Abstract
BackgroundAccurately 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.ResultsIn 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.ConclusionsA 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.
Highlights
Covering the conformational space of amino acid side chains is essential for important applications such as protein design, docking and high resolution structure prediction
We present BASILISK, a dynamic Bayesian network [22] that formulates a probabilistic model of the conformational space of amino acid side chains
We previously developed probabilistic models of RNA and protein structure based on the combination of dynamic Bayesian networks (DBNs) and directional statistics [30,32,33]
Summary
Covering the conformational space of amino acid side chains is essential for important applications such as protein design, docking and high resolution structure prediction. 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. It quickly became apparent that the c (chi) dihedral angles - the main degrees of freedom in side chains - are not distributed freely, but tend to cluster around certain positions [1,2,3]. The most common way to describe the conformational space of side chains is through so-called rotamer libraries: collections of representative side chain conformations, called rotamers [5]. These libraries are usually compiled from experimentally determined, high resolution protein structures by clustering the side chain
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