Advances in integrative structural biology: Towards understanding protein complexes in their cellular context

BioSpring: An elastic network framework for interactive exploration of macromolecular mechanics

BioSpring is an innovative tool for interactive molecular modeling and simulation, designed to explore the dynamics of biological structures in real time. Using an augmented elastic network model, the BioSpring framework enables researchers to intuitively examine complex biomolecules, and it combines real-time feedback with the user's experience. This capability makes it ideal for initial analysis of molecular systems, protein-protein and protein-DNA docking, protein mechanics, and protein-membrane interactions. The multi-resolution modeling approach combines accuracy and efficiency, supporting user-driven analysis of molecular interactions, conformational flexibility, and structural mechanics. This framework improves upon traditional methods in terms of robustness, accessibility, and ease of use, while requiring only modest computational resources and enabling a fast turnaround time to obtain initial results. It provides insights into molecular function and dynamics that advance the field of structural biology. Source code, executables, and examples for the BioSpring simulation engine are available at https://biospring.mol3d.tech.

Unraveling the catalase dynamics: Biophysical and computational insights into co-solutes driven stabilization under extreme pH conditions

Catalase plays a vital role in eliminating toxic peroxides from the human body and the environment. The versatile applications of this enzyme extend across biotechnological industries and innovative bioremediation approaches. Nonetheless, ensuring enzyme stability is a challenging task. This study investigated the efficacy of co-solutes (glucose and dextran 70) as stabilizing agents for catalase under denaturing pH conditions by employing a combination of spectroscopic techniques (UV-visible, circular dichroism, and Trp fluorescence), calorimetric measurements (DSC and ITC), enzymatic assay, and in silico studies. The results of spectroscopic and thermal stability studies indicated that the co-solutes tend to stabilize catalase, even under extreme pH conditions. Molecular docking and ITC findings showed that glucose has a higher binding tendency to catalase than dextran 70. MD simulations further underscore reduced structural deviations (RMSF and RMSD), compact structure (Rg and SASA), and formation of H-bonds between catalase and co-solutes, complementing the in vitro observations. This study contributes to the understanding of enzyme stability under suboptimal pH conditions and paves the way for the development of more robust enzyme formulations suitable for a range of applications.

RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data

High-throughput characterization of antibody-antigen complexes at the atomic level is critical for understanding antibody function and enabling therapeutic development. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) enables rapid epitope mapping, but its data are too sparse for independent structure determination. In this study, we introduce RosettaHDX, a hybrid method that combines computational docking with differential HDX-MS data to enhance the accuracy of antibody-antigen complex models beyond what either method can achieve individually. By incorporating HDX data as both distance restraints and a scoring term in the RosettaDock algorithm, RosettaHDX successfully generated near-native models (interface root-mean square deviation ≤ 4 Å) for all 9 benchmark complexes examined, averaging 3.6 times more near-native models than Rosetta alone. Near-native models among the top 10 scoring were identified in 3/9 cases, compared to 1/9 with Rosetta alone. Additionally, we developed a predictive metric based on docking results with HDX restraints to identify allosteric peptides in HDX datasets.

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

Effect of SNORD113-3/ADAR2 on glycolipid metabolism in glioblastoma via A-to-I editing of PHKA2

BACKGROUND

Glioblastoma multiforme (GBM) is a highly aggressive brain tumor, characterized by its poor prognosis. Glycolipid metabolism is strongly associated with GBM development and malignant behavior. However, the precise functions of snoRNAs and ADARs in glycolipid metabolism within GBM cells remain elusive. The objective of the present study is to delve into the underlying mechanisms through which snoRNAs and ADARs exert regulatory effects on glycolipid metabolism in GBM cells.

METHODS

RNA immunoprecipitation and RNA pull-down experiments were conducted to verify the homodimerization of ADAR2 by SNORD113-3, and Sanger sequencing and Western blot experiments were used to detect the A-to-I RNA editing of PHKA2 mRNA by ADAR2. Furthermore, the phosphorylation of EBF1 was measured by in vitro kinase assay. Finally, in vivo studies using nude mice confirmed that SNORD113-3 and ADAR2 overexpression, along with PHKA2 knockdown, could suppress the formation of subcutaneous xenograft tumors and improve the outcome of tumor-bearing nude mice.

