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Title: Rapid chain tracing of polypeptide backbones in electron-density maps

Journal Article · · Acta Crystallographica. Section D: Biological Crystallography
 [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

A method for the rapid tracing of polypeptide backbones has been developed. The method creates an approximate chain tracing that is useful for visual evaluation of whether a structure has been solved and for use in scoring the quality of electron-density maps. The essence of the method is to (i) sample candidate Cα positions at spacings of approximately 0.6 Å along ridgelines of high electron density, (ii) list all possible nonapeptides that satisfy simple geometric and density criteria using these candidate Cα positions, (iii) score the nonapeptides and choose the highest scoring ones, and (iv) find the longest chains that can be made by connecting nonamers. An indexing and storage scheme that allows a single calculation of most distances and density values is used to speed up the process. The method was applied to 42 density-modified electron-density maps at resolutions from 1.5 to 3.8 Å. A total of 21 428 residues in these maps were traced in 24 CPU min with an overall r.m.s.d. of 1.61 Å for Cα atoms compared with the known refined structures. The method appears to be suitable for rapid evaluation of electron-density map quality.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1625667
Journal Information:
Acta Crystallographica. Section D: Biological Crystallography, Vol. 66, Issue 3; ISSN 0907-4449
Publisher:
International Union of CrystallographyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 5 works
Citation information provided by
Web of Science

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Cited By (2)

A fully automatic method yielding initial models from high-resolution cryo-electron microscopy maps journal October 2018
Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps journal March 2020

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