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KFC Server: interactive forecasting of protein interaction hot spots

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 automa...

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Detalles Bibliográficos
Autores principales: Darnell, Steven J., LeGault, Laura, Mitchell, Julie C.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447760/
https://www.ncbi.nlm.nih.gov/pubmed/18539611
http://dx.doi.org/10.1093/nar/gkn346
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author Darnell, Steven J.
LeGault, Laura
Mitchell, Julie C.
author_facet Darnell, Steven J.
LeGault, Laura
Mitchell, Julie C.
author_sort Darnell, Steven J.
collection PubMed
description 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.
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spelling pubmed-24477602008-07-09 KFC Server: interactive forecasting of protein interaction hot spots Darnell, Steven J. LeGault, Laura Mitchell, Julie C. Nucleic Acids Res Articles 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. Oxford University Press 2008-07-01 2008-06-06 /pmc/articles/PMC2447760/ /pubmed/18539611 http://dx.doi.org/10.1093/nar/gkn346 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Darnell, Steven J.
LeGault, Laura
Mitchell, Julie C.
KFC Server: interactive forecasting of protein interaction hot spots
title KFC Server: interactive forecasting of protein interaction hot spots
title_full KFC Server: interactive forecasting of protein interaction hot spots
title_fullStr KFC Server: interactive forecasting of protein interaction hot spots
title_full_unstemmed KFC Server: interactive forecasting of protein interaction hot spots
title_short KFC Server: interactive forecasting of protein interaction hot spots
title_sort kfc server: interactive forecasting of protein interaction hot spots
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447760/
https://www.ncbi.nlm.nih.gov/pubmed/18539611
http://dx.doi.org/10.1093/nar/gkn346
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