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The Structure-Function Linkage Database.

Eyal Akiva1, Shoshana Brown, Daniel E Almonacid

  • 1Department 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.

Nucleic Acids Research
|November 26, 2013
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Summary
This summary is machine-generated.

The Structure-Function Linkage Database (SFLD) classifies enzyme superfamilies, aiding in accurate functional annotation. It uses sequence data to subgroup enzymes, preventing misclassification and enabling reliable functional feature transfer.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Enzymology

Background:

  • Enzyme superfamilies share common ancestors and conserved active site features.
  • Functional diversity within superfamilies can lead to misannotation due to similar structures.
  • Accurate functional annotation is crucial for understanding enzyme mechanisms.

Purpose of the Study:

  • To describe the Structure-Function Linkage Database (SFLD) as a resource for enzyme classification.
  • To address the challenge of misannotation in functionally diverse enzyme superfamilies.
  • To enable rational transfer of functional features to uncharacterized enzymes.

Main Methods:

  • Manual curation and automated classification of enzyme superfamilies.
  • Subdivision of superfamilies into subgroups and families based on sequence information.
  • Development of browsing, searching, and data download functionalities within the SFLD.

Main Results:

  • The SFLD provides a hierarchical classification of enzyme superfamilies, subgroups, and families.
  • Sequence similarity networks offer a novel visualization of functional trends.
  • The database includes manually curated and automatically classified sets with associated data.

Conclusions:

  • The SFLD facilitates accurate functional annotation and understanding of enzyme superfamilies.
  • The hierarchical classification and visualization tools aid in distinguishing closely related enzymes.
  • The database serves as a valuable resource for researchers studying enzyme function and evolution.