Model Reconstruction from Small-Angle X-Ray Scattering Data Using Deep Learning Methods

Unraveling Multiphase Conversion Pathways in Lithium-Sulfur Batteries through Cryo Transmission Electron Microscopy and Machine Learning-Assisted Operando Neutron Scattering

Understanding the complex physicochemical processes in conversion-type batteries requires investigations across multiple length scales. Here, we present a methodological approach to examine Li-S batteries on the nanoscale by combining cryogenic transmission electron microscopy (cryoTEM) with operando small-angle neutron scattering (SANS). CryoTEM revealed discharge products with a biphasic structure consisting of nanocrystalline Li2S within an amorphous Li2Sx matrix. Data analysis of complementary operando SANS measurements was accelerated by a convolutional neural network trained to predict scattering curves based on plurigaussian random fields, enabling comprehensive parameter space exploration for model fitting. Our findings are in line with disproportionation-driven deposition of Li2S2 particles that agglomerate and partially reduce to Li2S via solid-state conversion. This challenges the conventional view of direct, stepwise electroreduction of polysulfides at the electrode-electrolyte interface. Overall, our multitechnique approach demonstrates the value of combining localized high-resolution imaging with time-resolved operando scattering measurements to understand complex electrochemical conversion pathways in next-generation energy storage systems.

Analyzing aptamer structure and interactions: in silico modelling and instrumental methods

Aptamers are short oligonucleotides that bind specifically to various ligands and are characterized by their low immunogenicity, thermostability, and ease of labeling. Many biomedical applications of aptamers as biosensors and drug delivery agents are currently being actively researched. Selective affinity selection with exponential ligand enrichment (SELEX) allows to discover aptamers for a specific target, but it only provides information about the sequence of aptamers; hence other approaches are used for determining aptamer structure, aptamer-ligand interactions and the mechanism of action. The first one is in silico modelling that allows to infer likely secondary and tertiary structures and model their interactions with a ligand. The second approach is to use instrumental methods to study structure and aptamer-ligand interaction. In silico modelling and instrumental methods are complimentary and their combined use allows to eliminate some ambiguity in their respective results. This review examines both the advantages and limitations of in silico modelling and instrumental approaches currently used to study aptamers, which will allow researchers to develop optimal study designs for analyzing aptamer structure and ligand interactions.

Integrating machine learning with α -SAS for enhanced structural analysis in small-angle scattering: applications in biological and artificial macromolecular complexes

Small-Angle Scattering (SAS), encompassing both X-ray (SAXS) and Neutron (SANS) techniques, is a crucial tool for structural analysis at the nanoscale, particularly in the realm of biological macromolecules. This paper explores the intricacies of SAS, emphasizing its application in studying complex biological systems and the challenges associated with sample preparation and data analysis. We highlight the use of neutron-scattering properties of hydrogen isotopes and isotopic labeling in SANS for probing structures within multi-subunit complexes, employing techniques like contrast variation (CV) for detailed structural analysis. However, traditional SAS analysis methods, such as Guinier and Kratky plots, are limited by their partial use of available data and inability to operate without substantial a priori knowledge of the sample's chemical composition. To overcome these limitations, we introduce a novel approach integrating α -SAS, a computational method for simulating SANS with CV, with machine learning (ML). This approach enables the accurate prediction of scattering contrast in multicomponent macromolecular complexes, reducing the need for extensive sample preparation and computational resources. α -SAS, utilizing Monte Carlo methods, generates comprehensive datasets from which structural invariants can be extracted, enhancing our understanding of the macromolecular form factor in dilute systems. The paper demonstrates the effectiveness of this integrated approach through its application to two case studies: Janus particles, an artificial structure with a known SAS intensity and contrast, and a biological system involving RNA polymerase II in complex with Rtt103. These examples illustrate the method's capability to provide detailed structural insights, showcasing its potential as a powerful tool for advanced SAS analysis in structural biology.

Recent advances in structural characterization of biomacromolecules in foods via small-angle X-ray scattering

Small-angle X-ray scattering (SAXS) is a method for examining the solution structure, oligomeric state, conformational changes, and flexibility of biomacromolecules at a scale ranging from a few Angstroms to hundreds of nanometers. Wide time scales ranging from real time (milliseconds) to minutes can be also covered by SAXS. With many advantages, SAXS has been extensively used, it is widely used in the structural characterization of biomacromolecules in food science and technology. However, the application of SAXS in charactering the structure of food biomacromolecules has not been reviewed so far. In the current review, the principle, theoretical calculations and modeling programs are summarized, technical advances in the experimental setups and corresponding applications of in situ capabilities: combination of chromatography, time-resolved, temperature, pressure, flow-through are elaborated. Recent applications of SAXS for monitoring structural properties of biomacromolecules in food including protein, carbohydrate and lipid are also highlighted, and limitations and prospects for developing SAXS based on facility upgraded and artificial intelligence to study the structural properties of biomacromolecules are finally discussed. Future research should focus on extending machine time, simplifying SAXS data treatment, optimizing modeling methods in order to achieve an integrated structural biology based on SAXS as a practical tool for investigating the structure-function relationship of biomacromolecules in food industry.

