs of residues in the protein. (A,C,E) High quality structural ensemble of ubiquitin, obtained by combining NMR information with molecular dynamics simulations (PDB: 1xqq) [38]. (B,D,F) NMR structural ensemble of periplasmic chaperone FimC (PDB: 1bf8). The relatively rigid orientation of the two domains is ensured by specific interdomain interactions [39]. (A–B) . (C–D) . (E–F) .

Mentions: Beyond its performances in a mean protein environment, our new model also brings highly notable improvements with respect to previously described ENM variants when it is applied to the specific architecture of a given protein. This is illustrated by two examples, on Figure 6. A more thorough assessment of the ability of the different ENM variants to capture the motions of individual proteins was performed on an independent dataset of 349 proteins. The correlation coefficient between predicted and observed MSRF () has been widely used in the past but ignores the cooperativity inherent to protein dynamics, and presents other shortcomings. Therefore, we introduce a new measure () that quantifies the relative error on the estimation of the variability of the distance between residue pairs, and is thus focused on the cooperative aspects of residue motions (see Methods).


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

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

Performances of the sdENM on individual proteins.The accuracy of the estimation of pairwise residue fluctuations by different ENM variants is illustrated on the basis of two individual proteins. For each protein, 20 randomly selected residue pairs (10 with Å, and 10 with Å) are connected by solid lines. A green line indicates that the amplitude of the fluctuations of the interresidue distance is well estimated by the model. A red (blue) line indicates that the amplitude of the fluctuations of the interresidue distance is largely overestimated (underestimated) by the model. Values larger than 100% or lower than −100% are assimilated to 100% and −100%, respectively. In addition, for each protein and each ENM variant, we report the error  on the estimation of pairwise fluctuations (eq. 15), which accounts for all pairs of residues in the protein. (A,C,E) High quality structural ensemble of ubiquitin, obtained by combining NMR information with molecular dynamics simulations (PDB: 1xqq) [38]. (B,D,F) NMR structural ensemble of periplasmic chaperone FimC (PDB: 1bf8). The relatively rigid orientation of the two domains is ensured by specific interdomain interactions [39]. (A–B) . (C–D) . (E–F) .
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003209-g006: Performances of the sdENM on individual proteins.The accuracy of the estimation of pairwise residue fluctuations by different ENM variants is illustrated on the basis of two individual proteins. For each protein, 20 randomly selected residue pairs (10 with Å, and 10 with Å) are connected by solid lines. A green line indicates that the amplitude of the fluctuations of the interresidue distance is well estimated by the model. A red (blue) line indicates that the amplitude of the fluctuations of the interresidue distance is largely overestimated (underestimated) by the model. Values larger than 100% or lower than −100% are assimilated to 100% and −100%, respectively. In addition, for each protein and each ENM variant, we report the error on the estimation of pairwise fluctuations (eq. 15), which accounts for all pairs of residues in the protein. (A,C,E) High quality structural ensemble of ubiquitin, obtained by combining NMR information with molecular dynamics simulations (PDB: 1xqq) [38]. (B,D,F) NMR structural ensemble of periplasmic chaperone FimC (PDB: 1bf8). The relatively rigid orientation of the two domains is ensured by specific interdomain interactions [39]. (A–B) . (C–D) . (E–F) .
Mentions: Beyond its performances in a mean protein environment, our new model also brings highly notable improvements with respect to previously described ENM variants when it is applied to the specific architecture of a given protein. This is illustrated by two examples, on Figure 6. A more thorough assessment of the ability of the different ENM variants to capture the motions of individual proteins was performed on an independent dataset of 349 proteins. The correlation coefficient between predicted and observed MSRF () has been widely used in the past but ignores the cooperativity inherent to protein dynamics, and presents other shortcomings. Therefore, we introduce a new measure () that quantifies the relative error on the estimation of the variability of the distance between residue pairs, and is thus focused on the cooperative aspects of residue motions (see Methods).

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