Machine Learning Methods for X-Ray Scattering Data Analysis from Biomacromolecular Solutions

Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)

Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales (nanometers to microns). The output of an SAS measurement is the scattered intensity I(q) as a function of q, the scattered wavevector with respect to the incident wave; the latter is represented by its magnitude |q| ≡ q (in inverse distance units) and azimuthal angle θ. While isotropic structural arrangement can be interpreted by analysis of the azimuthally averaged one-dimensional (1D) scattering profile, to understand anisotropic arrangements, one has to interpret the two-dimensional (2D) scattering profile, I(q, θ). Manual interpretation of such 2D profiles usually involves fitting of approximate analytical models to azimuthally averaged sections of the 2D profile. In this paper, we present a new method called CREASE-2D that interprets, without any azimuthal averaging, the entire 2D scattering profile, I(q, θ), and outputs the relevant structural features. CREASE-2D is an extension of the "computational reverse engineering analysis for scattering experiments" (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE-2D goes beyond CREASE by enabling analysis of 2D scattering profiles, which is far more challenging to interpret than the azimuthally averaged 1D profiles. The CREASE-2D workflow identifies the structural features whose computed I(q, θ) profiles, calculated using a surrogate XGBoost machine learning model, match the input experimental I(q, θ). We expect that this CREASE-2D method will be a valuable tool for materials' researchers who need direct interpretation of the 2D scattering profiles in contrast to analyzing azimuthally averaged 1D I(q) vs q profiles that can lose important information related to structural anisotropy.

Automated selection of nanoparticle models for small-angle X-ray scattering data analysis using machine learning

Small-angle X-ray scattering (SAXS) is widely used to analyze the shape and size of nanoparticles in solution. A multitude of models, describing the SAXS intensity resulting from nanoparticles of various shapes, have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis, which can be difficult and time-consuming, especially for non-expert users. An algorithm is proposed, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data. The different algorithms compared are trained and evaluated on a simulated database. This database includes 75 000 scattering spectra from nine nanoparticle models, and realistically simulates two distinct device configurations. It will be made freely available to serve as a basis of comparison for future work. Deploying a universal solution for automatic nanoparticle model selection is a challenge made more difficult by the diversity of SAXS instruments and their flexible settings. The poor transferability of classification rules learned on one device configuration to another is highlighted. It is shown that training on several device configurations enables the algorithm to be generalized, without degrading performance compared with configuration-specific training. Finally, the classification algorithm is evaluated on a real data set obtained by performing SAXS experiments on nanoparticles for each of the instrumental configurations, which have been characterized by transmission electron microscopy. This data set, although very limited, allows estimation of the transferability of the classification rules learned on simulated data to real data.

Structural insights into the formation of repulsive netrin guidance complexes

Netrins dictate attractive and repulsive responses during axon growth and cell migration, where the presence of the receptor Uncoordinated-5 (UNC-5) on target cells results in repulsion. Here, we showed that UNC-5 is a heparin-binding protein, determined its structure bound to a heparin fragment, and could modulate UNC-5-heparin affinity using a directed evolution platform or structure-based rational design. We demonstrated that UNC-5 and UNC-6/netrin form a large, stable, and rigid complex in the presence of heparin, and heparin and UNC-5 exclude the attractive UNC-40/DCC receptor from binding to UNC-6/netrin to a large extent. Caenorhabditis elegans with a heparin-binding-deficient UNC-5 fail to establish proper gonad morphology due to abrogated cell migration, which relies on repulsive UNC-5 signaling in response to UNC-6. Combining UNC-5 mutations targeting heparin and UNC-6/netrin contacts results in complete cell migration and axon guidance defects. Our findings establish repulsive netrin responses to be mediated through a glycosaminoglycan-regulated macromolecular complex.

The Ruminococcus bromii amylosome protein Sas6 binds single and double helical α-glucan structures in starch

Resistant starch is a prebiotic accessed by gut bacteria with specialized amylases and starch-binding proteins. The human gut symbiont Ruminococcus bromii expresses Sas6 (Starch Adherence System member 6), which consists of two starch-specific carbohydrate-binding modules from family 26 (RbCBM26) and family 74 (RbCBM74). Here, we present the crystal structures of Sas6 and of RbCBM74 bound with a double helical dimer of maltodecaose. The RbCBM74 starch-binding groove complements the double helical α-glucan geometry of amylopectin, suggesting that this module selects this feature in starch granules. Isothermal titration calorimetry and native mass spectrometry demonstrate that RbCBM74 recognizes longer single and double helical α-glucans, while RbCBM26 binds short maltooligosaccharides. Bioinformatic analysis supports the conservation of the amylopectin-targeting platform in CBM74s from resistant-starch degrading bacteria. Our results suggest that RbCBM74 and RbCBM26 within Sas6 recognize discrete aspects of the starch granule, providing molecular insight into how this structure is accommodated by gut bacteria.

BioXTAS RAW 2: new developments for a free open-source program for small-angle scattering data reduction and analysis

BioXTAS RAW is a free open-source program for reduction, analysis and modelling of biological small-angle scattering data. Here, the new developments in RAW version 2 are described. These include improved data reduction using pyFAI; updated automated Guinier fitting and D max finding algorithms; automated series (e.g. size-exclusion chromatography coupled small-angle X-ray scattering or SEC-SAXS) buffer- and sample-region finding algorithms; linear and integral baseline correction for series; deconvolution of series data using regularized alternating least squares (REGALS); creation of electron-density reconstructions using electron density via solution scattering (DENSS); a comparison window showing residuals, ratios and statistical comparisons between profiles; and generation of PDF reports with summary plots and tables for all analysis. Furthermore, there is now a RAW API, which can be used without the graphical user interface (GUI), providing full access to all of the functionality found in the GUI. In addition to these new capabilities, RAW has undergone significant technical updates, such as adding Python 3 compatibility, and has entirely new documentation available both online and in the program.

