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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.

Show MeSH
Dihedral angles in glutamate: Dihedral angles are the main degrees of freedom for the backbone (ϕ and ψ angles) and the side chain (χ angles) of an amino acid. The number of χ angles varies between zero and four for the 20 standard amino acids. The figure shows a ball-and-stick representation of glutamate, which has three χ angles. The fading conformations in the background illustrate a rotation around χ1. The figure was made using PyMOL http://www.pymol.org.
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Figure 1: Dihedral angles in glutamate: Dihedral angles are the main degrees of freedom for the backbone (ϕ and ψ angles) and the side chain (χ angles) of an amino acid. The number of χ angles varies between zero and four for the 20 standard amino acids. The figure shows a ball-and-stick representation of glutamate, which has three χ angles. The fading conformations in the background illustrate a rotation around χ1. The figure was made using PyMOL http://www.pymol.org.

Mentions: For our purposes, bond angles and bond lengths in amino acid side chains can be considered as fixed to their ideal values, as they show only very small variations [25]. This leaves the rotations around the bonds - the χ dihedral angles - as the main degrees of freedom. Accordingly, the sequence of χ angles is a good parameterization of the conformation of a given side chain. The same parameterization of the conformational space is also used in most popular rotamer libraries [6,7]. The number of angles necessary to describe a side chain conformation varies between zero and four for the 20 different standard amino acid types. Figure 1 illustrates the dihedral angles for glutamate.


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)

Dihedral angles in glutamate: Dihedral angles are the main degrees of freedom for the backbone (ϕ and ψ angles) and the side chain (χ angles) of an amino acid. The number of χ angles varies between zero and four for the 20 standard amino acids. The figure shows a ball-and-stick representation of glutamate, which has three χ angles. The fading conformations in the background illustrate a rotation around χ1. The figure was made using PyMOL http://www.pymol.org.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Dihedral angles in glutamate: Dihedral angles are the main degrees of freedom for the backbone (ϕ and ψ angles) and the side chain (χ angles) of an amino acid. The number of χ angles varies between zero and four for the 20 standard amino acids. The figure shows a ball-and-stick representation of glutamate, which has three χ angles. The fading conformations in the background illustrate a rotation around χ1. The figure was made using PyMOL http://www.pymol.org.
Mentions: For our purposes, bond angles and bond lengths in amino acid side chains can be considered as fixed to their ideal values, as they show only very small variations [25]. This leaves the rotations around the bonds - the χ dihedral angles - as the main degrees of freedom. Accordingly, the sequence of χ angles is a good parameterization of the conformation of a given side chain. The same parameterization of the conformational space is also used in most popular rotamer libraries [6,7]. The number of angles necessary to describe a side chain conformation varies between zero and four for the 20 different standard amino acid types. Figure 1 illustrates the dihedral angles for glutamate.

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