TY - JOUR

T1 - Beyond rotamers

T2 - a generative, probabilistic model of side chains in proteins

AU - Harder, Tim

AU - Boomsma, Wouter Krogh

AU - Paluszewski, Martin

AU - Frellsen, Jes

AU - Johansson, Kristoffer Enøe

AU - Hamelryck, Thomas Wim

PY - 2010/1/1

Y1 - 2010/1/1

N2 - 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.

AB - 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.

KW - Models, Molecular

KW - Models, Statistical

KW - Protein Conformation

KW - Proteins

U2 - 10.1186/1471-2105-11-306

DO - 10.1186/1471-2105-11-306

M3 - Journal article

C2 - 20525384

VL - 11

SP - 306

JO - B M C Bioinformatics

JF - B M C Bioinformatics

SN - 1471-2105

ER -