BioXTAS RAW 2: new developments for a free open-source program for small angle scattering data reduction and analysis

BioXTAS RAW is a free, open-source program for reduction, analysis and modelling of biological small angle scattering data. Here, the new developments in RAW version 2 are described. These include: improved data reduction using pyFAI; updated automated Guinier fitting and Dmax finding algorithms; automated series (e.g. SEC-SAXS) buffer and sample region finding algorithms; linear and integral baseline correction for series; deconvolution of series data using REGALS; creation of electron density reconstructions via DENSS; a comparison window showing residuals, ratios, and statistical comparisons between profiles; and generation of PDF reports with summary plots and tables for all analysis. In addition, there is now a RAW API, which can be used without the GUI, providing full access to all of the functionality found in the GUI. In addition to these new capabilities, RAW has undergone significant technical updates, such as adding Python 3 compatibility, and has entirely new documentation available both online and in the program.

Legionella pneumophila macrophage infectivity potentiator protein appendage domains modulate protein dynamics and inhibitor binding

Macrophage infectivity potentiator (MIP) proteins are widespread in human pathogens including Legionella pneumophila, the causative agent of Legionnaires' disease and protozoans such as Trypanosoma cruzi. All MIP proteins contain a FKBP (FK506 binding protein)-like prolyl-cis/trans-isomerase domain that hence presents an attractive drug target. Some MIPs such as the Legionella pneumophila protein (LpMIP) have additional appendage domains of mostly unknown function. In full-length, homodimeric LpMIP, the N-terminal dimerization domain is linked to the FKBP-like domain via a long, free-standing stalk helix. Combining X-ray crystallography, NMR and EPR spectroscopy and SAXS, we elucidated the importance of the stalk helix for protein dynamics and inhibitor binding to the FKBP-like domain and bidirectional crosstalk between the different protein regions. The first comparison of a microbial MIP and a human FKBP in complex with the same synthetic inhibitor was made possible by high-resolution structures of LpMIP with a [4.3.1]-aza-bicyclic sulfonamide and provides a basis for designing pathogen-selective inhibitors. Through stereospecific methylation, the affinity of inhibitors to L. pneumophila and T. cruzi MIP was greatly improved. The resulting X-ray inhibitor-complex structures of LpMIP and TcMIP at 1.49 and 1.34 Å, respectively, provide a starting point for developing potent inhibitors against MIPs from multiple pathogenic microorganisms.

The role of intrinsic disorder in binding of plant microtubule-associated proteins to the cytoskeleton

Microtubules (MTs) represent one of the main components of the eukaryotic cytoskeleton and support numerous critical cellular functions. MTs are in principle tube-like structures that can grow and shrink in a highly dynamic manner; a process largely controlled by microtubule-associated proteins (MAPs). Plant MAPs are a phylogenetically diverse group of proteins that nonetheless share many common biophysical characteristics and often contain large stretches of intrinsic protein disorder. These intrinsically disordered regions are determinants of many MAP-MT interactions, in which structural flexibility enables low-affinity protein-protein interactions that enable a fine-tuned regulation of MT cytoskeleton dynamics. Notably, intrinsic disorder is one of the major obstacles in functional and structural studies of MAPs and represents the principal present-day challenge to decipher how MAPs interact with MTs. Here, we review plant MAPs from an intrinsic protein disorder perspective, by providing a complete and up-to-date summary of all currently known members, and address the current and future challenges in functional and structural characterization of MAPs.

Structural Basis of Clostridium perfringens Enterotoxin Activation and Oligomerization by Trypsin

Clostridium perfringens enterotoxin (CpE) is a β-pore forming toxin that disrupts gastrointestinal homeostasis in mammals by binding membrane protein receptors called claudins. Although structures of CpE fragments bound to claudins have been determined, the mechanisms that trigger CpE activation and oligomerization that lead to the formation of cytotoxic β-pores remain undetermined. Proteolysis of CpE in the gut by trypsin has been shown to play a role in this and subsequent cytotoxicity processes. Here, we report solution structures of full-length and trypsinized CpE using small-angle X-ray scattering (SAXS) and crystal structures of trypsinized CpE and its C-terminal claudin-binding domain (cCpE) using X-ray crystallography. Mass spectrometry and SAXS uncover that removal of the CpE N-terminus by trypsin alters the CpE structure to expose areas that are normally unexposed. Crystal structures of trypsinized CpE and cCpE reveal unique dimer interfaces that could serve as oligomerization sites. Moreover, comparisons of these structures to existing ones predict the functional implications of oligomerization in the contexts of cell receptor binding and β-pore formation. This study sheds light on trypsin's role in altering CpE structure to activate its function via inducing oligomerization on its path toward cytotoxic β-pore formation. Its findings can incite new approaches to inhibit CpE-based cytotoxicity with oligomer-disrupting therapeutics.

Small-angle scattering techniques for peptide and peptide hybrid nanostructures and peptide-based biomaterials

The use of small-angle scattering (SAS) in the study of the self-assembly of peptides and peptide conjugates (lipopeptides, polymer-peptide conjugates and others) is reviewed, highlighting selected research that illustrates different methods and analysis techniques. Both small-angle x-ray scattering (SAXS) and small-angle neutron scattering (SANS) are considered along with examples that exploit their unique capabilities. For SAXS, this includes the ability to perform rapid measurements enabling high throughput or fast kinetic studies and measurements under dilute conditions. For SANS, contrast variation using H2O/D2O mixtures enables the study of peptides interacting with lipids and TR-SANS (time-resolved SANS) studies of exchange kinetics and/or peptide-induced structural changes. Examples are provided of studies measuring form factors of different self-assembled structures (micelles, fibrils, nanotapes, nanotubes etc) as well as structure factors from ordered phases (lyotropic mesophases), peptide gels and hybrid materials such as membranes formed by mixing peptides with polysaccharides or peptide/liposome mixtures. SAXS/WAXS (WAXS: wide-angle x-ray scattering) on peptides and peptide hybrids is also discussed, and the review concludes with a perspective on potential future directions for research in the field.

