The Structure–Function Linkage Database
| Autor: | Shoshana D. Brown, John H. Morris, Patricia C. Babbitt, Alexandra M. Schnoes, Ashley F. Custer, Gemma L. Holliday, Susan T. Mashiyama, Jeffrey M. Yunes, Alan E. Barber, Florian Lauck, Michael A. Hicks, Doug Stryke, Thomas E. Ferrin, Elaine C. Meng, Eyal Akiva, Sunil Ojha, Conrad C. Huang, David Mischel, Daniel Almonacid |
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| Přispěvatelé: | Massachusetts Institute of Technology. Department of Chemical Engineering, Hicks, Michael A. |
| Rok vydání: | 2013 |
| Předmět: |
Sequence alignment
Context (language use) Linkage (mechanical) Biology computer.software_genre law.invention Databases Structure-Activity Relationship Similarity (network science) law Information and Computing Sciences Genetics Databases Protein III. Metabolic and signalling pathways enzymes Sequence (medicine) Internet Database Protein Structure function Molecular Sequence Annotation SUPERFAMILY Biological Sciences Enzymes Generic health relevance Sequence Alignment computer Environmental Sciences Developmental Biology |
| Zdroj: | Nucleic Acids Research Akiva, E; Brown, S; Almonacid, DE; Barber, AE; Custer, AF; Hicks, MA; et al.(2014). The Structure-Function Linkage Database. Nucleic Acids Research, 42(D1), D521-D530. doi: 10.1093/nar/gkt1130. UC San Francisco: Retrieved from: http://www.escholarship.org/uc/item/3bf997bt Nucleic acids research, vol 42, iss Database issue Oxford University Press |
| ISSN: | 1362-4962 0305-1048 |
| DOI: | 10.1093/nar/gkt1130 |
| Popis: | The Structure-Function Linkage Database (SFLD, http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure-function relationships for functionally diverse enzyme superfamilies. Members of such superfamilies are diverse in their overall reactions yet share a common ancestor and some conserved active site features associated with conserved functional attributes such as a partial reaction. Thus, despite their different functions, members of these superfamilies 'look alike', making them easy to misannotate. To address this complexity and enable rational transfer of functional features to unknowns only for those members for which we have sufficient functional information, we subdivide superfamily members into subgroups using sequence information, and lastly into families, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Browsing and searching options in the SFLD provide access to all of these levels. The SFLD offers manually curated as well as automatically classified superfamily sets, both accompanied by search and download options for all hierarchical levels. Additional information includes multiple sequence alignments, tab-separated files of functional and other attributes, and sequence similarity networks. The latter provide a new and intuitively powerful way to visualize functional trends mapped to the context of sequence similarity. © 2013 The Author(s). Published by Oxford University Press. |
| Databáze: | OpenAIRE |
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