Prediction of B-strand packing interactions using the signature product.
- Los Alamos National Laboratory, Biosciences Division, Los Alamos, NM
The prediction of {beta}-sheet topology requires the consideration of long-range interactions between {beta}-strands that are not necessarily consecutive in sequence. Since these interactions are difficult to simulate using ab initio methods, we propose a supplementary method able to assign {beta}-sheet topology using only sequence information. We envision using the results of our method to reduce the three-dimensional search space of ab initio methods. Our method is based on the signature molecular descriptor, which has been used previously to predict protein-protein interactions successfully, and to develop quantitative structure-activity relationships for small organic drugs and peptide inhibitors. Here, we show how the signature descriptor can be used in a Support Vector Machine to predict whether or not two {beta}-strands will pack adjacently within a protein. We then show how these predictions can be used to order {beta}-strands within {beta}-sheets. Using the entire PDB database with ten-fold cross-validation, we have achieved 74.0% accuracy in packing prediction and 75.6% accuracy in the prediction of edge strands. For the case of {beta}-strand ordering, we are able to predict the correct ordering accurately for 51.3% of the {beta}-sheets. Furthermore, using a simple confidence metric, we can determine those sheets for which accurate predictions can be obtained. For the top 25% highest confidence predictions, we are able to achieve 95.7% accuracy in {beta}-strand ordering.
- Research Organization:
- Sandia National Laboratories
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 948216
- Report Number(s):
- SAND2005-1786C
- Country of Publication:
- United States
- Language:
- English
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