Crosstalk between regulatory elements in disordered TRPV4 N-terminus modulates lipid-dependent channel activity

Intrinsically disordered regions (IDRs) are essential for membrane receptor regulation but often remain unresolved in structural studies. TRPV4, a member of the TRP vanilloid channel family involved in thermo- and osmosensation, has a large N-terminal IDR of approximately 150 amino acids. With an integrated structural biology approach, we analyze the structural ensemble of the TRPV4 IDR and the network of antagonistic regulatory elements it encodes. These modulate channel activity in a hierarchical lipid-dependent manner through transient long-range interactions. A highly conserved autoinhibitory patch acts as a master regulator by competing with PIP2 binding to attenuate channel activity. Molecular dynamics simulations show that loss of the interaction between the PIP2-binding site and the membrane reduces the force exerted by the IDR on the structured core of TRPV4. This work demonstrates that IDR structural dynamics are coupled to TRPV4 activity and highlights the importance of IDRs for TRP channel function and regulation.

Projection to extract the perpendicular component (PEPC) method for extracting kinetics from time-resolved data

Time-resolved x-ray liquidography (TRXL) is a potent method for investigating the structural dynamics of chemical and biological reactions in the liquid phase. It has enabled the extraction of detailed structural aspects of various dynamic processes, the molecular structures of intermediates, and kinetics of reactions across a wide range of systems, from small molecules to proteins and nanoparticles. Proper data analysis is key to extracting the information of the kinetics and structural dynamics of the studied system encrypted in the TRXL data. In typical TRXL data, the signals from solute scattering, solvent scattering, and solute-solvent cross scattering are mixed in the q-space, and the solute kinetics and solvent hydrodynamics are mixed in the time domain, thus complicating the data analysis. Various methods developed so far generally require prior knowledge of the molecular structures of candidate species involved in the reaction. Because such information is often unavailable, a typical data analysis often involves tedious trial and error. To remedy this situation, we have developed a method named projection to extract the perpendicular component (PEPC), capable of removing the contribution of solvent kinetics from TRXL data. The resulting data then contain only the solute kinetics, and, thus, the solute kinetics can be easily determined. Once the solute kinetics is determined, the subsequent data analysis to extract the structural information can be performed with drastically improved convenience. The application of the PEPC method is demonstrated with TRXL data from the photochemistry of two molecular systems: [Au(CN)₂^-]₃ in water and CHI₃ in cyclohexane.

Machine learning for scattering data: strategies, perspectives and applications to surface scattering

Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.

Determinants of receptor tyrosine phosphatase homophilic adhesion: Structural comparison of PTPRK and PTPRM extracellular domains

Type IIB receptor protein tyrosine phosphatases are cell surface transmembrane proteins that engage in cell adhesion via their extracellular domains (ECDs) and cell signaling via their cytoplasmic phosphatase domains. The ECDs of type IIB receptor protein tyrosine phosphatases form stable, homophilic, and trans interactions between adjacent cell membranes. Previous work has demonstrated how one family member, PTPRM, forms head-to-tail homodimers. However, as the interface was composed of residues conserved across the family, the determinants of homophilic specificity remain unknown. Here, we have solved the X-ray crystal structure of the membrane-distal N-terminal domains of PTPRK that form a head-to-tail dimer consistent with intermembrane adhesion. Comparison with the PTPRM structure demonstrates interdomain conformational differences that may define homophilic specificity. Using small-angle X-ray scattering, we determined the solution structures of the full-length ECDs of PTPRM and PTPRK, identifying that both are rigid extended molecules that differ in their overall long-range conformation. Furthermore, we identified one residue, W351, within the interaction interface that differs between PTPRM and PTPRK and showed that mutation to glycine, the equivalent residue in PTPRM, abolishes PTPRK dimer formation in vitro. This comparison of two members of the receptor tyrosine phosphatase family suggests that homophilic specificity is driven by a combination of shape complementarity and specific but limited sequence differences.

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.

Computational methods for the analysis of solution small-angle X-ray scattering of biomolecules: ATSAS

The ATSAS software suite provides a comprehensive set of programs for the processing, analysis and modeling of small-angle scattering data, tailored for but not limited to data acquired on biological macromolecules. In this review the major components and developments in the ATSAS package are described, with a focus on user driven application. Data reduction, analysis and modeling approaches and strategies will be introduced and discussed. At the time of writing the latest package, ATSAS 3.1, is freely available for academic users at: https://www.embl-hamburg.de/biosaxs/software.html.

Structure-Activity Relationship of the Dimeric and Oligomeric Forms of a Cytotoxic Biotherapeutic Based on Diphtheria Toxin

Protein aggregation is a well-recognized problem in industrial preparation, including biotherapeutics. These low-energy states constantly compete with a native-like conformation, which is more pronounced in the case of macromolecules of low stability in the solution. A better understanding of the structure and function of such aggregates is generally required for the more rational development of therapeutic proteins, including single-chain fusion cytotoxins to target specific receptors on cancer cells. Here, we identified and purified such particles as side products of the renaturation process of the single-chain fusion cytotoxin, composed of two diphtheria toxin (DT) domains and interleukin 13 (IL-13), and applied various experimental techniques to comprehensively understand their molecular architecture and function. Importantly, we distinguished soluble purified dimeric and fractionated oligomeric particles from aggregates. The oligomers are polydisperse and multimodal, with a distribution favoring lower and even stoichiometries, suggesting they are composed of dimeric building units. Importantly, all these oligomeric particles and the monomer are cystine-dependent as their innate disulfide bonds have structural and functional roles. Their reduction triggers aggregation. Presumably the dimer and lower oligomers represent the metastable state, retaining the native disulfide bond. Although significantly reduced in contrast to the monomer, they preserve some fraction of bioactivity, manifested by their IL-13RA2 receptor affinity and selective cytotoxic potency towards the U-251 glioblastoma cell line. These molecular assemblies probably preserve structural integrity and native-like fold, at least to some extent. As our study demonstrated, the dimeric and oligomeric cytotoxin may be an exciting model protein, introducing a new understanding of its monomeric counterpart’s molecular characteristics.

