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Stochastic reconstruction of protein structures from effective connectivity profiles.

Wolff K, Vendruscolo M, Porto M - PMC Biophys (2008)

Bottom Line: Structure information thus enters the folding dynamics via the effective connectivity, but the interaction is not restricted to pairs of amino acids that form native contacts, resulting in a free energy landscape which does not rely on the assumption of minimal frustration.Moreover, effective connectivity vectors can be predicted more readily from the amino acid sequence of proteins than the corresponding contact maps, thus suggesting that the stochastic protocol presented here could be effectively combined with other current methods for predicting native structures.PACS codes: 87.14.Ee.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut für Festkörperphysik, Technische Universität Darmstadt, Hochschulstrasse 6, 64289 Darmstadt, Germany. porto@fkp.tu-darmstadt.de.

ABSTRACT
We discuss a stochastic approach for reconstructing the native structures of proteins from the knowledge of the "effective connectivity", which is a one-dimensional structural profile constructed as a linear combination of the eigenvectors of the contact map of the target structure. The structural profile is used to bias a search of the conformational space towards the target structure in a Monte Carlo scheme operating on a Calpha-chain of uniform, finite thickness. Structure information thus enters the folding dynamics via the effective connectivity, but the interaction is not restricted to pairs of amino acids that form native contacts, resulting in a free energy landscape which does not rely on the assumption of minimal frustration. Moreover, effective connectivity vectors can be predicted more readily from the amino acid sequence of proteins than the corresponding contact maps, thus suggesting that the stochastic protocol presented here could be effectively combined with other current methods for predicting native structures. PACS codes: 87.14.Ee.

No MeSH data available.


Reconstruction of 1k1v. Comparison between the target (red) and reconstructed (blue) structures of the DNA-binding domain of MafG (PDB code 1k1v). The RMSD between the two structures is 2.4Å.
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Figure 6: Reconstruction of 1k1v. Comparison between the target (red) and reconstructed (blue) structures of the DNA-binding domain of MafG (PDB code 1k1v). The RMSD between the two structures is 2.4Å.

Mentions: The root mean square deviation (RMSD) of Cα atoms ranges between 2.2Å and 9.6Å over the entire chain. We explain these variations by the following considerations. The omission of unstructured tails at the ends improves the overall RMSD to between 1.5Å and 9.1Å. More importantly, there were many non-compact structures in the structure set. Compactness appears to be the crucial condition for low RMSDs, even more than a high content of secondary structure. Thus, compact structures (Fig. 6) were reconstructed to low RMSDs while less compact ones (Fig. 7) were considerably deformed even though qfull > 90%. This behaviour can often be observed for single α-helices or two-bundles as the helices that are at most weakly inter-connected can be deformed considerably without breaking the contact pattern (see Fig. 7). Here, the resolution is almost as good as a Gō-model of the full, unrestricted structure would achieve. In the case of α-helices the problem could likely be eased by an improved version of the helix energy Ehelix, for example by enforcing straight helix axes, but non-compact structures were generally problematic. Other structures can nicely be aligned piecewise to their targets but the relative orientations of the parts are poor resulting in a misleadingly high RMSD. Despite the sometimes large RMSD, no true ambiguity was found in the structure set, i.e. no alternative structures of the same EC.


Stochastic reconstruction of protein structures from effective connectivity profiles.

Wolff K, Vendruscolo M, Porto M - PMC Biophys (2008)

Reconstruction of 1k1v. Comparison between the target (red) and reconstructed (blue) structures of the DNA-binding domain of MafG (PDB code 1k1v). The RMSD between the two structures is 2.4Å.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2666633&req=5

Figure 6: Reconstruction of 1k1v. Comparison between the target (red) and reconstructed (blue) structures of the DNA-binding domain of MafG (PDB code 1k1v). The RMSD between the two structures is 2.4Å.
Mentions: The root mean square deviation (RMSD) of Cα atoms ranges between 2.2Å and 9.6Å over the entire chain. We explain these variations by the following considerations. The omission of unstructured tails at the ends improves the overall RMSD to between 1.5Å and 9.1Å. More importantly, there were many non-compact structures in the structure set. Compactness appears to be the crucial condition for low RMSDs, even more than a high content of secondary structure. Thus, compact structures (Fig. 6) were reconstructed to low RMSDs while less compact ones (Fig. 7) were considerably deformed even though qfull > 90%. This behaviour can often be observed for single α-helices or two-bundles as the helices that are at most weakly inter-connected can be deformed considerably without breaking the contact pattern (see Fig. 7). Here, the resolution is almost as good as a Gō-model of the full, unrestricted structure would achieve. In the case of α-helices the problem could likely be eased by an improved version of the helix energy Ehelix, for example by enforcing straight helix axes, but non-compact structures were generally problematic. Other structures can nicely be aligned piecewise to their targets but the relative orientations of the parts are poor resulting in a misleadingly high RMSD. Despite the sometimes large RMSD, no true ambiguity was found in the structure set, i.e. no alternative structures of the same EC.

Bottom Line: Structure information thus enters the folding dynamics via the effective connectivity, but the interaction is not restricted to pairs of amino acids that form native contacts, resulting in a free energy landscape which does not rely on the assumption of minimal frustration.Moreover, effective connectivity vectors can be predicted more readily from the amino acid sequence of proteins than the corresponding contact maps, thus suggesting that the stochastic protocol presented here could be effectively combined with other current methods for predicting native structures.PACS codes: 87.14.Ee.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut für Festkörperphysik, Technische Universität Darmstadt, Hochschulstrasse 6, 64289 Darmstadt, Germany. porto@fkp.tu-darmstadt.de.

ABSTRACT
We discuss a stochastic approach for reconstructing the native structures of proteins from the knowledge of the "effective connectivity", which is a one-dimensional structural profile constructed as a linear combination of the eigenvectors of the contact map of the target structure. The structural profile is used to bias a search of the conformational space towards the target structure in a Monte Carlo scheme operating on a Calpha-chain of uniform, finite thickness. Structure information thus enters the folding dynamics via the effective connectivity, but the interaction is not restricted to pairs of amino acids that form native contacts, resulting in a free energy landscape which does not rely on the assumption of minimal frustration. Moreover, effective connectivity vectors can be predicted more readily from the amino acid sequence of proteins than the corresponding contact maps, thus suggesting that the stochastic protocol presented here could be effectively combined with other current methods for predicting native structures. PACS codes: 87.14.Ee.

No MeSH data available.