Machine learning-based real-time object locator/evaluator for cryo-EM data collection.
Ontology highlight
ABSTRACT:
SUBMITTER: Yonekura K
PROVIDER: S-EPMC8423793 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We here introduce the third major release of the SIMPLE (Single-particle IMage Processing Linux Engine) open-source software package for analysis of cryogenic transmission electron microscopy (cryo-EM) movies of single-particles (Single-Particle Analysis, SPA). Development of SIMPLE 3.0 has been focused on real-time data processing using minimal CPU computing resources to allow easy and cost-efficient scaling of processing as data rates escalate. Our stream SPA tool implements the steps of anisotropic motion correction and CTF estimation, rapid template-based particle identification and 2D clustering with automatic class rejection. SIMPLE 3.0 additionally features an easy-to-use web-based graphical user interface (GUI) that can be run on any device (workstation, laptop, tablet or phone) and supports a remote multi-user environment over the network. The new project-based execution model automatically records the executed workflow and represents it as a flow diagram in the GUI. This facilitates meta-data handling and greatly simplifies usage. Using SIMPLE 3.0, it is possible to automatically obtain a clean SP data set amenable to high-resolution 3D reconstruction directly upon completion of the data acquisition, without the need for extensive image processing post collection. Only minimal standard CPU computing resources are required to keep up with a rate of ∼300 Gatan K3 direct electron detector movies per hour. SIMPLE 3.0 is available for download from simplecryoem.com.
| S-EPMC7695977 | biostudies-literature
Project description:A key goal for nanotechnology is to design synthetic objects that may ultimately achieve functionalities known today only from natural macromolecular complexes. Molecular self-assembly with DNA has shown potential for creating user-defined 3D scaffolds, but the level of attainable positional accuracy has been unclear. Here we report the cryo-EM structure and a full pseudoatomic model of a discrete DNA object that is almost twice the size of a prokaryotic ribosome. The structure provides a variety of stable, previously undescribed DNA topologies for future use in nanotechnology and experimental evidence that discrete 3D DNA scaffolds allow the positioning of user-defined structural motifs with an accuracy that is similar to that observed in natural macromolecules. Thereby, our results indicate an attractive route to fabricate nanoscale devices that achieve complex functionalities by DNA-templated design steered by structural feedback.
| S-EPMC3523823 | biostudies-literature
Project description:Structural biology efforts using cryogenic electron microscopy are frequently stifled by specimens adopting "preferred orientations" on grids, leading to anisotropic map resolution and impeding structure determination. Tilting the specimen stage during data collection is a generalizable solution but has historically led to substantial resolution attenuation. Here, we develop updated data collection and image processing workflows and demonstrate, using multiple specimens, that resolution attenuation is negligible or significantly reduced across tilt angles. Reconstructions with and without the stage tilted as high as 60° are virtually indistinguishable. These strategies allowed the reconstruction to 3 Å resolution of a bacterial RNA polymerase with preferred orientation, containing an unnatural nucleotide for studying novel base pair recognition. Furthermore, we present a quantitative framework that allows cryo-EM practitioners to define an optimal tilt angle during data acquisition. These results reinforce the utility of employing stage tilt for data collection and provide quantitative metrics to obtain isotropic maps.
| S-EPMC10776679 | biostudies-literature
Project description:The wide-ranging adoption of quantum technologies requires practical, high-performance advances in our ability to maintain quantum coherence while facing the challenge of state collapse under measurement. Here we use techniques from control theory and machine learning to predict the future evolution of a qubit's state; we deploy this information to suppress stochastic, semiclassical decoherence, even when access to measurements is limited. First, we implement a time-division multiplexed approach, interleaving measurement periods with periods of unsupervised but stabilised operation during which qubits are available, for example, in quantum information experiments. Second, we employ predictive feedback during sequential but time delayed measurements to reduce the Dick effect as encountered in passive frequency standards. Both experiments demonstrate significant improvements in qubit-phase stability over 'traditional' measurement-based feedback approaches by exploiting time domain correlations in the noise processes. This technique requires no additional hardware and is applicable to all two-level quantum systems where projective measurements are possible.
| S-EPMC5241809 | biostudies-other
Project description:Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.
