From words to literature in structural proteomics

Overcoming resolution loss due to thermal magnetic field fluctuations from phase plates in transmission electron microscopy

We identify thermal magnetic field fluctuations, caused by thermal electron motion ("Johnson noise") in electrically conductive materials, as a potential resolution limit in transmission electron microscopy with a phase plate. Specifically, resolution loss can occur if the electron diffraction pattern is magnified to extend phase contrast to lower spatial frequencies, and if conductive materials are placed too close to the electron beam. While our initial implementation of a laser phase plate (LPP) was significantly affected by these factors, a redesign eliminated the problem and brought the performance close to the expected level. The resolution now appears to be limited by residual Johnson noise arising from the electron beam liner tube in the region of the LPP, together with the chromatic aberration of the relay optics. These two factors can be addressed during future development of the LPP.

Development of General-purpose Dielectric Constant Imaging Unit for SEM and Direct Observation of Samples in Aqueous Solution

Electron microscopes can observe samples with a spatial resolution of 10 nm or higher; however, they cannot observe samples in solutions due to the vacuum conditions inside the sample chamber. Recently, we developed a scanning electron-assisted dielectric microscope (SE-ADM), based on scanning electron microscope, which enables the observation of various specimens in solution. Until now, the SE-ADM system used a custom-made SE-ADM stage with a built-in amplifier and could not be linked to the scanning electron microscopy (SEM) operation system. Therefore, it was necessary to manually acquire images from the SE-ADM system after setting the EB focus, astigmatism, and observation field-of-view from the SEM operating console. In this study, we developed a general-purpose dielectric constant imaging unit attached to commercially available SEMs. The new SE-ADM unit can be directly attached to the standard stage of an SEM, and the dielectric signal detected from this unit can be input to the external input terminal of the SEM, enabling simultaneous observation yielding SEM and SE-ADM images. Furthermore, 4.5 nm spatial resolution was achieved using a 10 nm thick silicon nitride film in the sample holder in the observation of aggregated PM2.5. We carried out the observation of cultured cells, PM2.5, and clay samples in solution.

Cryo-EM tomography and automatic segmentation delineate modular structures in the postsynaptic density

Postsynaptic densities (PSDs) are large protein complexes associated with the postsynaptic membrane of excitatory synapses important for synaptic function including plasticity. Conventional electron microscopy (EM) typically depicts PSDs as compact disk-like structures of hundreds of nanometers in size. Biochemically isolated PSDs were also similar in dimension revealing a predominance of proteins with the ability to polymerize into an extensive scaffold; several EM studies noted their irregular contours with often small granular structures (<30 nm) and holes. Super-resolution light microscopy studies observed clusters of PSD elements and their activity-induced lateral movement. Furthermore, our recent EM study on PSD fractions after sonication observed PSD fragments (40–90 nm in size) separate from intact PSDs; however, such structures within PSDs remained unidentified. Here we examined isolated PSDs by cryo-EM tomography with our new approach of automatic segmentation that enables delineation of substructures and their quantitative analysis. The delineated substructures broadly varied in size, falling behind 30 nm or exceeding 100 nm and showed that a considerable portion of the substructures (>38%) in isolated PSDs was in the same size range as those fragments. Furthermore, substructures spanning the entire thickness of the PSD were found, large enough to contain both membrane-associated and cytoplasmic proteins of the PSD; interestingly, they were similar to nanodomains in frequency. The structures detected here appear to constitute the isolated PSD as modules of various compositions, and this modular nature may facilitate remodeling of the PSD for proper synaptic function and plasticity.

Ion Mobility Mass Spectrometry (IM-MS) for Structural Biology: Insights Gained by Measuring Mass, Charge, and Collision Cross Section

The investigation of macromolecular biomolecules with ion mobility mass spectrometry (IM-MS) techniques has provided substantial insights into the field of structural biology over the past two decades. An IM-MS workflow applied to a given target analyte provides mass, charge, and conformation, and all three of these can be used to discern structural information. While mass and charge are determined in mass spectrometry (MS), it is the addition of ion mobility that enables the separation of isomeric and isobaric ions and the direct elucidation of conformation, which has reaped huge benefits for structural biology. In this review, where we focus on the analysis of proteins and their complexes, we outline the typical features of an IM-MS experiment from the preparation of samples, the creation of ions, and their separation in different mobility and mass spectrometers. We describe the interpretation of ion mobility data in terms of protein conformation and how the data can be compared with data from other sources with the use of computational tools. The benefit of coupling mobility analysis to activation via collisions with gas or surfaces or photons photoactivation is detailed with reference to recent examples. And finally, we focus on insights afforded by IM-MS experiments when applied to the study of conformationally dynamic and intrinsically disordered proteins.