RESULTS

We found that PHKA2 in GBM significantly promoted glycolipid metabolism, while SNORD113-3, ADAR2, and EBF1 significantly inhibited glycolipid metabolism. SNORD113-3 promotes ADAR2 protein expression by promoting ADAR2 homodimer formation. ADAR2 mediates the A-to-I RNA editing of PHKA2 mRNA. Mass spectrometry analysis and in vitro kinase testing revealed that PHKA2 phosphorylates EBF1 on Y256, reducing the stability and expression of EBF1. Furthermore, direct binding of EBF1 to PKM2 and ACLY promoters was observed, suggesting the inhibition of their expression by EBF1. These findings suggest the existence of a SNORD113-3/ADAR2/PHKA2/EBF1 pathway that collectively regulates the metabolism of glycolipid and the growth of GBM cells. Finally, in vivo studies using nude mice confirmed that knockdown of PHKA2, along with overexpression of SNORD113-3 and ADAR2, could obviously suppress GBM subcutaneous xenograft tumor formation and improve the outcome of those tumor-bearing nude mice.

CONCLUSIONS

Herein, we clarified the underlying mechanism involving the SNORD113-3/ADAR2/PHKA2/EBF1 pathway in the regulation of GBM cell growth and glycolipid metabolism. Our results provide a framework for the development of innovative therapeutic interventions to improve the prognosis of patients with GBM.

What Structural Biology Tells Us About the Mode of Action and Detection of Toxicants

The study of the adverse effects of chemical substances on living organisms is an old and intense field of research. However, toxicological and environmental health sciences have long been dominated by descriptive approaches that enable associations or correlations but relatively few robust causal links and molecular mechanisms. Recent achievements have shown that structural biology approaches can bring this added value to the field. By providing atomic-level information, structural biology is a powerful tool to decipher the mechanisms by which toxicants bind to and alter the normal function of essential cell components, causing adverse effects. Here, using endocrine-disrupting chemicals as illustrative examples, we describe recent advances in the structure-based understanding of their modes of action and how this knowledge can be exploited to develop computational tools aimed at predicting properties of large collections of compounds.

TargetSA: adaptive simulated annealing for target-specific drug design

MOTIVATION

The burgeoning field of target-specific drug design has attracted considerable attention, focusing on identifying compounds with high binding affinity toward specific target pockets. Nevertheless, existing target-specific deep generative models encounter notable challenges. Some models heavily rely on elaborate datasets and complicated training methodologies, while others neglect the multi-constraint optimization problem inherent in drug design, resulting in generated molecules with irrational structures or chemical properties.

RESULTS

To address these issues, we propose a novel framework (TargetSA) that leverages adaptive simulated annealing (SA) for target-specific molecular generation and multi-constraint optimization. The SA process explores the discrete structural space of molecules, progressively converging toward the optimal solution that fulfills the predefined objective. To propose novel compounds, we first predict promising editing positions based on historical experience, and then iteratively edit molecular graphs through four operations (insertion, replacement, deletion, and cyclization). Together, these operations collectively constitute a complete operation set, facilitating a thorough exploration of the drug-like space. Furthermore, we introduce a reversible sampling strategy to re-accept currently suboptimal solutions, greatly enhancing the generation quality. Empirical evaluations demonstrate that TargetSA achieves state-of-the-art performance in generating high-affinity molecules (average vina dock -9.09) while maintaining desirable chemical properties.

AVAILABILITY AND IMPLEMENTATION

https://github.com/XueZhe-Zachary/TargetSA.

Benzofuran and Benzo[b]thiophene-2-Carboxamide Derivatives as Modulators of Amyloid Beta (Aβ42) Aggregation

A group of N-phenylbenzofuran-2-carboxamide and N-phenylbenzo[b]thiophene-2-carboxamide derivatives were designed and synthesized as a novel class of Aβ42 aggregation modulators. In the thioflavin-T based fluorescence aggregation kinetics study, compounds 4 a, 4 b, 5 a and 5 b possessing a methoxyphenol pharmacophore were able to demonstrate concentration dependent inhibition of Aβ42 aggregation with maximum inhibition of 54 % observed for compound 4 b. In contrast, incorporation of a 4-methoxyphenyl ring in compounds 4 d and 5 d led to a significant increase in Aβ42 fibrillogenesis demonstrating their ability to accelerate Aβ42 aggregation. Compound 4 d exhibited 2.7-fold increase in Aβ42 fibrillogenesis when tested at the maximum concentration of 25 μM. These results were further confirmed by electron microscopy studies which demonstrates the ability of compounds 4 a, 4 b, 4 d, 5 a, 5 b and 5 d to modulate Aβ42 fibrillogenesis. Compounds 5 a and 5 b provided significant neuroprotection to mouse hippocampal neuronal HT22 cells against Aβ42-induced cytotoxicity. Molecular docking studies suggest that the orientation of the bicyclic aromatic rings (either benzofuran or benzo[b]thiophene) plays a major role in moderating their ability to either inhibit or accelerate Aβ42 aggregation. Our findings support the application of these novel derivatives as pharmacological tools to study the mechanisms of Aβ42 aggregation.

