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An estimate of the numbers and density of low-energy structures (or decoys) in the conformational landscape of proteins.

Vadivel K, Namasivayam G - PLoS ONE (2009)

Bottom Line: We show that the number of native-like structures for a polypeptide is, in general, an exponential function of its sequence length.The density of these structures in conformational space remains more or less constant and all the increase appears to come from an expansion in the volume of the space.These results are consistent with earlier reports that were based on other models and techniques.

View Article: PubMed Central - PubMed

Affiliation: Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Tamilnadu, India.

ABSTRACT

Background: The conformational energy landscape of a protein, as calculated by known potential energy functions, has several minima, and one of these corresponds to its native structure. It is however difficult to comprehensively estimate the actual numbers of low energy structures (or decoys), the relationships between them, and how the numbers scale with the size of the protein.

Methodology: We have developed an algorithm to rapidly and efficiently identify the low energy conformers of oligo peptides by using mutually orthogonal Latin squares to sample the potential energy hyper surface. Using this algorithm, and the ECEPP/3 potential function, we have made an exhaustive enumeration of the low-energy structures of peptides of different lengths, and have extrapolated these results to larger polypeptides.

Conclusions and significance: We show that the number of native-like structures for a polypeptide is, in general, an exponential function of its sequence length. The density of these structures in conformational space remains more or less constant and all the increase appears to come from an expansion in the volume of the space. These results are consistent with earlier reports that were based on other models and techniques.

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Performance of MOLS with explicit water.Stereo diagram of the best identified model (black) with the explicit water molecules for the nonameric sequence TGLGRSAGW, superimposed with its respective crystal structure (white).
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pone-0005148-g009: Performance of MOLS with explicit water.Stereo diagram of the best identified model (black) with the explicit water molecules for the nonameric sequence TGLGRSAGW, superimposed with its respective crystal structure (white).

Mentions: The results indicate that the energy function that we have used does not distinguish easily between the native structure and the decoys. Previous reports [12], [17], [51], [52], including some of our own [42], [53], have indicated that this is true of almost all known potential functions, though functions specifically designed to model a particular class of proteins (or peptides) [27], or functions based on known protein structures [39] tend to perform better than general, physics-based functions such as the one we have used. The inclusion of solvent effects has been reported to improve the identification of the native structure. Most potentials include such effects implicitly, for example in selecting the parameters defining the semi-emipirical force fields. In order to evaluate if the inclusion of explicit solvent molecules in calculating the structures would make a difference, we carried out the calculations with explicit water molecules, using the AMBER force field, for the nonameric sequence TGLGRSAGW. Besides the peptide intramolecular non-bonded terms, the force field also include the interactions between water and the peptide. Of the 10000 structures generated by the MOLS technique, the one shown in Figure 9 had the lowest rmsd of 1.82 Å with the respective native structure. The lowest energy structure showed a large deviation from the native structure (4.61 Å). The number of unique structures identified after inclusion of explicit solvent shows a pattern similar to that of in-vacuum simulations (Figure 10). This suggests that the results reported above for the vacuum simulations do not change on inclusion of explicit solvent.


An estimate of the numbers and density of low-energy structures (or decoys) in the conformational landscape of proteins.

Vadivel K, Namasivayam G - PLoS ONE (2009)

Performance of MOLS with explicit water.Stereo diagram of the best identified model (black) with the explicit water molecules for the nonameric sequence TGLGRSAGW, superimposed with its respective crystal structure (white).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2663821&req=5

pone-0005148-g009: Performance of MOLS with explicit water.Stereo diagram of the best identified model (black) with the explicit water molecules for the nonameric sequence TGLGRSAGW, superimposed with its respective crystal structure (white).
Mentions: The results indicate that the energy function that we have used does not distinguish easily between the native structure and the decoys. Previous reports [12], [17], [51], [52], including some of our own [42], [53], have indicated that this is true of almost all known potential functions, though functions specifically designed to model a particular class of proteins (or peptides) [27], or functions based on known protein structures [39] tend to perform better than general, physics-based functions such as the one we have used. The inclusion of solvent effects has been reported to improve the identification of the native structure. Most potentials include such effects implicitly, for example in selecting the parameters defining the semi-emipirical force fields. In order to evaluate if the inclusion of explicit solvent molecules in calculating the structures would make a difference, we carried out the calculations with explicit water molecules, using the AMBER force field, for the nonameric sequence TGLGRSAGW. Besides the peptide intramolecular non-bonded terms, the force field also include the interactions between water and the peptide. Of the 10000 structures generated by the MOLS technique, the one shown in Figure 9 had the lowest rmsd of 1.82 Å with the respective native structure. The lowest energy structure showed a large deviation from the native structure (4.61 Å). The number of unique structures identified after inclusion of explicit solvent shows a pattern similar to that of in-vacuum simulations (Figure 10). This suggests that the results reported above for the vacuum simulations do not change on inclusion of explicit solvent.

Bottom Line: We show that the number of native-like structures for a polypeptide is, in general, an exponential function of its sequence length.The density of these structures in conformational space remains more or less constant and all the increase appears to come from an expansion in the volume of the space.These results are consistent with earlier reports that were based on other models and techniques.

View Article: PubMed Central - PubMed

Affiliation: Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Tamilnadu, India.

ABSTRACT

Background: The conformational energy landscape of a protein, as calculated by known potential energy functions, has several minima, and one of these corresponds to its native structure. It is however difficult to comprehensively estimate the actual numbers of low energy structures (or decoys), the relationships between them, and how the numbers scale with the size of the protein.

Methodology: We have developed an algorithm to rapidly and efficiently identify the low energy conformers of oligo peptides by using mutually orthogonal Latin squares to sample the potential energy hyper surface. Using this algorithm, and the ECEPP/3 potential function, we have made an exhaustive enumeration of the low-energy structures of peptides of different lengths, and have extrapolated these results to larger polypeptides.

Conclusions and significance: We show that the number of native-like structures for a polypeptide is, in general, an exponential function of its sequence length. The density of these structures in conformational space remains more or less constant and all the increase appears to come from an expansion in the volume of the space. These results are consistent with earlier reports that were based on other models and techniques.

Show MeSH