Abstract
The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model-a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein-protein or protein-DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org.
Original language | English |
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Pages (from-to) | W265-269 |
Journal | Nucleic Acids Research |
Volume | 36 |
Issue number | Web Server issue |
DOIs | |
State | Published - Jul 1 2008 |
Externally published | Yes |
Funding
This work was supported by the NIH-NLM (5T15LM 007359 to SJD) and U.S. Department of Energy (DE-FG02-04ER25627 to JCM).
Funders | Funder number |
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NIH-NLM | 5T15LM 007359 |
U.S. Department of Energy | DE-FG02-04ER25627 |
U.S. National Library of Medicine | T15LM007359 |