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy Topographic Images

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

Exploring snake venoms beyond the primary sequence: From proteoforms to protein-protein interactions

Snakebite envenomation has been a long-standing global issue that is difficult to treat, largely owing to the flawed nature of current immunoglobulin-based antivenom therapy and the complexity of snake venoms as sophisticated mixtures of bioactive proteins and peptides. Comprehensive characterisation of venom compositions is essential to better understanding snake venom toxicity and inform effective and rationally designed antivenoms. Additionally, a greater understanding of snake venom composition will likely unearth novel biologically active proteins and peptides that have promising therapeutic or biotechnological applications. While a bottom-up proteomic workflow has been the main approach for cataloguing snake venom compositions at the toxin family level, it is unable to capture snake venom heterogeneity in the form of protein isoforms and higher-order protein interactions that are important in driving venom toxicity but remain underexplored. This review aims to highlight the importance of understanding snake venom heterogeneity beyond the primary sequence, in the form of post-translational modifications that give rise to different proteoforms and the myriad of higher-order protein complexes in snake venoms. We focus on current top-down proteomic workflows to identify snake venom proteoforms and further discuss alternative or novel separation, instrumentation, and data processing strategies that may improve proteoform identification. The current higher-order structural characterisation techniques implemented for snake venom proteins are also discussed; we emphasise the need for complementary and higher resolution structural bioanalytical techniques such as mass spectrometry-based approaches, X-ray crystallography and cryogenic electron microscopy, to elucidate poorly characterised tertiary and quaternary protein structures. We envisage that the expansion of the snake venom characterisation "toolbox" with top-down proteomics and high-resolution protein structure determination techniques will be pivotal in advancing structural understanding of snake venoms towards the development of improved therapeutic and biotechnology applications.

Investigating the Effects of Chelidonic Acid on Oxidative Stress-Induced Premature Cellular Senescence in Human Skin Fibroblast Cells

This study investigated the effects of chelidonic acid (CA) on hydrogen peroxide (H2O2) induced cellular senescence in human skin fibroblast cells (BJ). Cellular senescence is a critical mechanism that is linked to age-related diseases and chronic conditions. CA, a γ-pyrone compound known for its broad pharmacological activity, was assessed for its potential to mitigate oxidative stress and alter senescence markers. A stress-induced premature senescence (SIPS) model was designed in BJ fibroblast cells using the oxidative stress agent H2O2. After this treatment, cells were treated with CA, and the potential effect of CA on senescence was evaluated using senescence-related β-galactosidase, 4',6-diamino-2-phenylindole (DAPI), acridine-orange staining (AO), comet assay, molecular docking assays, gene expression, and protein analysis. These results demonstrate that CA effectively reduces senescence markers, including senescence-associated β-galactosidase activity, DNA damage, lysosomal activity, and oxidative stress indicators such as malondialdehyde. Molecular docking revealed CA's potential interactions with critical proteins involved in senescence signalling pathways, suggesting mechanisms by which CA may exert its effects. Gene expression and protein analyses corroborated the observed anti-senescent effects, with CA modulating p16, p21, and pRB1 expressions and reducing oxidative stress markers. In conclusion, CA appeared to have senolytic and senomorphic potential in vitro, which could mitigate and reverse SIPS markers in BJ fibroblasts.

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

ProSight Native: Defining Protein Complex Composition from Native Top-Down Mass Spectrometry Data

Native mass spectrometry has recently moved alongside traditional structural biology techniques in its ability to provide clear insights into the composition of protein complexes. However, to date, limited software tools are available for the comprehensive analysis of native mass spectrometry data on protein complexes, particularly for experiments aimed at elucidating the composition of an intact protein complex. Here, we introduce ProSight Native as a start-to-finish informatics platform for analyzing native protein and protein complex data. Combining mass determination via spectral deconvolution with a top-down database search and stoichiometry calculations, ProSight Native can determine the complete composition of protein complexes. To demonstrate its features, we used ProSight Native to successfully determine the composition of the homotetrameric membrane complex Aquaporin Z. We also revisited previously published spectra and were able to decipher the composition of a heterodimer complex bound with two noncovalently associated ligands. In addition to determining complex composition, we developed new tools in the software for validating native mass spectrometry fragment ions and mapping top-down fragmentation data onto three-dimensional protein structures. Taken together, ProSight Native will reduce the informatics burden on the growing field of native mass spectrometry, enabling the technology to further its reach.

