Convolutional networks for supervised mining of molecular patterns within cellular context

Teresa-Trueba, Irene de; Goetz, Sara K.; Mattausch, Alexander; Stojanovska, Frosina; Zimmerli, Christian E.; Toro-Nahuelpan, Mauricio; Cheng, Dorothy W. C.; Tollervey, Fergus; Pape, Constantin; Beck, Martin; Diz-Muñoz, Alba; Kreshuk, Anna; Mahamid, Julia; Zaugg, Judith B.

doi:10.1038/s41592-022-01746-2

Cited by 61 publications

(135 citation statements)

References 72 publications

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“… 97 https://bioconductor.org/packages/3.14/bioc/html/clusterProfiler.html 3D-Unet DeePiCt de Teresa et al. 98 https://github.com/irenedet/3d-unet/tree/7bc343971bdb818c5de90570b83731c8d77cde04 AlphaFold2 Evans et al. 48 https://github.com/deepmind/alphafold …”

Section: Methodsmentioning

confidence: 99%

“…For visualization of ribosomes and membranes in the tomogram shown in Figure 5 B, in-house developed 3D convolutional neural networks for ribosomes localization and membranes segmentation were pretrained with large datasets and used here for prediction. 98 The prediction output was inspected and cleaned manually with Amira and the final figure was generated with ChimeraX.…”

Section: Methodsmentioning

confidence: 99%

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Molecular mechanisms of stress-induced reactivation in mumps virus condensates

Zhang¹,

Sridharan²,

Zagoriy³

et al. 2023

Cell

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Molecular mechanisms of stress-induced reactivation in mumps virus condensates

Zhang¹,

Sridharan²,

Zagoriy³

et al. 2023

Cell

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“…While the reconstruction of tomograms can be streamlined (Mastronarde & Held, 2017;Balyschew et al, 2023), we did not manage to automate particle picking and had to pick them manually which is a very laborious process. Developments in machine learning and the potential applications for particle picking (de Teresa-Trueba et al, 2023;Rice et al, 2022;Zeng et al, 2023) could streamline particle picking in the future. Our dataset will be on the difficult side for such applications.…”

Section: Discussionmentioning

confidence: 99%

Determining The Structure of the Bacterial Voltage-gated Sodium Channel NaChBac Embedded in Liposomes by Cryo Electron Tomography and Subtomogram Averaging

Chang

Dijkman

Wiessing

et al. 2023

Preprint

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Voltage-gated sodium channels shape action potentials that propagate signals along cells. When the membrane potential reaches a certain threshold, the channels open and allow sodium ions to flow through the membrane depolarizing it, followed by the deactivation of the channels. Opening and closing of the channels is important for cellular signalling and regulates various physiological processes in muscles, heart and brain. Mechanistic insights into the voltage-gated channels are difficult to achieve as the proteins are typically extracted from membranes for structural analysis which results in the loss of the transmembrane potential. Here, we report the structural analysis of a bacterial voltage-gated sodium channel, NaChBac, reconstituted in liposomes under an electrochemical gradient by cryo electron tomography and subtomogram averaging. We show that the small channel, most of the residues of which are embedded in a membrane, can be localized using a genetically fused GFP. GFP can aid the initial alignment to an average resulting in a correct structure, but does not help for the final refinement. At a moderate resolution of 16 A; the structure of NaChBac in an unrestricted membrane bilayer is 10% wider than the structure of a purified protein previously solved in nanodiscs, suggesting the potential movement of the peripheral voltage-sensing domains. Our structural analysis explores the limits of structural analysis of membrane proteins in membranes.

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“…2, Extended Data Fig. 3 and the Supplementary Videos 1,3 were treated with an amplitude spectrum matching filter 77 .…”

Section: Cryo Electron Tomographymentioning

confidence: 99%

The distance between the plasma membrane and the actomyosin cortex acts as a nanogate to control cell surface mechanics

Lembo

Strauss

Cheng

et al. 2023

Preprint

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Animal cell shape changes are controlled by the actomyosin cortex, a peripheral actin network tethered to the plasma membrane by membrane-to-cortex attachment (MCA) proteins. Previous studies have focused on how myosin motors or actin turnover can generate the local deformations required for morphogenesis. However, how the cell controls local actin nucleation remains poorly understood. By combining molecular engineering with biophysical approaches and in situ characterization of cortical actin network architecture, we show that membrane-to-cortex tethering determines the distance between the plasma membrane and the actomyosin cortex at the nanoscale of single actin nucleators. In turn, the size of this gap dictates actin filament production and the mechanical properties of the cell surface. Specifically, it tunes formin activity, controlling actin bundling and cortical tension. Our study defines the membrane-to-cortex distance as a nanogate that cells can open or close by MCA proteins to control the activity of key molecules at the cell surface.

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Convolutional networks for supervised mining of molecular patterns within cellular context

Cited by 61 publications

References 72 publications

Molecular mechanisms of stress-induced reactivation in mumps virus condensates

Molecular mechanisms of stress-induced reactivation in mumps virus condensates

Determining The Structure of the Bacterial Voltage-gated Sodium Channel NaChBac Embedded in Liposomes by Cryo Electron Tomography and Subtomogram Averaging

The distance between the plasma membrane and the actomyosin cortex acts as a nanogate to control cell surface mechanics

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