Overcoming resolution loss due to thermal magnetic field fluctuations from phase plates in transmission electron microscopy

We identify thermal magnetic field fluctuations, caused by thermal electron motion ("Johnson noise") in electrically conductive materials, as a potential resolution limit in transmission electron microscopy with a phase plate. Specifically, resolution loss can occur if the electron diffraction pattern is magnified to extend phase contrast to lower spatial frequencies, and if conductive materials are placed too close to the electron beam. While our initial implementation of a laser phase plate (LPP) was significantly affected by these factors, a redesign eliminated the problem and brought the performance close to the expected level. The resolution now appears to be limited by residual Johnson noise arising from the electron beam liner tube in the region of the LPP, together with the chromatic aberration of the relay optics. These two factors can be addressed during future development of the LPP.

A scoring function for the prediction of protein complex interfaces based on the neighborhood preferences of amino acids

Proteins often assemble into functional complexes, the structures of which are more difficult to obtain than those of the individual protein molecules. Given the structures of the subunits, it is possible to predict plausible complex models via computational methods such as molecular docking. Assessing the quality of the predicted models is crucial to obtain correct complex structures. Here, an energy-scoring function was developed based on the interfacial residues of structures in the Protein Data Bank. The statistically derived energy function (Nepre) imitates the neighborhood preferences of amino acids, including the types and relative positions of neighboring residues. Based on the preference statistics, a program iNepre was implemented and its performance was evaluated with several benchmarking decoy data sets. The results show that iNepre scores are powerful in model ranking to select the best protein complex structures.

Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling

Covalent labeling (CL) in combination with mass spectrometry can be used as an analytical tool to study and determine structural properties of protein-protein complexes. However, data from these experiments is sparse and does not unambiguously elucidate protein structure. Thus, computational algorithms are needed to deduce structure from the CL data. In this work, we present a hybrid method that combines models of protein complex subunits generated with AlphaFold with differential CL data via a CL-guided protein-protein docking in Rosetta. In a benchmark set, the RMSD (root-mean-square deviation) of the best-scoring models was below 3.6 Å for 5/5 complexes with inclusion of CL data, whereas the same quality was only achieved for 1/5 complexes without CL data. This study suggests that our integrated approach can successfully use data obtained from CL experiments to distinguish between nativelike and non-nativelike models.

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.

Functional interaction between S100A1 and MDM2 may modulate p53 signaling in normal and malignant endometrial cells

BACKGROUND

S100A1 expression is deregulated in a variety of human malignancies, but its role in normal and malignant endometrial cells is unclear.

METHODS

We used endometrial carcinoma (Em Ca) cell lines to evaluate the physical and functional interaction of S100A1 with p53 and its negative regulator, mouse double minute 2 (MDM2). We also evaluated the expression of S100A1, p53, and MDM2 in clinical samples consisting of 89 normal endometrial and 189 Em Ca tissues.

RESULTS

S100A1 interacted with MDM2 but not p53 in Em Ca cell lines. Treatment of cells stably overexpressing S100A1 with Nutlin-3A, an inhibitor of the p53/MDM2 interaction, increased expression of p53-target genes including p21^waf1 and BAX. S100A1 overexpression enhanced cellular migration, but also sensitized cells to the antiproliferative and proapoptotic effects of Adriamycin, a genotoxic agent; these phenotypes were abrogated when S100A1 was knocked down using shRNA. In clinical samples from normal endometrium, S100A1 expression was significantly higher in endometrial glandular cells of the middle/late secretory and menstrual stages when compared to cells in the proliferative phases; high S100A1 was also positively correlated with expression of MDM2 and p21^waf1 and apoptotic status, and inversely correlated with Ki-67 scores. However, such correlations were absent in Em Ca tissues.

CONCLUSION

The interaction between S100A1 and MDM2 may modulate proliferation, susceptibility to apoptosis, and migration through alterations in p53 signaling in normal- but not malignant-endometrial cells.

