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BMC Bioinformatics. 2017 Nov 13;18(1):475. doi: 10.1186/s12859-017-1904-5.

A fragment based method for modeling of protein segments into cryo-EM density maps.

Author information

1
Institute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany.
2
RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, CA, 92093-0743, USA.
3
Institute of Medical Physics and Biophysics, University Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
4
Institute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany. peter.hildebrand@charite.de.
5
Institute of Medical Physics and Biophysics, University Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany. peter.hildebrand@charite.de.

Abstract

BACKGROUND:

Single-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability and the circle of users. To facilitate modeling of macromolecules into cryo-EM density maps, fast and easy to use methods for modeling are now demanded.

RESULTS:

Here we investigated and benchmarked the suitability of a classical and well established fragment-based approach for modeling of segments into cryo-EM density maps (termed FragFit). FragFit uses a hierarchical strategy to select fragments from a pre-calculated set of billions of fragments derived from structures deposited in the Protein Data Bank, based on sequence similarly, fit of stem atoms and fit to a cryo-EM density map. The user only has to specify the sequence of the segment and the number of the N- and C-terminal stem-residues in the protein. Using a representative data set of protein structures, we show that protein segments can be accurately modeled into cryo-EM density maps of different resolution by FragFit. Prediction quality depends on segment length, the type of secondary structure of the segment and local quality of the map.

CONCLUSION:

Fast and automated calculation of FragFit renders it applicable for implementation of interactive web-applications e.g. to model missing segments, flexible protein parts or hinge-regions into cryo-EM density maps.

KEYWORDS:

Cryo-EM; Flexible fitting; Fragment based modeling

PMID:
29132296
PMCID:
PMC5683378
DOI:
10.1186/s12859-017-1904-5
[Indexed for MEDLINE]
Free PMC Article

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