Limits...
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 2i2v, chain 4. Comparison between the target (red) and reconstructed (blue) structures of chain 4 of ribosome (PDB code 2i2v, chain 4). The RMSD between the two structures is 3.0Å.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2666633&req=5

Figure 8: Reconstruction of 2i2v, chain 4. Comparison between the target (red) and reconstructed (blue) structures of chain 4 of ribosome (PDB code 2i2v, chain 4). The RMSD between the two structures is 3.0Å.

Mentions: Successful reconstruction is not distributed evenly among the four SCOP classes covered by the candidate set (Fig. 4). All-α proteins were most abundant in the set and also easiest to reconstruct (18 out of 29). Out of only four all-β proteins two could be reconstructed while none of the three α + β-proteins and six out of 18 small proteins (SCOP class g) were successful. Of these six proteins two had only a secondary structure, two only β and two both. The secondary structure assignment did not by itself favour α-helical over β-sheet contacts, but it was observed that once a secondary structure element had been formed it remained relatively stable for the rest of the simulation. This situation turned out to be problematic for the formation of cooperative contacts between amino acids distant along the chain, in particular in the case of β-sheet contacts between distant strands, while it was irrelevant for more local patterns like α-helices or β-hairpins. Only for very small structures (see Fig. 8) β-sheets could be recovered. Increasing the length also expectedly made reconstruction more difficult owing to the larger conformation space to be sampled (Fig. 3) – and also slowed down computation due to larger eigensystems to be solved. In total, 26 of 54 protein structures were reconstructed from their respective EC profiles.


Stochastic reconstruction of protein structures from effective connectivity profiles.

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

Reconstruction of 2i2v, chain 4. Comparison between the target (red) and reconstructed (blue) structures of chain 4 of ribosome (PDB code 2i2v, chain 4). The RMSD between the two structures is 3.0Å.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Reconstruction of 2i2v, chain 4. Comparison between the target (red) and reconstructed (blue) structures of chain 4 of ribosome (PDB code 2i2v, chain 4). The RMSD between the two structures is 3.0Å.
Mentions: Successful reconstruction is not distributed evenly among the four SCOP classes covered by the candidate set (Fig. 4). All-α proteins were most abundant in the set and also easiest to reconstruct (18 out of 29). Out of only four all-β proteins two could be reconstructed while none of the three α + β-proteins and six out of 18 small proteins (SCOP class g) were successful. Of these six proteins two had only a secondary structure, two only β and two both. The secondary structure assignment did not by itself favour α-helical over β-sheet contacts, but it was observed that once a secondary structure element had been formed it remained relatively stable for the rest of the simulation. This situation turned out to be problematic for the formation of cooperative contacts between amino acids distant along the chain, in particular in the case of β-sheet contacts between distant strands, while it was irrelevant for more local patterns like α-helices or β-hairpins. Only for very small structures (see Fig. 8) β-sheets could be recovered. Increasing the length also expectedly made reconstruction more difficult owing to the larger conformation space to be sampled (Fig. 3) – and also slowed down computation due to larger eigensystems to be solved. In total, 26 of 54 protein structures were reconstructed from their respective EC profiles.

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.