Hybrid computational methods combining experimental information with molecular dynamics

A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.

Greater than the sum of parts: Mechanisms of metabolic regulation by enzyme filaments

Recent work in structural biology is shedding light on how many of the enzymes of intermediary metabolism are self- and co-assembling into large, filamentous polymers or agglomerates to organize and regulate the complex and essential biochemical pathways in cells. Filament assembly provides an additional layer of regulation by modulating the intrinsic allostery of the enzyme protomers which tunes activity in response to a variety of environmental cues. Enzyme filaments dynamically assemble and disassemble in response to changes in metabolite levels and environmental cues, shifting metabolic flux on a more rapid timescale than transcriptional or translational reprogramming. Here we present recent examples of high-resolution structures of filaments from proteins in intermediary metabolism and we discuss how filament assembly modulates the activities of these and other proteins.

Dynamic Interactomics by Cross-Linking Mass Spectrometry: Mapping the Daily Cell Life in Postgenomic Era

The majority of processes that occur in daily cell life are modulated by hundreds to thousands of dynamic protein-protein interactions (PPI). The resulting protein complexes constitute a tangled network that, with its continuous remodeling, builds up highly organized functional units. Thus, defining the dynamic interactome of one or more proteins allows determining the full range of biological activities these proteins are capable of. This conceptual approach is poised to gain further traction and significance in the current postgenomic era wherein the treatment of severe diseases needs to be tackled at both genomic and PPI levels. This also holds true for COVID-19, a multisystemic disease affecting biological networks across the biological hierarchy from genome to proteome to metabolome. In this overarching context and the current historical moment of the COVID-19 pandemic where systems biology increasingly comes to the fore, cross-linking mass spectrometry (XL-MS) has become highly relevant, emerging as a powerful tool for PPI discovery and characterization. This expert review highlights the advanced XL-MS approaches that provide in vivo insights into the three-dimensional protein complexes, overcoming the static nature of common interactomics data and embracing the dynamics of the cell proteome landscape. Many XL-MS applications based on the use of diverse cross-linkers, MS detection methods, and predictive bioinformatic tools for single proteins or proteome-wide interactions were shown. We conclude with a future outlook on XL-MS applications in the field of structural proteomics and ways to sustain the remarkable flexibility of XL-MS for dynamic interactomics and structural studies in systems biology and planetary health.

The snoRNA-like lncRNA LNC-SNO49AB drives leukemia by activating the RNA-editing enzyme ADAR1

Long noncoding RNAs (lncRNAs) are usually 5' capped and 3' polyadenylated, similar to most typical mRNAs. However, recent studies revealed a type of snoRNA-related lncRNA with unique structures, leading to questions on how they are processed and how they work. Here, we identify a novel snoRNA-related lncRNA named LNC-SNO49AB containing two C/D box snoRNA sequences, SNORD49A and SNORD49B; and show that LNC-SNO49AB represents an unreported type of lncRNA with a 5'-end m7G and a 3'-end snoRNA structure. LNC-SNO49AB was found highly expressed in leukemia patient samples, and silencing LNC-SNO49AB dramatically suppressed leukemia progression in vitro and in vivo. Subcellular location indicated that the LNC-SNO49AB is mainly located in nucleolus and interacted with the nucleolar protein fibrillarin. However, we found that LNC-SNO49AB does not play a role in 2'-O-methylation regulation, a classical function of snoRNA; instead, its snoRNA structure affected the lncRNA stability. We further demonstrated that LNC-SNO49AB could directly bind to the adenosine deaminase acting on RNA 1(ADAR1) and promoted its homodimerization followed by a high RNA A-to-I editing activity. Transcriptome profiling shows that LNC-SNO49AB and ADAR1 knockdown respectively share very similar patterns of RNA modification change in downstream signaling pathways, especially in cell cycle pathways. These findings suggest a previously unknown class of snoRNA-related lncRNAs, which function via a manner in nucleolus independently on snoRNA-guide rRNA modification. This is the first report that a lncRNA regulates genome-wide RNA A-to-I editing by enhancing ADAR1 dimerization to facilitate hematopoietic malignancy, suggesting that LNC-SNO49AB may be a novel target in therapy directed to leukemia.