Ten things I `hate' about refinement

Macromolecular refinement is an optimization process that aims to produce the most likely macromolecular structural model in the light of experimental data. As such, macromolecular refinement is one of the most complex optimization problems in wide use. Macromolecular refinement programs have to deal with the complex relationship between the parameters of the atomic model and the experimental data, as well as a large number of types of prior knowledge about chemical structure. This paper draws attention to areas of unfinished business in the field of macromolecular refinement. In it, we describe ten refinement topics that we think deserve attention and discuss directions leading to macromolecular refinement software that would make the best use of modern computer resources to meet the needs of structural biologists of the twenty-first century.

It's noisy out there! A review of denoising techniques in cryo-electron tomography

Cryo-electron tomography is the only technique that can provide sub-nanometer resolved images of cell regions or even whole cells, without the need of labeling or staining methods. Technological advances over the past decade in electron microscope stability, cameras, stage precision and software have resulted in faster acquisition speeds and considerably improved resolution. In pursuit of even better image resolution, researchers seek to reduce noise - a crucial factor affecting the reliability of the tomogram interpretation and ultimately limiting the achieved resolution. Sub-tomogram averaging is the method of choice for reducing noise in repetitive objects. However, when averaging is not applicable, a trade-off between reducing noise and conserving genuine image details must be achieved. Thus, denoising is an important process that improves the interpretability of the tomogram not only directly but also by facilitating other downstream tasks, such as segmentation and 3D visualization. Here, I review contemporary denoising techniques for cryo-electron tomography by taking into account noise-specific properties of both reconstruction and detector noise. The outcomes of different techniques are compared, in order to help researchers select the most appropriate for each dataset and to achieve better and more reliable interpretation of the tomograms.

In-Cell Labeling and Mass Spectrometry for Systems-Level Structural Biology

Biological systems have evolved to utilize proteins to accomplish nearly all functional roles needed to sustain life. A majority of biological functions occur within the crowded environment inside cells and subcellular compartments where proteins exist in a densely packed complex network of protein-protein interactions. The structural biology field has experienced a renaissance with recent advances in crystallography, NMR, and CryoEM that now produce stunning models of large and complex structures previously unimaginable. Nevertheless, measurements of such structural detail within cellular environments remain elusive. This review will highlight how advances in mass spectrometry, chemical labeling, and informatics capabilities are merging to provide structural insights on proteins, complexes, and networks that exist inside cells. Because of the molecular detection specificity provided by mass spectrometry and proteomics, these approaches provide systems-level information that not only benefits from conventional structural analysis, but also is highly complementary. Although far from comprehensive in their current form, these approaches are currently providing systems structural biology information that can uniquely reveal how conformations and interactions involving many proteins change inside cells with perturbations such as disease, drug treatment, or phenotypic differences. With continued advancements and more widespread adaptation, systems structural biology based on in-cell labeling and mass spectrometry will provide an even greater wealth of structural knowledge.

Trapped Ion Mobility Spectrometry of Native Macromolecular Assemblies

The structural elucidation of native macromolecular assemblies has been a subject of considerable interest in native mass spectrometry (MS), and more recently in tandem with ion mobility spectrometry (IMS-MS), for a better understanding of their biochemical and biophysical functions. In the present work, we describe a new generation trapped ion mobility spectrometer (TIMS), with extended mobility range (K0 = 0.185-1.84 cm2·V-1·s-1), capable of trapping high-molecular-weight (MW) macromolecular assemblies. This compact 4 cm long TIMS analyzer utilizes a convex electrode, quadrupolar geometry with increased pseudopotential penetration in the radial dimension, extending the mobility trapping to high-MW species under native state (i.e., lower charge states). The TIMS capabilities to perform variable scan rate (Sr) mobility measurements over short time (100-500 ms), high-mobility resolution, and ion-neutral collision cross-section (CCSN2) measurements are presented. The trapping capabilities of the convex electrode TIMS geometry and ease of operation over a wide gas flow, rf range, and electric field trapping range are illustrated for the first time using a comprehensive list of standards varying from CsI clusters (n = 6-73), Tuning Mix oligomers (n = 1-5), common proteins (e.g., ubiquitin, cytochrome C, lysozyme, concanavalin (n = 1-4), carbonic anhydrase, β clamp (n = 1-4), topoisomerase IB, bovine serum albumin (n = 1-3), topoisomerase IA, alcohol dehydrogenase), IgG antibody (e.g., avastin), protein-DNA complexes, and macromolecular assemblies (e.g., GroEL and RNA polymerase (n = 1-2)) covering a wide mass (up to m/z 19 000) and CCS range (up to 22 000 Å2 with <0.6% relative standard deviation (RSD)).

