Current data processing strategies for cryo-electron tomography and subtomogram averaging

Cryo-electron tomography (cryo-ET) can be used to reconstruct three-dimensional (3D) volumes, or tomograms, from a series of tilted two-dimensional images of biological objects in their near-native states in situ or in vitro. 3D subvolumes, or subtomograms, containing particles of interest can be extracted from tomograms, aligned, and averaged in a process called subtomogram averaging (STA). STA overcomes the low signal to noise ratio within the individual subtomograms to generate structures of the particle(s) of interest. In recent years, cryo-ET with STA has increasingly been capable of reaching subnanometer resolution due to improvements in microscope hardware and data processing strategies. There has also been an increase in the number and quality of software packages available to process cryo-ET data with STA. In this review, we describe and assess the data processing strategies available for cryo-ET data and highlight the recent software developments which have enabled the extraction of high-resolution information from cryo-ET datasets

The impact of meteorology on the interannual growth rate of atmospheric methane

The impact of interannual changes in meteorology on the local and global growth rates of atmospheric methane is assessed in a nineteen year simulation using a tropospheric chemical transport model forced by ECMWF meteorological analyses from 1980 to 1998. A very simple CH4 chemistry scheme has been implemented, using prescribed OH fields. There are no interannual variations in modeled methane emissions or in the OH fields, so any changes in the modeled growth rate arise from changes in meteorology. The methane simulation shows significant interannual variability at both local and global scales. The local scale variability is comparable in magnitude to the interannual variability found in surface observations and shows some clear correlation with observed changes in growth rates. This suggests that, even over interannual timescales, meteorology could be important in driving the interannual fluctuations of atmospheric methane at the surface

A Bayesian approach to single-particle electron cryo-tomography in RELION-4.0

We present a new approach for macromolecular structure determination from multiple particles in electron cryo-tomography (cryo-ET) data sets. Whereas existing subtomogram averaging approaches are based on 3D data models, we propose to optimise a regularised likelihood target that approximates a function of the 2D experimental images. In addition, analogous to Bayesian polishing and contrast transfer function (CTF) refinement in single-particle analysis, we describe the approaches that exploit the increased signal-to-noise ratio in the averaged structure to optimise tilt-series alignments, beam-induced motions of the particles throughout the tilt-series acquisition, defoci of the individual particles, as well as higher-order optical aberrations of the microscope. Implementation of our approaches in the open-source software package RELION aims to facilitate their general use, particularly for those researchers who are already familiar with its single-particle analysis tools. We illustrate for three applications that our approaches allow structure determination from cryo-ET data to resolutions sufficient for de novo atomic modelling.This work was funded by the UK Research and Innovation (UKRI) Medical Research Council (MC_UP_A025_1013 to SHWS; and MC_UP_1201/16 to JAGB), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (ERC-CoG-2014, grant 648432, MEMBRANEFUSION to JAGB and ERC StG-2019, grant 852915 CRYTOCOP to GZ); the Swiss National Science Foundation (grant 205321_179041/1 to DC-D), the Max Planck Society (to JAGB) and the UKRI Biotechnology and Biological Sciences Research Council (grant BB/T002670/1 to GZ). TAMB is a recipient of a Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and the Royal Society (202231/Z/16/Z). JZ was partially funded by the European Union’s Horizon 2020 research and innovation program (ERC-ADG-2015, grant 692726, GlobalBioIm to Michael Unser)

ATP-induced asymmetric pre-protein folding as a driver of protein translocation through the Sec machinery

Funding: Royal Society for a University Research Fellowship; Wellcome Multi-User Equipment Grant (099149/Z/12/Z) (JEL).Transport of proteins across membranes is a fundamental process, achieved in every cell by the 'Sec' translocon. In prokaryotes, SecYEG associates with the motor ATPase SecA to carry out translocation for pre-protein secretion. Previously, we proposed a Brownian ratchet model for transport, whereby the free energy of ATP-turnover favours the directional diffusion of the polypeptide [Allen et al. eLife 2016]. Here, we show that ATP enhances this process by modulating secondary structure formation within the translocating protein. A combination of molecular simulation with hydrogen-deuterium-exchange mass spectrometry and electron paramagnetic resonance spectroscopy reveal an asymmetry across the membrane: ATP induced conformational changes in the cytosolic cavity promote unfolded pre-protein structure, while the exterior cavity favours its formation. This ability to exploit structure within a pre-protein is an unexplored area of protein transport, which may apply to other protein transporters, such as those of the endoplasmic reticulum and mitochondria.Publisher PDFPeer reviewe

A study of machine learning object detection performance for phased array ultrasonic testing of carbon fibre reinforced plastics

