BioSciences

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.

Enzymes populate ensembles of structures necessary for catalysis that are difficult to experimentally characterize. We use time-resolved mix-and-inject serial crystallography at an x-ray free electron laser to observe catalysis in a designed mutant isocyanide hydratase (ICH) enzyme that enhances sampling of important minor conformations. The active site exists in a mixture of conformations, and formation of the thioimidate intermediate selects for catalytically competent substates. The influence of cysteine ionization on the ICH ensemble is validated by determining structures of the enzyme at multiple pH values. Large molecular dynamics simulations in crystallo and time-resolved electron density maps show that Asp17 ionizes during catalysis and causes conformational changes that propagate across the dimer, permitting water to enter the active site for intermediate hydrolysis. ICH exhibits a tight coupling between ionization of active site residues and catalysis-activated protein motions, exemplifying a mechanism of electrostatic control of enzyme dynamics.

Layered BC₃, a metastable phase within the binary boron-carbon system that is composed of graphite-like sheets with hexagonally symmetric C₆B₆ units, has never been successfully crystallized. Instead, poorly-crystalline BC₃-like materials with significant stacking disorder have been isolated, based on the co-pyrolysis of a boron trihalide precursor with benzene at around 800 °C. The halide leaving group (-X) is a significant driving force of these reactions, but the subsequent evolution of gaseous HX species at such high temperatures hampers their scaling up and also prohibits their further use in the presence of hard-casting templates such as ordered silicates. Herein, we report a novel halide-free synthesis route to turbostratic BC₃ with long-range in-plane ordering, as evidenced by multi-wavelength Raman spectroscopy. Judicious pairing of the two molecular precursors is crucial to achieving B-C bond formation and preventing phase-segregation into the thermodynamically favored products. A simple computational method used herein to evaluate the compatibility of bottom-up molecular precursors can be generalized to guide the future synthesis of other metastable materials beyond the boron-carbon system.

PURPOSE: To investigate the impact of reduced injected doses on the quantitative and qualitative assessment of the amyloid PET tracers [18F]flutemetamol and [18F]florbetaben. METHODS: Cognitively impaired and unimpaired individuals (N = 250, 36% Aβ-positive) were included and injected with [18F]flutemetamol (N = 175) or [18F]florbetaben (N = 75). PET scans were acquired in list-mode (90-110 min post-injection) and reduced-dose images were simulated to generate images of 75, 50, 25, 12.5 and 5% of the original injected dose. Images were reconstructed using vendor-provided reconstruction tools and visually assessed for Aβ-pathology. SUVRs were calculated for a global cortical and three smaller regions using a cerebellar cortex reference tissue, and Centiloid was computed. Absolute and percentage differences in SUVR and CL were calculated between dose levels, and the ability to discriminate between Aβ- and Aβ + scans was evaluated using ROC analyses. Finally, intra-reader agreement between the reduced dose and 100% images was evaluated. RESULTS: At 5% injected dose, change in SUVR was 3.72% and 3.12%, with absolute change in Centiloid 3.35CL and 4.62CL, for [18F]flutemetamol and [18F]florbetaben, respectively. At 12.5% injected dose, percentage change in SUVR and absolute change in Centiloid were < 1.5%. AUCs for discriminating Aβ- from Aβ + scans were high (AUC ≥ 0.94) across dose levels, and visual assessment showed intra-reader agreement of > 80% for both tracers. CONCLUSION: This proof-of-concept study showed that for both [18F]flutemetamol and [18F]florbetaben, adequate quantitative and qualitative assessments can be obtained at 12.5% of the original injected dose. However, decisions to reduce the injected dose should be made considering the specific clinical or research circumstances.

Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.

