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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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Univariate histograms for lysine and arginine: The histograms marked "Training" represent the training set. The histograms marked "BASILISK" represent BASILISK samples. For each amino acid, all histograms are plotted on the same scale.
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Figure 3: Univariate histograms for lysine and arginine: The histograms marked "Training" represent the training set. The histograms marked "BASILISK" represent BASILISK samples. For each amino acid, all histograms are plotted on the same scale.

Mentions: For these first tests, we generated over 300,000 samples with the same amino acid composition as the training set. Figure 3 compares the marginal angular distributions of the training set with those of the BASILISK samples for arginine and lysine. We show plots for arginine and lysine because they are the only amino acids with four χ angles; they were most difficult to capture accurately with alternative models (data not shown). A comparison of all remaining relevant amino acids is available as additional material (Additional files 1, 2, 3, Figures S1-S3).


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)

Univariate histograms for lysine and arginine: The histograms marked "Training" represent the training set. The histograms marked "BASILISK" represent BASILISK samples. For each amino acid, all histograms are plotted on the same scale.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Univariate histograms for lysine and arginine: The histograms marked "Training" represent the training set. The histograms marked "BASILISK" represent BASILISK samples. For each amino acid, all histograms are plotted on the same scale.
Mentions: For these first tests, we generated over 300,000 samples with the same amino acid composition as the training set. Figure 3 compares the marginal angular distributions of the training set with those of the BASILISK samples for arginine and lysine. We show plots for arginine and lysine because they are the only amino acids with four χ angles; they were most difficult to capture accurately with alternative models (data not shown). A comparison of all remaining relevant amino acids is available as additional material (Additional files 1, 2, 3, Figures S1-S3).

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