The growing adoption of Carbon Fibre Reinforced Plastics (CFRPs) in the aerospace industry has resulted in a significant reliance on Non-Destructive Evaluation (NDE) to ensure the quality and integrity of these materials. The interpretation of large amounts of data acquired from automated robotic ultrasonic scanning by expert operators is often time consuming, tedious, and prone to human error creating a bottleneck in the manufacturing process. However, with ever growing trend of computing power and digitally stored NDE data, intelligent Machine Learning (ML) algorithms have been gaining more traction than before for NDE data analysis. In this study, the performance of ML object detection models, statistical methods for defect detection, and traditional amplitude thresholding approaches for defect detection in CFRPs were compared. A novel augmentation technique was used to enhance synthetically generated datasets used for ML model training. All approaches were tested on real data obtained from an experimental setup mimicking industrial conditions, with ML models showing improvement over amplitude thresholding and statistical thresholding techniques. The advantages and limitations of all methods are reported and discussed

Automated deep learning for defect detection in carbon fibre reinforced plastic composites

Carbon Fibre Reinforced Polymers (CFRPs) are used extensively in the aerospace industry because of their unique physical properties and reduced weight that enables lower fuel consumption. This increase was especially rapid in the past decade, with CFRPs accounting for around 50% of the total material weight used in flagship models by Airbus and Boeing [1,2]. Before shipping, Non-Destructive Testing (NDT) methods are used to validate and control the quality of manufactured parts. Commonly used NDT technologies are radiographic testing, eddy current testing, and Ultrasonic Testing (UT). In the aerospace industry, UT is most prominent due to its flexibility and safety. However, when UT is done manually, reliability issues are often observed due to human inspector errors [3]. In addition to this, manufactured parts that need to be inspected are quite large (e.g., wing covers), resulting in slow inspection times. On the other hand, when NDT robotic inspection is deployed, large amounts of data can be captured in a short period of time. While this accelerates the acquisition of information, data interpretation is still done manually thus creating a bottleneck. Therefore, an automated data interpretation system would greatly improve the NDT process. To overcome these challenges, this project proposes a fully automated Deep Learning (DL) approach that leverages current technological advances in Machine Learning (ML) field for defect localization, sizing, and automatic report generation based on ultrasonic amplitude C-scans. Such an approach could decrease the processing time from approximately 6 hours for a 15-meter wing cover to just minutes, significantly benefiting the process throughput. In this research, a manually annotated semi-analytical simulated dataset in form of C-scans was used for training of "You Only Look Once" family of models for the detection and sizing of back-drilled holes and delamination defects in CFRPs. The purpose of using model-based simulations for training was the scarcity of real-world data, and a novel approach of image augmentation was introduced to ensure that the simulated scans closely mimic the experimental data. For NDT inspection, a force-torque-controlled 6-axis industrial robotic arm was used to deliver a phased array ultrasound roller probe to both defect-free and defective CFRP samples of varying thicknesses. The roller-probe array was connected to an array controller and water-coupled to the surface of the CFRPs. Raster scans were performed while the array was excited in linear-scan mode with a sub-aperture of 4 elements and an operating frequency of 5 MHz. Lastly, amplitude C-scan images of 64 x 64 resolution were extracted and used as an object detection validation dataset. These combined methods result in an accurate and precise deep learning network that enables rapid analysis of image data (with the possibility of real-time analysis)

Insights into protein-lipid interactions by structural mass spectrometry

Interactions with membrane lipids have long been known to affect a wide range of membrane protein properties including folding, stability, and function. Our understanding of the precise molecular basis of these interactions is currently limited. Here, we combine mass spectrometry (MS)-based techniques with molecular dynamics (MD) simulations, in vivo biochemical assays, and other biophysical techniques to characterise how protein-lipid interactions affect the oligomerisation, conformation, and function of the structurally related xanthine/uric acid transporter UapA from Aspergillus nidulans and the boron transporter BOR1 from Saccharomyces cerevisiae (ScBOR1p). Using native MS and lipidomics, we found that UapA requires phosphatidylethanolamine or phosphatidylinositol binding to form the physiological dimer. A putative lipid binding site at the dimer interface was identified using MD simulations. Lipid binding at this site is essential for formation of functional UapA dimers. Similar analysis of ScBOR1p revealed that this protein is primarily monomeric in detergent-based solution and that phosphatidylethanolamine or phosphatidylserine binding is also essential for its dimerization. Mutation of a putative lipid binding site in ScBOR1p results in loss of lipid-induced dimer formation but has no effect on transport function. Thus, the lipids play slightly different roles in UapA and ScBOR1p. Additional research probed the conformational dynamics of UapA in different lipid compositions using hydrogen-deuterium exchange MS (HDX-MS). The results revealed that protein-lipid interactions stabilise the mobile domain of UapA responsible for substrate transport. HDX-MS also revealed how substrate binding stabilises the inward facing conformation of UapA and that specific mutations stabilise the outward facing conformation of UapA. Together, this work represents the first detailed analyses of the interactions between eukaryotic membrane transporters and their associated lipids.Open Acces