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.

Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn-Hilliard-type simulations using auto-encoder networks

Machine learning methods are used for an automated classification of experimental two-time X-ray photon correlation maps from an arrested liquid-liquid phase separation of a protein solution. The correlation maps are matched with correlation maps generated with Cahn-Hilliard-type simulations of liquid-liquid phase separations according to two simulation parameters and in the last step interpreted in the framework of the simulation. The matching routine employs an auto-encoder network and a differential evolution based algorithm. The method presented here is a first step towards handling large amounts of dynamic data measured at high-brilliance synchrotron and X-ray free-electron laser sources, facilitating fast comparison with phase field models of phase separation.

Novel interaction interfaces mediate the interaction between the NEIL1 DNA glycosylase and mitochondrial transcription factor A

The maintenance of human mitochondrial DNA (mtDNA) is critical for proper cellular function as damage to mtDNA, if left unrepaired, can lead to a diverse array of pathologies. Of the pathways identified to participate in DNA repair within the mitochondria, base excision repair (BER) is the most extensively studied. Protein-protein interactions drive the step-by-step coordination required for the successful completion of this pathway and are important for crosstalk with other mitochondrial factors involved in genome maintenance. Human NEIL1 is one of seven DNA glycosylases that initiates BER in both the nuclear and mitochondrial compartments. In the current work, we scrutinized the interaction between NEIL1 and mitochondrial transcription factor A (TFAM), a protein that is essential for various aspects of mtDNA metabolism. We note, for the first time, that both the N- and C- terminal domains of NEIL1 interact with TFAM revealing a unique NEIL1 protein-binding interface. The interaction between the two proteins, as observed biochemically, appears to be transient and is most apparent at concentrations of low salt. The presence of DNA (or RNA) also positively influences the interaction between the two proteins, and molar mass estimates indicate that duplex DNA is required for complex formation at higher salt concentrations. Hydrogen deuterium exchange mass spectrometry data reveal that both proteins exchange less deuterium upon DNA binding, indicative of an interaction, and the addition of NEIL1 to the TFAM-DNA complex alters the interaction landscape. The transcriptional activity of TFAM appears to be independent of NEIL1 expression under normal cellular conditions, however, in the presence of DNA damage, we observe a significant reduction in the mRNA expression of TFAM-transcribed mitochondrial genes in the absence of NEIL1. Overall, our data indicate that the interaction between NEIL1 and TFAM can be modulated by local environment such as salt concentrations, protein availability, the presence of nucleic acids, as well as the presence of DNA damage.

The Disordered Amino Terminus of the Circadian Enzyme Nocturnin Modulates Its NADP(H) Phosphatase Activity by Changing Protein Dynamics

Endogenous circadian clocks control the rhythmicity of a broad range of behavioral and physiological processes, and this is entrained by the daily fluctuations in light and dark. Nocturnin (Noct) is a rhythmically expressed gene regulated by the circadian clock that belongs to the CCR4 family of endonuclease-exonuclease-phosphatase (EEP) enzymes, and the NOCT protein exhibits phosphatase activity, catalyzing the removal of the 2'-phosphate from NADP(H). In addition to its daily nighttime peak of expression, it is also induced by acute stimuli. Loss of Nocturnin (Noct-/-) in mice results in resistance to high-fat diet-induced obesity, and loss of Noct in HEK293T cells confers a protective effect to oxidative stress. Modeling of the full-length Nocturnin protein reveals a partially structured amino terminus that is disparate from its CCR4 family members. The high sequence conservation of a leucine zipper-like (LZ-like) motif, the only structural element in the amino terminus, highlights the potential importance of this domain in modulating phosphatase activity. In vitro biochemical and biophysical techniques demonstrate that the LZ-like domain within the flexible N-terminus is necessary for preserving the active site cleft in an optimal conformation to promote the efficient turnover of the substrate. This modulation occurs in cis and is pivotal in maintaining the stability and conformational integrity of the enzyme. These new findings suggest an additional layer of modulating the activity of Nocturnin in addition to its rhythmicity to provide fine-tuned control over cellular levels of NADPH.

Recent advances in small-angle scattering and its expanding impact in structural biology

Applications of small-angle scattering (SAS) in structural biology have benefited from continuing developments in instrumentation, tools for data analysis, modeling capabilities, standards for data and model presentation, and data archiving. The interplay of these capabilities has enabled SAS to contribute to advances in structural biology as the field pushes the boundaries in studies of biomolecular complexes and assemblies as large as whole cells, membrane proteins in lipid environments, and dynamic systems on time scales ranging from femtoseconds to hours. This review covers some of the important advances in biomolecular SAS capabilities for structural biology focused on over the last 5 years and presents highlights of recent applications that demonstrate how the technique is exploring new territories.

SAXSMoW 3.0: New advances in the determination of the molecular weight of proteins in dilute solutions from SAXS intensity data on a relative scale

SAXSMoW (SAXS Molecular Weight) is an online platform widely used over the past few years for determination of molecular weights of proteins in dilute solutions. The scattering intensity retrieved from small-angle X-ray scattering (SAXS) raw data is the sole input to SAXSMoW for determination of molecular weights of proteins in liquid. The current updated SAXSMoW version 3.0 determines the linear dependence of the true protein volume on their apparent protein volume, based on SAXS curves calculated for 67,000 protein structures selected from the Protein Data Bank. SAXSMoW 3.0 was tested against 43 experimental SAXS scattering curves from proteins with known molecular weights. Our results demonstrate that most of the molecular weights determined for the nonglycosylated and also for the glycosylated proteins are in good agreement with their expected molecular weights. Additionally, the average discrepancies between the calculated molecular weights and their nominal values for glycosylated proteins are similar to those for nonglycosylated ones.