Direct experimental observation of blue-light-induced conformational change and intermolecular interactions of cryptochrome

Cryptochromes are blue light receptors that mediate circadian rhythm and magnetic sensing in various organisms. A typical cryptochrome consists of a conserved photolyase homology region domain and a varying carboxyl-terminal extension across species. The structure of the flexible carboxyl-terminal extension and how carboxyl-terminal extension participates in cryptochrome’s signaling function remain mostly unknown. In this study, we uncover the potential missing link between carboxyl-terminal extension conformational changes and downstream signaling functions. Specifically, we discover that the blue-light induced opening of carboxyl-terminal extension in C. reinhardtii animal-like cryptochrome can structurally facilitate its interaction with Rhythm Of Chloroplast 15, a circadian-clock-related protein. Our finding is made possible by two technical advances. Using single-molecule Förster resonance energy transfer technique, we directly observe the displacement of carboxyl-terminal extension by about 15 Å upon blue light excitation. Combining structure prediction and solution X-ray scattering methods, we propose plausible structures of full-length cryptochrome under dark and lit conditions. The structures provide molecular basis for light active conformational changes of cryptochrome and downstream regulatory functions.

Refined structures, protein-docking analysis and single molecule assays provides insights into light-induced conformational changes in the cryptochrome CraCRY.

Parameter inversion of a polydisperse system in small-angle scattering

An accurate and efficient method for model- and form-free inversion of a polydisperse small-angle scattering system is presented. It supports an arbitrary number of model parameters and both 1D and 2D intensity observations.

A general method to invert parameter distributions of a polydisperse system using data acquired from a small-angle scattering (SAS) experiment is presented. The forward problem, i.e. calculating the scattering intensity given the distributions of any causal parameters of a theoretical model, is generalized as a multi-linear map, characterized by a high-dimensional Green tensor that represents the complete scattering physics. The inverse problem, i.e. finding the maximum-likelihood estimation of the parameter distributions (in free form) given the scattering intensity (either a curve or an image) acquired from an experiment, is formulated as a constrained nonlinear programming (NLP) problem. This NLP problem is solved with high accuracy and efficiency via several theoretical and computational enhancements, such as an automatic data scaling for accuracy preservation and GPU acceleration for large-scale multi-parameter systems. Six numerical examples are presented, including both synthetic tests and solutions to real neutron and X-ray data sets, where the method is compared with several existing methods in terms of their generality, accuracy and computational cost. These examples show that SAS inversion is subject to a high degree of non-uniqueness of solution or structural ambiguity. With an ultra-high accuracy, the method can yield a series of near-optimal solutions that fit data to different acceptable levels.

Refining conformational ensembles of flexible proteins against small-angle x-ray scattering data

Intrinsically disordered proteins and flexible regions in multidomain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining one or more biophysical techniques with computational modeling or simulations. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. In both cases, one generally needs a so-called forward model (i.e., an algorithm to calculate experimental observables from individual conformations or ensembles). In many cases, this involves one or more parameters that need to be set, and it is not always trivial to determine the optimal values or to understand the impact on the choice of parameters. For example, in the case of small-angle x-ray scattering (SAXS) experiments, many forward models include parameters that describe the contribution of the hydration layer and displaced solvent to the background-subtracted experimental data. Often, one also needs to fit a scale factor and a constant background for the SAXS data but across the entire ensemble. Here, we present a protocol to dissect the effect of the free parameters on the calculated SAXS intensities and to identify a reliable set of values. We have implemented this procedure in our Bayesian/maximum entropy framework for ensemble refinement and demonstrate the results on four intrinsically disordered proteins and a protein with three domains connected by flexible linkers. Our results show that the resulting ensembles can depend on the parameters used for solvent effects and suggest that these should be chosen carefully. We also find a set of parameters that work robustly across all proteins.

Analysis of the nano and microstructures of the cervical cementum and saliva in periodontitis: A pilot study

OBJECTIVES

During the progression of periodontitis, the structures of the cementum and saliva are altered due to pathological changes in the environment. This study aimed to analyze the nanostructures of the cervical cementum and saliva in patients with periodontitis.

METHODS

Patients with periodontitis (n = 10) and periodontally healthy controls (n = 8) were included. Single-rooted teeth with indications for extraction were obtained from individuals. The cervical-thirds of the roots were sectioned transversely to obtain 1 mm thick sections. Unstimulated whole saliva samples were collected from each individual. The nanostructures of the cementum and saliva were analyzed using small and wide-angle X-ray scattering methods.