Applications of Multi-Omics Technologies for Crop Improvement

Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

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.

The promise and the challenges of cryo-electron tomography

Structural biologists have traditionally approached cellular complexity in a reductionist manner in which the cellular molecular components are fractionated and purified before being studied individually. This 'divide and conquer' approach has been highly successful. However, awareness has grown in recent years that biological functions can rarely be attributed to individual macromolecules. Most cellular functions arise from their concerted action, and there is thus a need for methods enabling structural studies performed in situ, ideally in unperturbed cellular environments. Cryo-electron tomography (Cryo-ET) combines the power of 3D molecular-level imaging with the best structural preservation that is physically possible to achieve. Thus, it has a unique potential to reveal the supramolecular architecture or 'molecular sociology' of cells and to discover the unexpected. Here, we review state-of-the-art Cryo-ET workflows, provide examples of biological applications, and discuss what is needed to realize the full potential of Cryo-ET.

Salt bridges govern the structural heterogeneity of heme protein interactions and porphyrin networks: microperoxidase-11

In this work, a proteolytic digest of cytochrome c (microperoxidase 11, MP-11) was used as a model to study the structural aspects of heme protein interactions and porphyrin networks. The MP-11 structural heterogeneity was studied as a function of the starting pH (e.g., pH 3.1-6.1) and concentration (e.g., 1-50 μM) conditions and adduct coordination. Trapped ion mobility spectrometry coupled to mass spectrometry (TIMS-MS) showed the MP-11 structural dependence of the charge state distribution and molecular ion forms with the starting pH conditions. The singly charged (e.g., [M]+, [M - 2H + NH4]+, [M - H + Na]+ and [M - H + K]+) and doubly charged (e.g., [M + H]2+, [M - H + NH4]2+, [M + Na]2+ and [M + K]2+) molecular ion forms were observed for all solvent conditions, although the structural heterogeneity (e.g., number of mobility bands) significantly varied with the pH value and ion form. The MP-11 dimer formation as a model for heme-protein protein interactions showed that dimer formation is favored toward more neutral pH and favored when assisted by salt bridges (e.g., NH4 +, Na+ and K+ vs. H+). Inspection of the dimer mobility profiles (2+ and 3+ charge states) showed a high degree of structural heterogeneity as a function of the solution pH and ion form; the observation of common mobility bands suggest that the different salt bridges can stabilize similar structural motifs. In addition, the salt bridge influence on the MP-11 dimer formations was measured using collision induced dissociation and showed a strong dependence with the type of salt bridge (i.e., a CE50 of 10.0, 11.5, 11.8 and 13.0 eV was observed for [2M + H]3+, [2M - H + NH4]3+, [2M + Na]3+ and [2M + K]3+, respectively). Measurements of the dimer equilibrium constant showed that the salt bridge interactions increase the binding strength of the dimeric species.

Smart Molecular Nanosheets for Advanced Preparation of Biological Samples in Electron Cryo-Microscopy

Transmission electron cryo-microscopy (cryoEM) of vitrified biological specimens is a powerful tool for structural biology. Current preparation of vitrified biological samples starts off with sample isolation and purification, followed by the fixation in a freestanding layer of amorphous ice. Here, we demonstrate that ultrathin (∼10 nm) smart molecular nanosheets having specific biorecognition sites embedded in a biorepulsive layer covalently bound to a mechanically stable carbon nanomembrane allow for a much simpler isolation and structural analysis. We characterize in detail the engineering of these nanosheets and their biorecognition properties employing complementary methods such as X-ray photoelectron and infrared spectroscopy, atomic force microscopy as well as surface plasmon resonance measurements. The desired functionality of the developed nanosheets is demonstrated by in situ selection of a His-tagged protein from a mixture and its subsequent structural analysis by cryoEM.