A General Small-Angle X-ray Scattering-Based Screening Protocol for Studying Physical Stability of Protein Formulations

A fundamental step in developing a protein drug is the selection of a stable storage formulation that ensures efficacy of the drug and inhibits physiochemical degradation or aggregation. Here, we designed and evaluated a general workflow for screening of protein formulations based on small-angle X-ray scattering (SAXS). Our SAXS pipeline combines automated sample handling, temperature control, and fast data analysis and provides protein particle interaction information. SAXS, together with different methods including turbidity analysis, dynamic light scattering (DLS), and SDS-PAGE measurements, were used to obtain different parameters to provide high throughput screenings. Using a set of model proteins and biopharmaceuticals, we show that SAXS is complementary to dynamic light scattering (DLS), which is widely used in biopharmaceutical research and industry. We found that, compared to DLS, SAXS can provide a more sensitive measure for protein particle interactions, such as protein aggregation and repulsion. Moreover, we show that SAXS is compatible with a broader range of buffers, excipients, and protein concentrations and that in situ SAXS provides a sensitive measure for long-term protein stability. This workflow can enable future high-throughput analysis of proteins and biopharmaceuticals and can be integrated with well-established complementary physicochemical analysis pipelines in (biopharmaceutical) research and industry.

Neural network analysis of neutron and x-ray reflectivity data: pathological cases, performance and perspectives

Neutron and x-ray reflectometry (NR and XRR) are powerful techniques to investigate the structural, morphological and even magnetic properties of solid and liquid thin films. While neutrons and x-rays behave similarly in many ways and can be described by the same general theory, they fundamentally differ in certain specific aspects. These aspects can be exploited to investigate different properties of a system, depending on which particular questions need to be answered. Having demonstrated the general applicability of neural networks to analyze XRR and NR data before (Greco et al 2019 J. Appl. Cryst. 52 1342), this study discusses challenges arising from certain pathological cases as well as performance issues and perspectives. These cases include a low signal-to-noise ratio, a high background signal (e.g. from incoherent scattering), as well as a potential lack of a total reflection edge (TRE). By dynamically modifying the training data after every mini batch, a fully-connected neural network was trained to determine thin film parameters from reflectivity curves. We show that noise and background intensity pose no significant problem as long as they do not affect the TRE. However, for curves without strong features the prediction accuracy is diminished. Furthermore, we compare the prediction accuracy for different scattering length density combinations. The results are demonstrated using simulated data of a single-layer system while also discussing challenges for multi-component systems.

The Disease Associated Tau35 Fragment has an Increased Propensity to Aggregate Compared to Full-Length Tau

Tau35 is a truncated form of tau found in human brain in a subset of tauopathies. Tau35 expression in mice recapitulates key features of human disease, including progressive increase in tau phosphorylation, along with cognitive and motor dysfunction. The appearance of aggregated tau suggests that Tau35 may have structural properties distinct from those of other tau species that could account for its pathological role in disease. To address this hypothesis, we performed a structural characterization of monomeric and aggregated Tau35 and compared the results to those of two longer isoforms, 2N3R and 2N4R tau. We used small angle X-ray scattering to show that Tau35, 2N3R and 2N4R tau all behave as disordered monomeric species but Tau35 exhibits higher rigidity. In the presence of the poly-anion heparin, Tau35 increases thioflavin T fluorescence significantly faster and to a greater extent than full-length tau, demonstrating a higher propensity to aggregate. By using atomic force microscopy, circular dichroism, transmission electron microscopy and X-ray fiber diffraction, we provide evidence that Tau35 aggregation is mechanistically and morphologically similar to previously reported tau fibrils but they are more densely packed. These data increase our understanding of the aggregation inducing properties of clinically relevant tau fragments and their potentially damaging role in the pathogenesis of human tauopathies.

Structural, thermodynamic and functional studies of human 71 kDa heat shock cognate protein (HSPA8/hHsc70)

Human 71 kDa heat shock cognate protein (HSPA8, also known as Hsc70, Hsp70-8, Hsc71, Hsp71 or Hsp73) is a constitutively expressed chaperone that is critical for cell proteostasis. In the cytosol, HSPA8 plays a pivotal role in folding and refolding, facilitates protein trafficking across membranes and targets proteins for degradation, among other functions. Here, we report an in solution study of recombinant HSPA8 (rHSPA8) using a variety of biophysical and biochemical approaches. rHSPA8 shares several structural and functional similarities with others human Hsp70s. It has two domains with different stabilities and interacts with adenosine nucleotides with dissociation constants in the low micromolar range, which were higher in the presence of Mg²⁺. rHSPA8 showed lower ATPase activity than its homolog HSPA5/hGrp78/hBiP, but it was 4-fold greater than that of recombinant HSPA1A/hHsp70-1A, with which it is 86% identical. Small angle X-ray scattering indicated that rHSPA8 behaved as an elongated monomeric protein in solution with dimensions similar to those observed for HSPA1A. In addition, rHSPA8 showed structural flexibility between its compacted and extended conformations. The data also indicated that HSPA8 has capacity in preventing the aggregation of model client proteins. The present study expands the understanding of the structure and activity of this chaperone and aligns with the idea that human homologous Hsp70s have divergent functions.

Identification of a Region in the Common Amino-terminal Domain of Hendra Virus P, V, and W Proteins Responsible for Phase Transition and Amyloid Formation

Henipaviruses are BSL-4 zoonotic pathogens responsible in humans for severe encephalitis. Their V protein is a key player in the evasion of the host innate immune response. We previously showed that the Henipavirus V proteins consist of a long intrinsically disordered N-terminal domain (NTD) and a β-enriched C-terminal domain (CTD). These terminals are critical for V binding to DDB1, which is a cellular protein that is a component of the ubiquitin ligase E3 complex, as well as binding to MDA5 and LGP2, which are two host sensors of viral RNA. Here, we serendipitously discovered that the Hendra virus V protein undergoes a liquid-to-hydrogel phase transition and identified the V region responsible for this phenomenon. This region, referred to as PNT3 and encompassing residues 200-310, was further investigated using a combination of biophysical and structural approaches. Congo red binding assays, together with negative-staining transmisison electron microscopy (TEM) studies, show that PNT3 forms amyloid-like fibrils. Fibrillation abilities are dramatically reduced in a rationally designed PNT3 variant in which a stretch of three contiguous tyrosines, falling within an amyloidogenic motif, were replaced by three alanines. Worthy to note, Congo red staining experiments provided hints that these amyloid-like fibrils form not only in vitro but also in cellula after transfection or infection. The present results set the stage for further investigations aimed at assessing the functional role of phase separation and fibrillation by the Henipavirus V proteins.