RESULTS

The mean radius and distance values of the cementum nanoparticles in the periodontitis and control groups were 368 Å and 1152 Å, and 377 Å and 1186 Å, respectively. The mean radius and distance values of the saliva nanoparticles in the periodontitis and control groups were 425 Å and 1359 Å, and 468 Å and 1452 Å, respectively. More wide-angle X-ray scattering profile peaks were observed in the cementum of the controls. Similarities were observed between the 3D profiles of the cementum and the saliva nanoparticles.

CONCLUSIONS

According to the results of the present study, (i) the cementum and saliva nanoparticles were of similar size in periodontitis and healthy controls, (ii) the cementum was more crystalline according to the (002) crystallographic plane in controls, and (iii) the similarities in the 3D-profile of the cementum and saliva nanoparticles suggest some interactions between them in the sulcus/periodontal pocket at the nanolevel.

Latent representation learning in biology and translational medicine

Current data generation capabilities in the life sciences render scientists in an apparently contradicting situation. While it is possible to simultaneously measure an ever-increasing number of systems parameters, the resulting data are becoming increasingly difficult to interpret. Latent variable modeling allows for such interpretation by learning non-measurable hidden variables from observations. This review gives an overview over the different formal approaches to latent variable modeling, as well as applications at different scales of biological systems, such as molecular structures, intra- and intercellular regulatory up to physiological networks. The focus is on demonstrating how these approaches have enabled interpretable representations and ultimately insights in each of these domains. We anticipate that a wider dissemination of latent variable modeling in the life sciences will enable a more effective and productive interpretation of studies based on heterogeneous and high-dimensional data modalities.

Towards molecular movies with X-ray photon correlation spectroscopy

In this perspective article we highlight research opportunities and challenges in probing structural dynamics of molecular systems using X-ray Photon Correlation Spectroscopy (XPCS). The development of new X-ray sources, such as 4th generation storage rings and X-ray free-electron lasers (XFELs), provides promising new insights into molecular motion. Employing XPCS at these sources allows to capture a very broad range of timescales and lengthscales, spanning from femtoseconds to minutes and atomic scales to the mesoscale. Here, we discuss the scientific questions that can be addressed with these novel tools for two prominent examples: the dynamics of proteins in biomolecular condensates and the dynamics of supercooled water. Finally, we provide practical tips for designing and estimating feasibility of XPCS experiments as well as on detecting and mitigating radiation damage.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

Machine learning deciphers structural features of RNA duplexes measured with solution X-ray scattering

Macromolecular structures can be determined from solution X-ray scattering. Small-angle X-ray scattering (SAXS) provides global structural information on length scales of 10s to 100s of Ångstroms, and many algorithms are available to convert SAXS data into low-resolution structural envelopes. Extension of measurements to wider scattering angles (WAXS or wide-angle X-ray scattering) can sharpen the resolution to below 10 Å, filling in structural details that can be critical for biological function. These WAXS profiles are especially challenging to interpret because of the significant contribution of solvent in addition to solute on these smaller length scales. Based on training with molecular dynamics generated models, the application of extreme gradient boosting (XGBoost) is discussed, which is a supervised machine learning (ML) approach to interpret features in solution scattering profiles. These ML methods are applied to predict key structural parameters of double-stranded ribonucleic acid (dsRNA) duplexes. Duplex conformations vary with salt and sequence and directly impact the foldability of functional RNA molecules. The strong structural periodicities in these duplexes yield scattering profiles with rich sets of features at intermediate-to-wide scattering angles. In the ML models, these profiles are treated as 1D images or features. These ML models identify specific scattering angles, or regions of scattering angles, which correspond with and successfully predict distinct structural parameters. Thus, this work demonstrates that ML strategies can integrate theoretical molecular models with experimental solution scattering data, providing a new framework for extracting highly relevant structural information from solution experiments on biological macromolecules.

SAS-cam: a program for automatic processing and analysis of small-angle scattering data

Small-angle X-ray scattering (SAXS) is a widely used method for investigating biological macromolecules in structural biology, providing information on macromolecular structures and dynamics in solution. Modern synchrotron SAXS beamlines are characterized as high-throughput, capable of collecting large volumes of data and thus demanding fast data processing for efficient beamline operations. This article presents a fully automated and high-throughput SAXS data analysis pipeline, SAS-cam, primarily based on the SASTBX package. Five modules are included in SAS-cam, encompassing the data analysis process from data reduction to model interpretation. The model parameters are extracted from SAXS profiles and stored in an HTML summary file, ready for online visualization using a web browser. SAS-cam can provide the user with the possibility of optimizing experimental parameters based on real-time feedback and it therefore significantly improves the efficiency of beam time. SAS-cam is installed on the BioSAXS beamline at the Shanghai Synchrotron Radiation Facility. The source code is available upon request.