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Incredibly fun to work with Alireza Ghanbarpour and Bob Sauer on this project - read more here: biorxiv.org/content/10.110…. Also, exciting things brewing in the Ghanbarpour lab at WashU, be sure to check out Ali's new group (sites.wustl.edu/ghanbarpour/)!!

Congratulations Dr. BERTINA TELUSMA #MSRPBio 2014 & ⁦HHMI⁩ EXROP alumus for a FANTASTIC PhD thesis ⁦MIT Biology⁩ ⁦MITSchool ofScience⁩ from everyone ⁦Joey Davis @[email protected]⁩ and MSRP Bio!!! You have come such a long way from RISE student ⁦Barry University⁩

Happy to share our little discovery with Simone Mattei team on how ribosomes hibernate upside down on mitochondria during cellular stress is now out Nature Communications! A collaboration between @medicineUVA, @EMBL, EMBL Imaging Centre, MEMC. Congrats to all authors! nature.com/articles/s4146…

I’m excited to share our latest preprint, where we showcase the development of two computational tools to facilitate the analysis of highly heterogeneous cryo-EM datasets. (1/7) biorxiv.org/content/10.110…

With the advent of modern heterogeneous reconstruction tools and particularly deep learning -based approaches, we now have the ability to generate many density maps from a single cryo-EM dataset. But what next? How do you actually learn biology from these ensembles? (2/7)

In previous work, we’ve shown the power of atomic model -based approaches to survey and quantify heterogeneity across an ensemble, highlighting how these approaches can be used to measure changes in subunit occupancy as a function of experimental condition. (3/n)

But these approaches are limited in their power/utility: they’re poorly suited to characterizing conformational heterogeneity, and they require prior knowledge about where heterogeneity is likely to be found. (4/n)

To address these challenges, we hypothesized that we should be able to infer regions of variability (or ‘structural blocks’) in a volume directly from the ensemble, by leveraging information about how voxels are co-occupied across the ensemble. (5/n)

The result is SIREn (Subunit Inference from Real-space Ensembles), which takes as input a large volume ensemble (typically 500-1000 maps), and returns a series of .mrc files that can be viewed in ChimeraX (bsky: chimerax.ucsf.edu), each representing a different inferred structural block. (6/n)

ChimeraX (bsky: chimerax.ucsf.edu) We also describe several pipelines for leveraging the inferred blocks to identify homogeneous subsets of particles for high-resolution refinement of states of interest, and demonstrate that SIREn works not only for SPA datasets, but also for in situ cryo- ET data. (7/n)

ChimeraX (bsky: chimerax.ucsf.edu) Lastly, in work led by my coauthor Maria V. Carreira, we trained a 3D convolutional neural network on thousands of maps from the EMDB - EMPIAR @EBI to predict a binarization threshold (equivalently, a contour or isosurface level) for any input 3D density map. (8/n)

ChimeraX (bsky: chimerax.ucsf.edu) Maria V. Carreira EMDB - EMPIAR @EBI We anticipate this tool will have broad utility – there is a need for a more unbiased and higher-throughput approach to determining contour levels. To make this tool accessible, Maria also created a script that lets you predict thresholds directly within ChimeraX (bsky: chimerax.ucsf.edu)! (9/n)

ChimeraX (bsky: chimerax.ucsf.edu) Maria V. Carreira EMDB - EMPIAR @EBI Grateful to my coauthors Maria V. Carreira Barrett Powell Joey Davis @[email protected] for all their hard work, and if you want to try out these tools yourself, you can access them at github.com/lkinman/siren and github.com/mariacarreira/… Happy processing, all! (n/n)

hey #teamtomo if you are looking to add to your toolkit for molecular identification inside cells, our preprint is now out!

I am looking for a highly talented postdoc to join my lab to study ATP-dependent AAA machines using structural biology, biochemistry, and mass spectrometry. Please apply through the following link: bit.ly/4haSZID For more information, visit: sites.wustl.edu/ghanbarpour/