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, thus far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions but are relatively slow, if carried out at all-atom resolution, or are significantly coarse grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise fast Fourier transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

EPR Spectroscopy Provides New Insights into Complex Biological Reaction Mechanisms

In the last 20 years, the use of electron paramagnetic resonance (EPR) has made a pronounced and lasting impact in the field of structural biology. The advantage of EPR spectroscopy over other structural techniques is its ability to target even minor conformational changes in any biomolecule or macromolecular complex, independent of its size or complexity, or whether it is in solution or in the cell during a biological or chemical reaction. Here, we focus on the use of EPR spectroscopy to study transmembrane transport and transcription mechanisms. We discuss experimental and analytical concerns when referring to studies of two biological reaction mechanisms, namely, transfer of copper ions by the human copper transporter hCtr1 and the mechanism of action of the Escherichia coli copper-dependent transcription factor CueR. Last, we elaborate on future avenues in the field of EPR structural biology.

Mass spectrometry-based technologies for probing the 3D world of plant proteins

Over the past two decades, mass spectrometric (MS)-based proteomics technologies have facilitated the study of signaling pathways throughout biology. Nowhere is this needed more than in plants, where an evolutionary history of genome duplications has resulted in large gene families involved in posttranslational modifications and regulatory pathways. For example, at least 5% of the Arabidopsis thaliana genome (ca. 1,200 genes) encodes protein kinases and protein phosphatases that regulate nearly all aspects of plant growth and development. MS-based technologies that quantify covalent changes in the side-chain of amino acids are critically important, but they only address one piece of the puzzle. A more crucially important mechanistic question is how noncovalent interactions-which are more difficult to study-dynamically regulate the proteome's 3D structure. The advent of improvements in protein 3D technologies such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography has allowed considerable progress to be made at this level, but these methods are typically limited to analyzing proteins, which can be expressed and purified in milligram quantities. Newly emerging MS-based technologies have recently been developed for studying the 3D structure of proteins. Importantly, these methods do not require protein samples to be purified and require smaller amounts of sample, opening the wider proteome for structural analysis in complex mixtures, crude lysates, and even in intact cells. These MS-based methods include covalent labeling, crosslinking, thermal proteome profiling, and limited proteolysis, all of which can be leveraged by established MS workflows, as well as newly emerging methods capable of analyzing intact macromolecules and the complexes they form. In this review, we discuss these recent innovations in MS-based "structural" proteomics to provide readers with an understanding of the opportunities they offer and the remaining challenges for understanding the molecular underpinnings of plant structure and function.

Building Biological Relevance Into Integrative Modelling of Macromolecular Assemblies

Recent advances in structural biophysics and integrative modelling methods now allow us to decipher the structures of large macromolecular assemblies. Understanding the dynamics and mechanisms involved in their biological function requires rigorous integration of all available data. We have developed a complete modelling pipeline that includes analyses to extract biologically significant information by consistently combining automated and interactive human-guided steps. We illustrate this idea with two examples. First, we describe the ryanodine receptor, an ion channel that controls ion flux across the cell membrane through transitions between open and closed states. The conformational changes associated with the transitions are small compared to the considerable system size of the receptor; it is challenging to consistently track these states with the available cryo-EM structures. The second example involves homologous recombination, in which long filaments of a recombinase protein and DNA catalyse the exchange of homologous DNA strands to reliably repair DNA double-strand breaks. The nucleoprotein filament reaction intermediates in this process are short-lived and heterogeneous, making their structures particularly elusive. The pipeline we describe, which incorporates experimental and theoretical knowledge combined with state-of-the-art interactive and immersive modelling tools, can help overcome these challenges. In both examples, we point to new insights into biological processes that arise from such interdisciplinary approaches.

Overall structure of fully assembled cyanobacterial KaiABC circadian clock complex by an integrated experimental-computational approach

In the cyanobacterial circadian clock system, KaiA, KaiB and KaiC periodically assemble into a large complex. Here we determined the overall structure of their fully assembled complex by integrating experimental and computational approaches. Small-angle X-ray and inverse contrast matching small-angle neutron scatterings coupled with size-exclusion chromatography provided constraints to highlight the spatial arrangements of the N-terminal domains of KaiA, which were not resolved in the previous structural analyses. Computationally built 20 million structural models of the complex were screened out utilizing the constrains and then subjected to molecular dynamics simulations to examine their stabilities. The final model suggests that, despite large fluctuation of the KaiA N-terminal domains, their preferential positionings mask the hydrophobic surface of the KaiA C-terminal domains, hindering additional KaiA-KaiC interactions. Thus, our integrative approach provides a useful tool to resolve large complex structures harboring dynamically fluctuating domains.

