Structure of the native myosin filament in the relaxed cardiac sarcomere.

The thick filament is a key component of sarcomeres, the basic units of striated muscle1. Alterations in thick filament proteins are associated with familial hypertrophic cardiomyopathy and other heart and muscle diseases2. Despite the central importance of the thick filament, its molecular organization remains unclear. Here we present the molecular architecture of native cardiac sarcomeres in the relaxed state, determined by cryo-electron tomography. Our reconstruction of the thick filament reveals the three-dimensional organization of myosin, titin and myosin-binding protein C (MyBP-C). The arrangement of myosin molecules is dependent on their position along the filament, suggesting specialized capacities in terms of strain susceptibility and force generation. Three pairs of titin-α and titin-ß chains run axially along the filament, intertwining with myosin tails and probably orchestrating the length-dependent activation of the sarcomere. Notably, whereas the three titin-α chains run along the entire length of the thick filament, titin-ß chains do not. The structure also demonstrates that MyBP-C bridges thin and thick filaments, with its carboxy-terminal region binding to the myosin tails and directly stabilizing the OFF state of the myosin heads in an unforeseen manner. These results provide a foundation for future research investigating muscle disorders involving sarcomeric components.

TomoTwin: generalized 3D localization of macromolecules in cryo-electron tomograms with structural data mining.

Cryogenic-electron tomography enables the visualization of cellular environments in extreme detail, however, tools to analyze the full amount of information contained within these densely packed volumes are still needed. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: an open source general picking model for cryogenic-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network to locate new proteins.

Accelerated 2D Classification With ISAC Using GPUs.

A widely used approach to analyze single particles in electron microscopy data is 2D classification. This process is very computationally expensive, especially when large data sets are analyzed. In this paper we present GPU ISAC, a newly developed, GPU-accelerated version of the established Iterative Stable Alignment and Clustering (ISAC) algorithm for 2D images and generating class averages. While the previously existing implementation of ISAC relied on a computer cluster, GPU ISAC enables users to produce high quality 2D class averages from large-scale data sets on a single desktop machine equipped with affordable, consumer-grade GPUs such as Nvidia GeForce GTX 1080 TI cards. With only two such cards GPU ISAC matches the performance of twelve high end cluster nodes and, using high performance GPUs, is able to produce class averages from a million particles in between six to thirteen hours, depending on data set quality and box size. We also show GPU ISAC to scale linearly in all input dimensions, and thereby capable of scaling well with the increasing data load demand of future data sets. Further user experience improvements integrate GPU ISAC seamlessly into the existing SPHIRE GUI, as well as the TranSPHIRE on-the-fly processing pipeline. It is open source and can be downloaded at https://gitlab.gwdg.de/mpi-dortmund/sphire/cuISAC/.

TranSPHIRE: automated and feedback-optimized on-the-fly processing for cryo-EM.

Single particle cryo-EM requires full automation to allow high-throughput structure determination. Although software packages exist where parts of the cryo-EM pipeline are automated, a complete solution that offers reliable on-the-fly processing, resulting in high-resolution structures, does not exist. Here we present TranSPHIRE: A software package for fully-automated processing of cryo-EM datasets during data acquisition. TranSPHIRE transfers data from the microscope, automatically applies the common pre-processing steps, picks particles, performs 2D clustering, and 3D refinement parallel to image recording. Importantly, TranSPHIRE introduces a machine learning-based feedback loop to re-train its picking model to adapt to any given data set live during processing. This elegant approach enables TranSPHIRE to process data more effectively, producing high-quality particle stacks. TranSPHIRE collects and displays all metrics and microscope settings to allow users to quickly evaluate data during acquisition. TranSPHIRE can run on a single work station and also includes the automated processing of filaments.

Structural basis of TRPC4 regulation by calmodulin and pharmacological agents.

Canonical transient receptor potential channels (TRPC) are involved in receptor-operated and/or store-operated Ca2+ signaling. Inhibition of TRPCs by small molecules was shown to be promising in treating renal diseases. In cells, the channels are regulated by calmodulin (CaM). Molecular details of both CaM and drug binding have remained elusive so far. Here, we report structures of TRPC4 in complex with three pyridazinone-based inhibitors and CaM. The structures reveal that all the inhibitors bind to the same cavity of the voltage-sensing-like domain and allow us to describe how structural changes from the ligand-binding site can be transmitted to the central ion-conducting pore of TRPC4. CaM binds to the rib helix of TRPC4, which results in the ordering of a previously disordered region, fixing the channel in its closed conformation. This represents a novel CaM-induced regulatory mechanism of canonical TRP channels.

Two particle-picking procedures for filamentous proteins: SPHIRE-crYOLO filament mode and SPHIRE-STRIPER.

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.

SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM.

Selecting particles from digital micrographs is an essential step in single-particle electron cryomicroscopy (cryo-EM). As manual selection of complete datasets-typically comprising thousands of particles-is a tedious and time-consuming process, numerous automatic particle pickers have been developed. However, non-ideal datasets pose a challenge to particle picking. Here we present the particle picking software crYOLO which is based on the deep-learning object detection system You Only Look Once (YOLO). After training the network with 200-2500 particles per dataset it automatically recognizes particles with high recall and precision while reaching a speed of up to five micrographs per second. Further, we present a general crYOLO network able to pick from previously unseen datasets, allowing for completely automated on-the-fly cryo-EM data preprocessing during data acquisition. crYOLO is available as a standalone program under http://sphire.mpg.de/ and is distributed as part of the image processing workflow in SPHIRE.

Cryo-EM reveals the asymmetric assembly of squid hemocyanin.

The oxygen transporter of molluscs, hemocyanin, consists of long pearl-necklace-like subunits of several globular domains. The subunits assemble in a complex manner to form cylindrical decamers. Typically, the first six domains of each subunit assemble together to form the cylinder wall, while the C-terminal domains form a collar that fills or caps the cylinder. During evolution, various molluscs have been able to fine-tune their oxygen binding by deleting or adding C-terminal domains and adjusting their inner-collar architecture. However, squids have duplicated one of the wall domains of their subunits instead. Here, using cryo-EM and an optimized refinement protocol implemented in SPHIRE, this work tackled the symmetry-mismatched structure of squid hemocyanin, revealing the precise effect of this duplication on its quaternary structure and providing a potential model for its structural evolution.

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE.

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 Å structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.