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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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Selecting the optimal model: The Akaike information criterion (AIC) is used to determine the optimal number of hidden node values. The AIC score (y axis) points towards a model with 30 hidden node values (x-axis). The optimal model is shown as a filled circle.
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Figure 7: Selecting the optimal model: The Akaike information criterion (AIC) is used to determine the optimal number of hidden node values. The AIC score (y axis) points towards a model with 30 hidden node values (x-axis). The optimal model is shown as a filled circle.

Mentions: where L(Θ/) is the likelihood of the trained model Θ given the observations , and k is the number of free parameters. The AIC is a measure of the estimated KL divergence between the trained model and a fictitious truth model which generated the training data [56]. In our case, the AIC points towards a model with 30 hidden node states (see Figure 7). The number of relevant, none-zero parameters for the selected model is 9871.


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)

Selecting the optimal model: The Akaike information criterion (AIC) is used to determine the optimal number of hidden node values. The AIC score (y axis) points towards a model with 30 hidden node values (x-axis). The optimal model is shown as a filled circle.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Selecting the optimal model: The Akaike information criterion (AIC) is used to determine the optimal number of hidden node values. The AIC score (y axis) points towards a model with 30 hidden node values (x-axis). The optimal model is shown as a filled circle.
Mentions: where L(Θ/) is the likelihood of the trained model Θ given the observations , and k is the number of free parameters. The AIC is a measure of the estimated KL divergence between the trained model and a fictitious truth model which generated the training data [56]. In our case, the AIC points towards a model with 30 hidden node states (see Figure 7). The number of relevant, none-zero parameters for the selected model is 9871.

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