The revealed full KaiA₁₂B₆C₆ complex is assembled including the dynamic and asynchronous KaiA N-terminal domains that have been missing in cryo-EM structures.

Using cryo-EM to uncover mechanisms of bacterial transcriptional regulation

Transcription is the principal control point for bacterial gene expression, and it enables a global cellular response to an intracellular or environmental trigger. Transcriptional regulation is orchestrated by transcription factors, which activate or repress transcription of target genes by modulating the activity of RNA polymerase. Dissecting the nature and precise choreography of these interactions is essential for developing a molecular understanding of transcriptional regulation. While the contribution of X-ray crystallography has been invaluable, the 'resolution revolution' of cryo-electron microscopy has transformed our structural investigations, enabling large, dynamic and often transient transcription complexes to be resolved that in many cases had resisted crystallisation. In this review, we highlight the impact cryo-electron microscopy has had in gaining a deeper understanding of transcriptional regulation in bacteria. We also provide readers working within the field with an overview of the recent innovations available for cryo-electron microscopy sample preparation and image reconstruction of transcription complexes.

Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons

Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.

The HIV-1 Nucleocapsid Regulates Its Own Condensation by Phase-Separated Activity-Enhancing Sequestration of the Viral Protease during Maturation

A growing number of studies indicate that mRNAs and long ncRNAs can affect protein populations by assembling dynamic ribonucleoprotein (RNP) granules. These phase-separated molecular ‘sponges’, stabilized by quinary (transient and weak) interactions, control proteins involved in numerous biological functions. Retroviruses such as HIV-1 form by self-assembly when their genomic RNA (gRNA) traps Gag and GagPol polyprotein precursors. Infectivity requires extracellular budding of the particle followed by maturation, an ordered processing of ∼2400 Gag and ∼120 GagPol by the viral protease (PR). This leads to a condensed gRNA-NCp7 nucleocapsid and a CAp24-self-assembled capsid surrounding the RNP. The choreography by which all of these components dynamically interact during virus maturation is one of the missing milestones to fully depict the HIV life cycle. Here, we describe how HIV-1 has evolved a dynamic RNP granule with successive weak–strong–moderate quinary NC-gRNA networks during the sequential processing of the GagNC domain. We also reveal two palindromic RNA-binding triads on NC, KxxFxxQ and QxxFxxK, that provide quinary NC-gRNA interactions. Consequently, the nucleocapsid complex appears properly aggregated for capsid reassembly and reverse transcription, mandatory processes for viral infectivity. We show that PR is sequestered within this RNP and drives its maturation/condensation within minutes, this process being most effective at the end of budding. We anticipate such findings will stimulate further investigations of quinary interactions and emergent mechanisms in crowded environments throughout the wide and growing array of RNP granules.

Completeness and Consistency in Structural Domain Classifications

Domain classifications are a useful resource for computational analysis of the protein structure, but elements of their composition are often opaque to potential users. We perform a comparative analysis of our classification ECOD against the SCOPe, SCOP2, and CATH domain classifications with respect to their constituent domain boundaries and hierarchal organization. The coverage of these domain classifications with respect to ECOD and to the PDB was assessed by structure and by sequence. We also conducted domain pair analysis to determine broad differences in hierarchy between domains shared by ECOD and other classifications. Finally, we present domains from the major facilitator superfamily (MFS) of transporter proteins and provide evidence that supports their split into domains and for multiple conformations within these families. We find that the ECOD and CATH provide the most extensive structural coverage of the PDB. ECOD and SCOPe have the most consistent domain boundary conditions, whereas CATH and SCOP2 both differ significantly.

CellPAINT: Turnkey Illustration of Molecular Cell Biology

CellPAINT is an interactive digital tool that allows non-expert users to create illustrations of the molecular structure of cells and viruses. We present a new release with several key enhancements, including the ability to generate custom ingredients from structure information in the Protein Data Bank, and interaction, grouping, and locking functions that streamline the creation of assemblies and illustration of large, complex scenes. An example of CellPAINT as a tool for hypothesis generation in the interpretation of cryoelectron tomograms is presented. CellPAINT is freely available at http://ccsb.scripps.edu/cellpaint.

From integrative structural biology to cell biology

Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.