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Beyond rotamers: a generative, probabilistic model of side chains in proteins.

Harder T, Boomsma W, Paluszewski M, Frellsen J, Johansson KE, Hamelryck T - BMC Bioinformatics (2010)

Bottom Line: For example, rigorously combining rotamers with physical force fields is associated with numerous problems.We also illustrate how the model can be used for rigorous, unbiased sampling with a physical force field, and how it improves side chain prediction when used as a pseudo-energy term.In conclusion, BASILISK is an important step forward on the way to a rigorous probabilistic description of protein structure in continuous space and in atomic detail.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark.

ABSTRACT

Background: Accurately covering the conformational space of amino acid side chains is essential for important applications such as protein design, docking and high resolution structure prediction. Today, the most common way to capture this conformational space is through rotamer libraries - discrete collections of side chain conformations derived from experimentally determined protein structures. The discretization can be exploited to efficiently search the conformational space. However, discretizing this naturally continuous space comes at the cost of losing detailed information that is crucial for certain applications. For example, rigorously combining rotamers with physical force fields is associated with numerous problems.

Results: In this work we present BASILISK: a generative, probabilistic model of the conformational space of side chains that makes it possible to sample in continuous space. In addition, sampling can be conditional upon the protein's detailed backbone conformation, again in continuous space - without involving discretization.

Conclusions: A careful analysis of the model and a comparison with various rotamer libraries indicates that the model forms an excellent, fully continuous model of side chain conformational space. We also illustrate how the model can be used for rigorous, unbiased sampling with a physical force field, and how it improves side chain prediction when used as a pseudo-energy term. In conclusion, BASILISK is an important step forward on the way to a rigorous probabilistic description of protein structure in continuous space and in atomic detail.

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Comparison between BASILISK and a standard rotamer library: We calculated the log-likelihood for every rotamer in the Dunbrack backbone independent rotamer library according to the Gaussian model of the library itself (y-axis), and according to BASILISK (x-axis). The Pearson correlation coefficient is 0.91.
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Figure 5: Comparison between BASILISK and a standard rotamer library: We calculated the log-likelihood for every rotamer in the Dunbrack backbone independent rotamer library according to the Gaussian model of the library itself (y-axis), and according to BASILISK (x-axis). The Pearson correlation coefficient is 0.91.

Mentions: As a first test, we determine whether the Dunbrack rotamer library and BASILISK report similar probabilities for the same conformations. We calculated the log-likelihood of the side chain conformations according to the Dunbrack backbone independent rotamer library and according to BASILISK for all side chains in the test set (see Data sets for training and testing). The results show that the two methods indeed correlate very well (Pearson correlation coefficient is 0.88). Figure 5 shows a scatter-plot of the log-likelihood values for all rotamer conformations in the Dunbrack library according to the library itself, and according to BASILISK. Again we find a very good correlation (Pearson correlation coefficient is 0.91). Outliers, especially in the low probability region, are limited to very rare rotamer conformations with little to no observations according to the Dunbrack library.


Beyond rotamers: a generative, probabilistic model of side chains in proteins.

Harder T, Boomsma W, Paluszewski M, Frellsen J, Johansson KE, Hamelryck T - BMC Bioinformatics (2010)

Comparison between BASILISK and a standard rotamer library: We calculated the log-likelihood for every rotamer in the Dunbrack backbone independent rotamer library according to the Gaussian model of the library itself (y-axis), and according to BASILISK (x-axis). The Pearson correlation coefficient is 0.91.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902450&req=5

Figure 5: Comparison between BASILISK and a standard rotamer library: We calculated the log-likelihood for every rotamer in the Dunbrack backbone independent rotamer library according to the Gaussian model of the library itself (y-axis), and according to BASILISK (x-axis). The Pearson correlation coefficient is 0.91.
Mentions: As a first test, we determine whether the Dunbrack rotamer library and BASILISK report similar probabilities for the same conformations. We calculated the log-likelihood of the side chain conformations according to the Dunbrack backbone independent rotamer library and according to BASILISK for all side chains in the test set (see Data sets for training and testing). The results show that the two methods indeed correlate very well (Pearson correlation coefficient is 0.88). Figure 5 shows a scatter-plot of the log-likelihood values for all rotamer conformations in the Dunbrack library according to the library itself, and according to BASILISK. Again we find a very good correlation (Pearson correlation coefficient is 0.91). Outliers, especially in the low probability region, are limited to very rare rotamer conformations with little to no observations according to the Dunbrack library.

Bottom Line: For example, rigorously combining rotamers with physical force fields is associated with numerous problems.We also illustrate how the model can be used for rigorous, unbiased sampling with a physical force field, and how it improves side chain prediction when used as a pseudo-energy term.In conclusion, BASILISK is an important step forward on the way to a rigorous probabilistic description of protein structure in continuous space and in atomic detail.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark.

ABSTRACT

Background: Accurately covering the conformational space of amino acid side chains is essential for important applications such as protein design, docking and high resolution structure prediction. Today, the most common way to capture this conformational space is through rotamer libraries - discrete collections of side chain conformations derived from experimentally determined protein structures. The discretization can be exploited to efficiently search the conformational space. However, discretizing this naturally continuous space comes at the cost of losing detailed information that is crucial for certain applications. For example, rigorously combining rotamers with physical force fields is associated with numerous problems.

Results: In this work we present BASILISK: a generative, probabilistic model of the conformational space of side chains that makes it possible to sample in continuous space. In addition, sampling can be conditional upon the protein's detailed backbone conformation, again in continuous space - without involving discretization.

Conclusions: A careful analysis of the model and a comparison with various rotamer libraries indicates that the model forms an excellent, fully continuous model of side chain conformational space. We also illustrate how the model can be used for rigorous, unbiased sampling with a physical force field, and how it improves side chain prediction when used as a pseudo-energy term. In conclusion, BASILISK is an important step forward on the way to a rigorous probabilistic description of protein structure in continuous space and in atomic detail.

Show MeSH