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Detalle de: FEMME database: Topologic and geometric information of macromolecules

Title FEMME database: Topologic and geometric information of macromolecules
Abstract

FEMME (Feature Extraction in a Multi-resolution Macromolecular Environment: http://www.biocomp.cnb.uam.es/FEMME/) database version 1.0 is a new bioinformatics data resource that collects topologic and geometric information obtained from macromolecular structures solved by three-dimensional electron microscopy (3D-EM). Although the FEMME database is focused on medium resolution data, the methodology employed (based on the so-called alpha-shape theory) is applicable to atomic resolution data as well. The alpha-shape representation allows the automatic extraction of structural features from 3D-EM volumes and their subsequent characterisation. FEMME is being populated with 3D-EM data stored in the electron microscopy database EMD-DB (http://www.ebi.ac.uk/msd/). However, and since the number of entries in EMD-DB is still relatively small, FEMME is also being populated in this initial phase with structural data from PDB and PQS databases (http://www.rcsb.org/pdb/ and pqs.ebi.ac.uk/, respectively) whose resolution has been lowered accordingly. Each FEMME entry contains macromolecular geometry and topology information with a detailed description of its structural features. Moreover, FEMME data have facilitated the study and development of a method to retrieve macromolecular structures by their structural content based on the combined use of spin images and neural networks with encouraging results. Therefore, the FEMME database constitutes a powerful tool that provides a uniform and automatic way of analysing volumes coming from 3D-EM that will hopefully help the scientific community to perform wide structural comparisons.

Apa Reference Jiménez-Lozano, N., Chagoyen, M., Cuenca-Alba, J., & Carazo, J. M. (2003). FEMME database: topologic and geometric information of macromolecules. Journal of Structural Biology, 144(1–2), 104–113. 10.1016/j.jsb.2003.09.014
Doi 10.1016/j.jsb.2003.09.014
Publication Date Oct 2003
Publication Type Article
Researcher Mónica Chagoyen
Ontology Artificial Intelligence, Experimental structure determination, Biological Databases, Deep Learning in Biology
Research Groups
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