On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.

Insights into the structure and function of the C-terminus of SGTs (small glutamine-rich TPR-containing proteins): A study of the Aedes aegypti homolog

SGTs (small glutamine-rich TPR-containing proteins) are dimeric proteins that belong to the class of co-chaperones characterized by the presence of TPR domains (containing tetratricopeptide repeats). Human (SGTA) and yeast (Sgt2) SGTs are characterized by three distinct domains: an N-terminal dimerization domain, a central TPR-domain important for binding to other proteins (chaperones included) and a C-terminal domain involved in hydrophobic interactions. Both these SGTs are involved in the cellular PQC (protein quality control) system, as they interact with chaperones and have functions that aid stress recovery. However, there are differences between them, such as structural features and binding specificities, that could be better understood if other orthologous proteins were studied. Therefore, we produced and characterized a putative SGT protein, designated AaSGT, from the mosquito Aedes aegypti, which is a vector of several diseases, such as dengue and Zika. The protein was produced as a folded dimer which was stable up to 40 °C and was capable of binding to AaHsp90 and fully protecting a model protein, α-synuclein, from aggregation. The conformation of AaSGT was investigated by biophysical tools and small angle X-ray scattering, which showed that the protein had an elongated conformation and that its C-terminal domain was mainly disordered. The results with a C-terminal deletion mutant supported these observations. Altogether, these results are consistent with those from other functional SGT proteins and add to the understanding of the PQC system in Aedes aegypti, an important aim that may help to develop inhibitory strategies against this vector of neglected diseases.

Advances in the Development of Sol-Gel Materials Combining Small-Angle X-ray Scattering (SAXS) and Machine Learning (ML)

The requirements for new materials are increasing with each new application, which, in most cases, means an enhancement in the complexity of the development process. Nanoporous sol-gel-based materials, especially aerogels, are promising candidates for thermal superinsulation, electrodes for energy conversion and storage or high-end adsorbers. Their synthesis and processing route is complex, and the relationship between the material/processing parameters and the resulting structural and physical properties is not straightforward. Using small-angle X-ray scattering (SAXS) allows for fast structural characterization of both the gel and the resulting aerogel; combining these results with the respective physical properties of the aerogels and using these data as inputs for machine learning (ML) algorithms provide an approach to predict physical properties on the basis of a structural dataset. This data-driven strategy may be a feasible approach to speed up the development process. Thus, the study aimed to provide a proof of concept of ML-based model derivation from material, process and SAXS data to predict physical properties such as the solid-phase thermal conductivity (λs) of silica aerogels from a structural dataset. Here, we used different data subsets as predictors according to different states of synthesis (wet and dry) to evaluate the model performance.

Structure and thermodynamics of transient protein-protein complexes by chemometric decomposition of SAXS datasets

Transient biomolecular interactions play crucial roles in many cellular signaling and regulation processes. However, deciphering the structure of these assemblies is challenging owing to the difficulties in isolating complexes from the individual partners. The additive nature of small-angle X-ray scattering (SAXS) data allows for probing the species present in these mixtures, but decomposition into structural and thermodynamic information is difficult. We present a chemometric approach enabling the decomposition of titration SAXS data into species-specific information. Using extensive synthetic SAXS data, we demonstrate that robust decomposition can be achieved for titrations with a maximum fraction of complex of 0.5 that can be extended to 0.3 when two orthogonal titrations are simultaneously analyzed. The effect of the structural features, titration points, relative concentrations, and noise are thoroughly analyzed. The validation of the strategy with experimental data highlights the power of the approach to provide unique insights into this family of biomolecular assemblies.

An Integrative Structural Biology Analysis of Von Willebrand Factor Binding and Processing by ADAMTS-13 in Solution

Von Willebrand Factor (vWF), a 300-kDa plasma protein key to homeostasis, is cleaved at a single site by multi-domain metallopeptidase ADAMTS-13. vWF is the only known substrate of this peptidase, which circulates in a latent form and becomes allosterically activated by substrate binding. Herein, we characterised the complex formed by a competent peptidase construct (AD13-MDTCS) comprising metallopeptidase (M), disintegrin-like (D), thrombospondin (T), cysteine-rich (C), and spacer (S) domains, with a 73-residue functionally relevant vWF-peptide, using nine complementary techniques. Pull-down assays, gel electrophoresis, and surface plasmon resonance revealed tight binding with sub-micromolar affinity. Cross-linking mass spectrometry with four reagents showed that, within the peptidase, domain D approaches M, C, and S. S is positioned close to M and C, and the peptide contacts all domains. Hydrogen/deuterium exchange mass spectrometry revealed strong and weak protection for C/D and M/S, respectively. Structural analysis by multi-angle laser light scattering and small-angle X-ray scattering in solution revealed that the enzyme adopted highly flexible unbound, latent structures and peptide-bound, active structures that differed from the AD13-MDTCS crystal structure. Moreover, the peptide behaved like a self-avoiding random chain. We integrated the results with computational approaches, derived an ensemble of structures that collectively satisfied all experimental restraints, and discussed the functional implications. The interaction conforms to a 'fuzzy complex' that follows a 'dynamic zipper' mechanism involving numerous reversible, weak but additive interactions that result in strong binding and cleavage. Our findings contribute to illuminating the biochemistry of the vWF:ADAMTS-13 axis.

ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis

The ATSAS software suite encompasses a number of programs for the processing, visualization, analysis and modelling of small-angle scattering data, with a focus on the data measured from biological macromolecules. Here, new developments in the ATSAS 3.0 package are described. They include IMSIM, for simulating isotropic 2D scattering patterns; IMOP, to perform operations on 2D images and masks; DATRESAMPLE, a method for variance estimation of structural invariants through parametric resampling; DATFT, which computes the pair distance distribution function by a direct Fourier transform of the scattering data; PDDFFIT, to compute the scattering data from a pair distance distribution function, allowing comparison with the experimental data; a new module in DATMW for Bayesian consensus-based concentration-independent molecular weight estimation; DATMIF, an ab initio shape analysis method that optimizes the search model directly against the scattering data; DAMEMB, an application to set up the initial search volume for multiphase modelling of membrane proteins; ELLLIP, to perform quasi-atomistic modelling of liposomes with elliptical shapes; NMATOR, which models conformational changes in nucleic acid structures through normal mode analysis in torsion angle space; DAMMIX, which reconstructs the shape of an unknown intermediate in an evolving system; and LIPMIX and BILMIX, for modelling multilamellar and asymmetric lipid vesicles, respectively. In addition, technical updates were deployed to facilitate maintainability of the package, which include porting the PRIMUS graphical interface to Qt5, updating SASpy - a PyMOL plugin to run a subset of ATSAS tools - to be both Python 2 and 3 compatible, and adding utilities to facilitate mmCIF compatibility in future ATSAS releases. All these features are implemented in ATSAS 3.0, freely available for academic users at https://www.embl-hamburg.de/biosaxs/software.html.

Increasing complexity in small-angle X-ray and neutron scattering experiments: from biological membrane mimics to live cells

Small-angle X-ray and neutron scattering are well-established, non-invasive experimental techniques to interrogate global structural properties of biological membrane mimicking systems under physiologically relevant conditions. Recent developments, both in bottom-up sample preparation techniques for increasingly complex model systems, and in data analysis techniques have opened the path toward addressing long standing issues of biological membrane remodelling processes. These efforts also include emerging quantitative scattering studies on live cells, thus enabling a bridging of molecular to cellular length scales. Here, we review recent progress in devising compositional models for joint small-angle X-ray and neutron scattering studies on diverse membrane mimics - with a specific focus on membrane structural coupling to amphiphatic peptides and integral proteins - and live Escherichia coli. In particular, we outline the present state-of-the-art in small-angle scattering methods applied to complex membrane systems, highlighting how increasing system complexity must be followed by an advance in compositional modelling and data-analysis tools.

Structural Characterization of Biomaterials by Means of Small Angle X-rays and Neutron Scattering (SAXS and SANS), and Light Scattering Experiments

Scattering techniques represent non-invasive experimental approaches and powerful tools for the investigation of structure and conformation of biomaterial systems in a wide range of distances, ranging from the nanometric to micrometric scale. More specifically, small-angle X-rays and neutron scattering and light scattering techniques represent well-established experimental techniques for the investigation of the structural properties of biomaterials and, through the use of suitable models, they allow to study and mimic various biological systems under physiologically relevant conditions. They provide the ensemble averaged (and then statistically relevant) information under in situ and operando conditions, and represent useful tools complementary to the various traditional imaging techniques that, on the contrary, reveal more local structural information. Together with the classical structure characterization approaches, we introduce the basic concepts that make it possible to examine inter-particles interactions, and to study the growth processes and conformational changes in nanostructures, which have become increasingly relevant for an accurate understanding and prediction of various mechanisms in the fields of biotechnology and nanotechnology. The upgrade of the various scattering techniques, such as the contrast variation or time resolved experiments, offers unique opportunities to study the nano- and mesoscopic structure and their evolution with time in a way not accessible by other techniques. For this reason, highly performant instruments are installed at most of the facility research centers worldwide. These new insights allow to largely ameliorate the control of (chemico-physical and biologic) processes of complex (bio-)materials at the molecular length scales, and open a full potential for the development and engineering of a variety of nano-scale biomaterials for advanced applications.

Structural basis for DNA recognition and allosteric control of the retinoic acid receptors RAR-RXR

Retinoic acid receptors (RARs) as a functional heterodimer with retinoid X receptors (RXRs), bind a diverse series of RA-response elements (RAREs) in regulated genes. Among them, the non-canonical DR0 elements are bound by RXR–RAR with comparable affinities to DR5 elements but DR0 elements do not act transcriptionally as independent RAREs. In this work, we present structural insights for the recognition of DR5 and DR0 elements by RXR–RAR heterodimer using x-ray crystallography, small angle x-ray scattering, and hydrogen/deuterium exchange coupled to mass spectrometry. We solved the crystal structures of RXR–RAR DNA-binding domain in complex with the Rarb2 DR5 and RXR–RXR DNA-binding domain in complex with Hoxb13 DR0. While cooperative binding was observed on DR5, the two molecules bound non-cooperatively on DR0 on opposite sides of the DNA. In addition, our data unveil the structural organization and dynamics of the multi-domain RXR–RAR DNA complexes providing evidence for DNA-dependent allosteric communication between domains. Differential binding modes between DR0 and DR5 were observed leading to differences in conformation and structural dynamics of the multi-domain RXR–RAR DNA complexes. These results reveal that the topological organization of the RAR binding element confer regulatory information by modulating the overall topology and structural dynamics of the RXR–RAR heterodimers.

