Learning Rules for Protein Exposure (KUL)
| Application domain: |
Protein Engineering |
| Further specification: |
Data set |
| Pointers: |
Contact Hendrik Blockeel Hendrik.Blockeel@cs.kuleuven.ac.be |
| Data complexity: |
860 KB |
| Data format: |
interpretations (ICL/Claudien) |
Annotation:
This application is very similar to the well-known protein
secondary structure prediction problem. A protein is a long sequence of
amino acids, that is somehow folded in 3-D space. Some parts of the sequence
will be located at the surface of the 3-D shape formed by the protein (these
are exposed), others are located at the inside (buried). The aim of the
learning process is to predict which parts of a protein are exposed.
The background information is the same as the one available
for the secondary structure prediction problem. The data themselves are
the same as used in previous experiments by Holbrook et al. (1990).
Reference
-
S.R. Holbrook, S.M. Muskal and S.H. Kim. Predicting surface
exposure of amino acids from protein sequence. Protein Engineering,
3, 289-294. 1990.
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