Photoprotection mechanisms are ubiquitous among photosynthetic organisms. The photoprotection capacity of the green alga Chlamydomonas reinhardtii is correlated with protein levels of stress-related light-harvesting complex (LHCSR) proteins, which are strongly induced by high light (HL). However, the dynamic response of overall thylakoid structure during acclimation to growth in HL has not been fully understood. Here, we combined live-cell super-resolution microscopy and analytical membrane subfractionation to investigate macroscale structural changes of thylakoid membranes during HL acclimation in Chlamydomonas. Subdiffraction-resolution live-cell imaging revealed that the overall thylakoid structures became thinned and shrunken during HL acclimation. The stromal space around the pyrenoid also became enlarged. Analytical density-dependent membrane fractionation indicated that the structural changes were partly a consequence of membrane unstacking. The analysis of both an LHCSR loss-of-function mutant, npq4 lhcsr1, and a regulatory mutant that over-expresses LHCSR, spa1-1, showed that structural changes occurred independently of LHCSR protein levels, demonstrating that LHCSR was neither necessary nor sufficient to induce the thylakoid structural changes associated with HL acclimation. In contrast, stt7-9, a mutant lacking a kinase of major light-harvesting antenna proteins, had a slower thylakoid structural response to HL relative to all other lines tested but still showed membrane unstacking. These results indicate that neither LHCSR- nor antenna-phosphorylation-dependent HL acclimation are required for the observed macroscale structural changes of thylakoid membranes in HL conditions.

Electron bifurcation (BF) is an evolutionarily ancient energy coupling mechanism in anaerobes, whose associated enzymatic machinery remains enigmatic. In BF-flavoenzymes, a chemically high-potential electron forms in a thermodynamically favorable fashion by simultaneously dropping the potential of a second electron before its donation to physiological acceptors. The cryo-EM and spectroscopic analyses of the BF-enzyme Fix/EtfABCX from Thermotoga maritima suggest that the BF-site contains a special flavin-adenine dinucleotide and, upon its reduction with NADH, a low-potential electron transfers to ferredoxin and a high-potential electron reduces menaquinone. The transfer of energy from high-energy intermediates must be carefully orchestrated conformationally to avoid equilibration. Herein, anaerobic size exclusion-coupled small-angle X-ray scattering (SEC-SAXS) shows that the Fix/EtfAB heterodimer subcomplex, which houses BF- and electron transfer (ET)-flavins, exists in a conformational equilibrium of compacted and extended states between flavin-binding domains, the abundance of which is impacted by reduction and NAD(H) binding. The conformations identify dynamics associated with the T. maritima enzyme and also recapitulate states identified in static structures of homologous BF-flavoenzymes. Reduction of Fix/EtfABCX's flavins alone is insufficient to elicit domain movements conducive to ET but requires a structural "trigger" induced by NAD(H) binding. Models show that Fix/EtfABCX's superdimer exists in a combination of states with respect to its BF-subcomplexes, suggesting a cooperative mechanism between supermonomers for optimizing catalysis. The correlation of conformational states with pathway steps suggests a structural means with which Fix/EtfABCX may progress through its catalytic cycle. Collectively, these observations provide a structural framework for tracing Fix/EtfABCX's catalysis.

X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; ii) data collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min -- sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS-NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Completing data analysis with 10 mins is a first for XFEL experiments and an important milestone if we are to keep up with data collection trends.

Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

Organic bilayers or amorphous silica films of a few nanometer thickness featuring embedded molecular wires offer opportunities for chemically separating while at the same time electronically connecting photo- or electrocatalytic components. Such ultrathin membranes enable the integration of components for which direct coupling is not sufficiently efficient or stable. Photoelectrocatalytic systems for the generation or utilization of renewable energy are among the most prominent ones for which ultrathin separation layers open up new approaches for component integration for improving efficiency. Recent advances in the assembly and spectroscopic, microscopic, and photoelectrochemical characterization have enabled the systematic optimization of the structure, energetics, and density of embedded molecular wires for maximum charge transfer efficiency. The progress enables interfacial designs for the nanoscale integration of the incompatible oxidation and reduction catalysis environments of artificial photosystems and of microbial (or biomolecular)-abiotic systems for renewable energy.