Beyond rotamers: a generative, probabilistic model of side chains in proteins
Research output: Contribution to journal › Journal article › peer-review
Documents
- Beyond rotamers: a generative...
Submitted manuscript, 1.39 MB, PDF document
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.
Original language | English |
---|---|
Journal | B M C Bioinformatics |
Volume | 11 |
Pages (from-to) | 306 |
ISSN | 1471-2105 |
DOIs | |
Publication status | Published - 1 Jan 2010 |
- Models, Molecular, Models, Statistical, Protein Conformation, Proteins
Research areas
Number of downloads are based on statistics from Google Scholar and www.ku.dk
No data available
ID: 33977139