SPI-EM: Towards a Tool For Predicting CATH Superfamilies in 3D-EM Maps

In this paper the theoretical framework used to build a superfamily probability in electron microscopy (SPI-EM) is presented. SPI-EM is a new tool for determining the homologous superfamily to which a protein domain belongs looking at its three-dimensional electron microscopy map. The homologous sup...

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Bibliographic Details
Published in:Journal of molecular biology 2005-01, Vol.345 (4), p.759-771
Main Authors: Velázquez-Muriel, Javier A., Sorzano, Carlos O.S., Scheres, Sjors H.W., Carazo, José-María
Format: Article
Language:English
Subjects:
Color
Computer Simulation
cross-correlation
Databases, Protein
fitting
fold recognition
Microscopy, Electron - methods
Models, Molecular
Probability
Protein Structure, Tertiary
Proteins - chemistry
Proteins - classification
Proteins - ultrastructure
Software
structural biology
three-dimensional electron microscopy
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Summary:In this paper the theoretical framework used to build a superfamily probability in electron microscopy (SPI-EM) is presented. SPI-EM is a new tool for determining the homologous superfamily to which a protein domain belongs looking at its three-dimensional electron microscopy map. The homologous superfamily is assigned according to the domain-architecture database CATH. Our method follows a probabilistic approach applied to the results of fitting protein domains into maps of proteins and the computation of local cross-correlation coefficient measures. The method has been tested and its usefulness proven with isolated domains at a resolution of 8 Å and 12 Å. Results obtained with simulated and experimental data at 10 Å suggest that it is also feasible to detect the correct superfamily of the domains when dealing with electron microscopy maps containing multi-domain proteins. The inherent difficulties and limitations that multi-domain proteins impose are discussed. Our procedure is complementary to other techniques existing in the field to detect structural elements in electron microscopy maps like α-helices and β-sheets. Based on the proposed methodology, a database of relevant distributions is being built to serve the community.
ISSN:0022-2836
1089-8638
DOI:10.1016/j.jmb.2004.11.005