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

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 confo...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Tim Harder, Wouter Boomsma, Martin Paluszewski, Jes Frellsen, Kristoffer E. Johansson, Thomas Hamelryck
• Keywords: BMCBI 2010, Chains, Conformational Space, Continuous Space