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Cryo-EM reconstruction of the chlororibosome to 3.2 angstrom resolution within 24 h
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
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Number of Authors: 6
2017 (English)In: IUCrJ, ISSN 0972-6918, E-ISSN 2052-2525, Vol. 4, p. 723-727Article in journal (Refereed) Published
Abstract [en]

The introduction of direct detectors and the automation of data collection in cryo-EM have led to a surge in data, creating new opportunities for advancing computational processing. In particular, on-the-fly workflows that connect data collection with three-dimensional reconstruction would be valuable for more efficient use of cryo-EM and its application as a sample-screening tool. Here, accelerated on-the-fly analysis is reported with optimized organization of the data-processing tools, image acquisition and particle alignment that make it possible to reconstruct the three-dimensional density of the 70S chlororibosome to 3.2 angstrom resolution within 24 h of tissue harvesting. It is also shown that it is possible to achieve even faster processing at comparable quality by imposing some limits to data use, as illustrated by a 3.7 angstrom resolution map that was obtained in only 80 min on a desktop computer. These on-the-fly methods can be employed as an assessment of data quality from small samples and extended to high-throughput approaches.

Place, publisher, year, edition, pages
2017. Vol. 4, p. 723-727
Keyword [en]
cryo-EM, image processing, chlororibosome
National Category
Chemical Sciences Materials Engineering
Identifiers
URN: urn:nbn:se:su:diva-149831DOI: 10.1107/S205225251701226XISI: 000414266200004PubMedID: 29123673OAI: oai:DiVA.org:su-149831DiVA, id: diva2:1165371
Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-13Bibliographically approved

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Forsberg, Björn O.Aibara, ShintaroKimanius, DariLindahl, ErikAmunts, Alexey
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Department of Biochemistry and BiophysicsScience for Life Laboratory (SciLifeLab)
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