Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular-weight threshold required for single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure-determination methods. Here, a data-processing scheme is presented that combines routines from X-ray crystallography and new algorithms that have been developed to solve structures from tomograms of nanocrystals. This pipeline handles image-processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. The tolerance of this workflow to the effects of radiation damage is also assessed. The simulations indicate a trade-off between a wider tilt range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors, but not merging errors, can be overcome with additional data sets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure

Recent Major Improvements to the ALS Sector 5 MacromolecularCrystallography Beamlines

Although the Advanced Light Source (ALS) was initially conceived primarily as a low energy (1.9GeV) 3rd generation source of VUV and soft x-ray radiation it was realized very early in the development of the facility that a multipole wiggler source coupled with high quality, (brightness preserving), optics would result in a beamline whose performance across the optimal energy range (5-15keV) for macromolecular crystallography (MX) would be comparable to, or even exceed, that of many existing crystallography beamlines at higher energy facilities. Hence, starting in 1996, a suite of three beamlines, branching off a single wiggler source, was constructed, which together formed the ALS Macromolecular Crystallography Facility. From the outset this facility was designed to cater equally to the needs of both academic and industrial users with a heavy emphasis placed on the development and introduction of high throughput crystallographic tools, techniques, and facilities--such as large area CCD detectors, robotic sample handling and automounting facilities, a service crystallography program, and a tightly integrated, centralized, and highly automated beamline control environment for users. This facility was immediately successful, with the primary Multiwavelength Anomalous Diffraction beamline (5.0.2) in particular rapidly becoming one of the foremost crystallographic facilities in the US--responsible for structures such as the 70S ribosome. This success in-turn triggered enormous growth of the ALS macromolecular crystallography community and spurred the development of five additional ALS MX beamlines all utilizing the newly developed superconducting bending magnets ('superbends') as sources. However in the years since the original Sector 5.0 beamlines were built the performance demands of macromolecular crystallography users have become ever more exacting; with growing emphasis placed on studying larger complexes, more difficult structures, weakly diffracting or smaller crystals, and on more rapidly screening larger numbers of candidate crystals; all of these requirements translate directly into a pressing need for increased flux, a tighter beam focus and faster detectors. With these growing demands in mind a major program of beamline and detector upgrades was initiated in 2004 with the goal of dramatically enhancing all aspects of beamline performance. Approximately $3 million in funding from diverse sources including NIH, LBL, the ALS, and the industrial and academic members of the beamline Participating Research Team (PRT), has been employed to develop and install new high performance beamline optics and to purchase the latest generation of CCD detectors. This project, which reached fruition in early 2007, has now fulfilled all of its original goals--boosting the flux on all three beamlines by up to 20-fold--with a commensurate reduction in exposure and data acquisition times for users. The performance of the Sector 5.0 beamlines is now comparable to that of the latest generation ALS superbend beamlines and, in the case of beamline 5.0.2, even surpasses it by a considerable margin. Indeed, the present performance of this beamline is now, once again, comparable to that envisioned for many MX beamlines planned or under construction on newer or higher energy machines

DLSIA: Deep Learning for Scientific Image Analysis.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis

A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering.

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980