Two particle‐picking procedures for filamentous proteins: SPHIRE‐crYOLO filament mode and SPHIRE‐STRIPER. Issue 7 (6th July 2020)

• Record Type: Journal Article
• Authors: Wagner, Thorsten; Lusnig, Luca; Pospich, Sabrina; Stabrin, Markus; Schönfeld, Fabian; Raunser, Stefan
• Is Part Of: Acta crystallographica. Volume 76:Issue 7(2020)
• Page Start: 613
• Page End: 620
• Publication Date: 2020-07-06
• Subjects: SPHIRE‐crYOLO -- SPHIRE‐STRIPER -- cryo‐EM -- particle picking -- filaments -- deep learning; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; Molecular biology -- Periodicals; Molecular structure -- Periodicals; Biomolecules -- Structure -- Periodicals; Cytology -- Periodicals; Biomolecules -- Structure; Crystallography; Cytology; Molecular biology; Molecular structure; X-ray crystallography; Periodicals; 548
• DOI: 10.1107/S2059798320007342
• Languages: English
• ISSNs: 2059-7983

Abstract

Abstract : Two approaches for the selection of filaments from cryo‐EM micrographs are described. Abstract : Structure determination of filamentous molecular complexes involves the selection of filaments from cryo‐EM micrographs. The automatic selection of helical specimens is particularly difficult, and thus many challenging samples with issues such as contamination or aggregation are still manually picked. Here, two approaches for selecting filamentous complexes are presented: one uses a trained deep neural network to identify the filaments and is integrated in SPHIRE‐crYOLO, while the other, called SPHIRE‐STRIPER, is based on a classical line‐detection approach. The advantage of the crYOLO ‐based procedure is that it performs accurately on very challenging data sets and selects filaments with high accuracy. Although STRIPER is less precise, the user benefits from less intervention, since in contrast to crYOLO, STRIPER does not require training. The performance of both procedures on Tobacco mosaic virus and filamentous F‐actin data sets is described to demonstrate the robustness of each method.