Adding Size Exclusion Chromatography (SEC) and Light Scattering (LS) Devices to Obtain High-Quality Small Angle X-Ray Scattering (SAXS) Data

We describe the updated size-exclusion chromatography small angle X-ray scattering (SEC-SAXS) set-up used at the P12 bioSAXS beam line of the European Molecular Biology Laboratory (EMBL) at the PETRAIII synchrotron, DESY Hamburg (Germany). The addition of size exclusion chromatography (SEC) directly on-line to the SAXS capillary has become a well-established approach to reduce the effects of the sample heterogeneity on the SAXS measurements. The additional use of multi-angle laser light scattering (MALLS), UV absorption spectroscopy, refractive index (RI), and quasi-elastic light scattering (QELS) in parallel to the SAXS measurements enables independent molecular weight validation and hydrodynamic radius estimates. This allows one to address sample monodispersity as well as conformational heterogeneity. The benefits of the current SEC-SAXS set-up are demonstrated on a set of selected standard proteins. The processed SEC-SAXS data and models are provided in the Small Angle Scattering Biological Data Bank (SASBDB) and are labeled as “bench-marked” datasets that include the unsubtracted data frames spanning the respective SEC elution profiles and corresponding MALLS-UV-RI-QELS data. These entries provide method developers with datasets suitable for testing purposes, in addition to an educational resource for SAS data analysis and modeling.

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.

Methods, development and applications of small-angle X-ray scattering to characterize biological macromolecules in solution

Applications of small-angle X-ray scattering (SAXS) in structural biology are reviewed. A brief introduction of the SAXS basics is followed by the presentation of the structural features of biological macromolecules in solution that can be assessed by SAXS. The approaches are considered allowing one to obtain low resolution three-dimensional (3D) structural models and to describe assembly states and conformations. Metrics and descriptors required for the assessment of model quality are presented and recent biological applications of SAXS are shown.

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

Small-angle X-ray scattering (SAXS) method is widely used in investigating protein structures in solution, but high-quality 3D model reconstructions are challenging. We present a new algorithm based on a deep learning method for model reconstruction from SAXS data. An auto-encoder for protein 3D models was trained to compress 3D shape information into vectors of a 200-dimensional latent space, and the vectors are optimized using genetic algorithms to build 3D models that are consistent with the scattering data. The program has been tested with experimental SAXS data, demonstrating the capacity and robustness of accurate model reconstruction. Furthermore, the model size information can be optimized using this algorithm, enhancing the automation in model reconstruction directly from SAXS data. The program was implemented using Python with the TensorFlow framework, with source code and webserver available from http://liulab.csrc.ac.cn/decodeSAXS.

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A convolutional neural network auto-encoder framework for 3D models is developed

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The auto-encoder compresses protein shape information to 200 parameters

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Accurate 3D models (both shape and radius) can be reconstructed from 1D SAXS data

Synchrotron Scattering Methods for Nanomaterials and Soft Matter Research

This article aims to provide an overview of broad range of applications of synchrotron scattering methods in the investigation of nanoscale materials. These scattering techniques allow the elucidation of the structure and dynamics of nanomaterials from sub-nm to micron size scales and down to sub-millisecond time ranges both in bulk and at interfaces. A major advantage of scattering methods is that they provide the ensemble averaged information under in situ and operando conditions. As a result, they are complementary to various imaging techniques which reveal more local information. Scattering methods are particularly suitable for probing buried structures that are difficult to image. Although, many qualitative features can be directly extracted from scattering data, derivation of detailed structural and dynamical information requires quantitative modeling. The fourth-generation synchrotron sources open new possibilities for investigating these complex systems by exploiting the enhanced brightness and coherence properties of X-rays.

Zinc-finger protein CNBP alters the 3-D structure of lncRNA Braveheart in solution

Long non-coding RNAs (lncRNAs) constitute a significant fraction of the transcriptome, playing important roles in development and disease. However, our understanding of structure-function relationships for this emerging class of RNAs has been limited to secondary structures. Here, we report the 3-D atomistic structural study of epigenetic lncRNA, Braveheart (Bvht), and its complex with CNBP (Cellular Nucleic acid Binding Protein). Using small angle X-ray scattering (SAXS), we elucidate the ensemble of Bvht RNA conformations in solution, revealing that Bvht lncRNA has a well-defined, albeit flexible 3-D structure that is remodeled upon CNBP binding. Our study suggests that CNBP binding requires multiple domains of Bvht and the RHT/AGIL RNA motif. We show that RHT/AGIL, previously shown to interact with CNBP, contains a highly flexible loop surrounded by more ordered helices. As one of the largest RNA-only 3-D studies, the work lays the foundation for future structural studies of lncRNA-protein complexes.

Structural determination of Enzyme-Graphene Nanocomposite Sensor Material

State-of-the-art ultra-sensitive blood glucose-monitoring biosensors, based on glucose oxidase (GOx) covalently linked to a single layer graphene (SLG), will be a valuable next generation diagnostic tool for personal glycemic level management. We report here our observations of sensor matrix structure obtained using a multi-physics approach towards analysis of small-angle neutron scattering (SANS) on graphene-based biosensor functionalized with GOx under different pH conditions for various hierarchical GOx assemblies within SLG. We developed a methodology to separately extract the average shape of GOx molecules within the hierarchical assemblies. The modeling is able to resolve differences in the average GOx dimer structure and shows that treatment under different pH conditions lead to differences within the GOx at the dimer contact region with SLG. The coupling of different analysis methods and modeling approaches we developed in this study provides a universal approach to obtain detailed structural quantifications, for establishing robust structure-property relationships. This is an essential step to obtain an insight into the structure and function of the GOx-SLG interface for optimizing sensor performance.

Conformational characterization of full-length X-chromosome-linked inhibitor of apoptosis protein (XIAP) through an integrated approach

The X-chromosome-linked inhibitor of apoptosis protein (XIAP) is a multidomain protein whose main function is to block apoptosis by caspase inhibition. XIAP is also involved in other signalling pathways, including NF-κB activation and copper homeostasis. XIAP is overexpressed in tumours, potentiating cell survival and resistance to chemotherapeutics, and has therefore become an important target for the treatment of malignancy. Despite the fact that the structure of each single domain is known, the conformation of the full-length protein has never been determined. Here, the first structural model of the full-length XIAP dimer, determined by an integrated approach using nuclear magnetic resonance, small-angle X-ray scattering and electron paramagnetic resonance data, is presented. It is shown that XIAP adopts a compact and relatively rigid conformation, implying that the spatial arrangement of its domains must be taken into account when studying the interactions with its physiological partners and in developing effective inhibitors.