| S-EPMC10287764 | biostudies-literature
Project description:Proteins are dynamic molecules that can undergo rapid conformational rearrangements in response to stimuli. These structural changes are often critical to protein function, and thus elucidating time-dependent conformational landscapes has been a long-standing goal of structural biology. To harness the power of single particle cryo-EM methods to enable 'time-resolved' structure determination, we have developed a light-coupled cryo-plunger that pairs flash-photolysis of caged ligands with rapid sample vitrification. The 'flash-plunger' consists of a high-power ultraviolet LED coupled with focusing optics and a motorized linear actuator, enabling the user to immobilize protein targets in vitreous ice within a programmable time window - as short as tens of milliseconds - after stimulus delivery. The flash-plunger is a simple, inexpensive and flexible tool to explore short-lived conformational states previously unobtainable by conventional sample preparation methods.
| S-EPMC7959588 | biostudies-literature
Project description:We present an approach for preparing cryo-electron microscopy (cryo-EM) grids to study short-lived molecular states. Using piezoelectric dispensing, two independent streams of ~50-pl droplets of sample are deposited within 10 ms of each other onto the surface of a nanowire EM grid, and the mixing reaction stops when the grid is vitrified in liquid ethane ~100 ms later. We demonstrate this approach for four biological systems where short-lived states are of high interest.
| S-EPMC7799389 | biostudies-literature
Project description:Single-particle cryogenic electron microscopy has recently become a major method for determining the structures of proteins and protein complexes. This has markedly increased the demand for throughput of high-resolution electron microscopes, which are required to produce high-resolution images at high rates. An increase in data-collection throughput can be achieved by using large beam-image shifts combined with off-axis coma correction, enabling the acquisition of multiple images from a large area of the EM grid without moving the microscope stage. Here, the optical properties of the JEOL CRYO ARM 300 electron microscope equipped with a K3 camera were characterized under off-axis illumination conditions. It is shown that efficient coma correction can be achieved for beam-image shifts with an amplitude of at least 10 µm, enabling a routine throughput for data collection of between 6000 and 9000 images per day. Use of the benchmark for the rapid data-collection procedure (with beam-image shifts of up to 7 µm) on apoferritin resulted in a reconstruction at a resolution of 1.7 Å. This demonstrates that the rapid automated acquisition of high-resolution micrographs is possible using a CRYO ARM 300.
| S-EPMC8098478 | biostudies-literature
Project description:Cryo-electron microscopy (cryo-EM) is currently the most powerful technique for determining the structures of large protein complexes and assemblies. Picking single-protein particles from cryo-EM micrographs (images) is a key step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though the emerging machine learning-based particle picking can potentially automate the process, its development is severely hindered by lack of large, high-quality, manually labelled training data. Here, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for single protein particle picking and analysis to address this bottleneck. It consists of manually labelled cryo-EM micrographs of 32 non-redundant, representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). It includes 9,089 diverse, high-resolution micrographs (∼300 cryo-EM images per EMPIAR dataset) in which the coordinates of protein particles were labelled by human experts. The protein particle labelling process was rigorously validated by both 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of machine learning and artificial intelligence methods for automated cryo-EM protein particle picking. The dataset and data processing scripts are available at https://github.com/BioinfoMachineLearning/cryoppp.
| S-EPMC9980126 | biostudies-literature
Project description:Mass spectrometry-based proteomics technologies are the prime methods for the high-throughput identification of proteins expressed in complex biological samples. Nevertheless, mass spectrometry’s technical limitations still hinder its ability to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in a sample. Still today, a large part of the mass spectrometry-based proteomics performed use a data-dependent approach that favors the acquisition of mass spectra and detection of proteins of higher abundance. Combined to the fact that the computational identification of proteins from mass spectrometry data is typically performed after mass spectrometry data acquisition, large numbers of mass spectra are redundantly collected from the same abundant proteins and little to no mass spectra are acquired for proteins of lower abundance. To address this problem, we propose a novel supervised learning algorithm that identifies proteins in real-time as mass spectrometry data is acquired and prevents the further data acquisition related to confidently identified proteins to improve the identification sensitivity of low abundance proteins. We show in real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysates that our approach can identify 92.1% of the proteins using 66.2% of the MS2 spectra acquired in the experiment. We also demonstrate that our approach is fast enough for real-time mass spectrometry analysis, is flexible and that it outperforms previously proposed methods. Our method efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.
2020-10-27 | PXD017673 | Pride