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Nucleic Acids Res. 2014 Jan;42(Database issue):D521-30. doi: 10.1093/nar/gkt1130. Epub 2013 Nov 23.

The Structure-Function Linkage Database.

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Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA, Universidad Andres Bello, Center for Bioinformatics and Integrative Biology, Facultad de Ciencias Biologicas, Santiago 8370146, Chile, Nodality, Inc., South San Francisco, CA 94080, USA, Department of Electrical and Computer Engineering, College of Engineering, Boston University, Boston, MA 02215, USA, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, San Francisco, CA 94158, USA, Center for Bioinformatics (ZBH), University of Hamburg, Hamburg 20146, Germany, Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717, USA, School of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA, UC Berkeley - UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158 and Berkeley, CA 94720, USA and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.


The Structure-Function Linkage Database (SFLD, 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.

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