S100A4/Nonmuscle Myosin IIA/p53 Axis Contributes to Aggressive Features in Ovarian High-Grade Serous Carcinoma

S100A4 is a small calcium-binding protein that exerts its biological functions by interacting with nonmuscle myosin IIA (NMIIA) and p53. Although S100A4 promotes metastasis in several tumors, little is known about its involvement in the progression of ovarian high-grade serous carcinomas (HGSCs). Herein, we focused on functional roles of the S100A4/NMIIA/p53 axis in these tumors. In HGSC cell lines harboring mutant p53, knockdown (KD) of S100A4 reduced the expression of several epithelial-mesenchymal transition/cancer stem cell markers and the ALDH1^high population, consistent with an inhibition of stemness features. S100A4-KD also increased apoptosis, decreased cell proliferation, and accelerated cell mobility. This was accompanied by increased Snail expression, which, in turn, was likely due to loss of p53 function. In contrast, specific inhibition of NMIIA by blebbistatin induced phenotypes that-with the exception of cell proliferation and mobility-were opposite to those observed in S100A4-KD cells. In clinical samples, cytoplasmic and/or nuclear interactions between S100A4, NMIIA, and mutant p53 were observed. In addition, high expression of S100A4, but not NMIIA or p53, was a significant and independent unfavorable prognostic factor in HGSC patients. These findings suggest that, via its interaction with NMIIA and p53, overexpressed S100A4 may induce epithelial-mesenchymal transition/cancer stem cell properties in HGSC and elicit several other tumor-associated phenotypes.

Improving Identification of In-organello Protein-Protein Interactions Using an Affinity-enrichable, Isotopically Coded, and Mass Spectrometry-cleavable Chemical Crosslinker

An experimental and computational approach for identification of protein-protein interactions by ex vivo chemical crosslinking and mass spectrometry (CLMS) has been developed that takes advantage of the specific characteristics of cyanurbiotindipropionylsuccinimide (CBDPS), an affinity-tagged isotopically coded mass spectrometry (MS)-cleavable crosslinking reagent. Utilizing this reagent in combination with a crosslinker-specific data-dependent acquisition strategy based on MS2 scans, and a software pipeline designed for integrating crosslinker-specific mass spectral information led to demonstrated improvements in the application of the CLMS technique, in terms of the detection, acquisition, and identification of crosslinker-modified peptides. This approach was evaluated on intact yeast mitochondria, and the results showed that hundreds of unique protein-protein interactions could be identified on an organelle proteome-wide scale. Both known and previously unknown protein-protein interactions were identified. These interactions were assessed based on their known sub-compartmental localizations. Additionally, the identified crosslinking distance constraints are in good agreement with existing structural models of protein complexes involved in the mitochondrial electron transport chain.

A simulated annealing approach for resolution guided homogeneous cryo-electron microscopy image selection

BACKGROUND

Cryo-electron microscopy (Cryo-EM) and tomography (Cryo-ET) have emerged as important imaging techniques for studying structures of macromolecular complexes. In 3D reconstruction of large macromolecular complexes, many 2D projection images of macromolecular complex particles are usually acquired with low signal-to-noise ratio. Therefore, it is meaningful to select multiple images containing the same structure with identical orientation. The selected images are averaged to produce a higher-quality representation of the underlying structure with improved resolution. Existing approaches of selecting such images have limited accuracy and speed.

METHODS

We propose a simulated annealing-based algorithm (SA) to pick the homogeneous image set with best average. Its performance is compared with two baseline methods based on both 2D and 3D datasets. When tested on simulated and experimental 3D Cryo-ET images of Ribosome complex, SA sometimes stopped at a local optimal solution. Restarting is applied to settle this difficulty and significantly improved the performance of SA on 3D datasets.

RESULTS

Experimented on simulated and experimental 2D Cryo-EM images of Ribosome complex datasets respectively with SNR = 10 and SNR = 0.5, our method achieved better accuracy in terms of F-measure, resolution score, and time cost than two baseline methods. Additionally, SA shows its superiority when the proportion of homogeneous images decreases.

CONCLUSIONS

SA is introduced for homogeneous image selection to realize higher accuracy with faster processing speed. Experiments on both simulated and real 2D Cryo-EM and 3D Cryo-ET images demonstrated that SA achieved expressively better performance. This approach serves as an important step for improving the resolution of structural recovery of macromolecular complexes captured by Cryo-EM and Cryo-ET.

Reporting on the future of integrative structural biology ORAU workshop

Integrative and hybrid methods have the potential to bridge long-standing knowledge gaps in structural biology. These methods will have a prominent role in the future of the field as we make advances toward a complete, unified representation of biology that spans the molecular and cellular scales. The Department of Physics and Astronomy at Clemson University hosted The Future of Integrative Structural Biology workshop on April 29, 2017 and partially sponsored by partially sponsored by a program of the Oak Ridge Associated Universities (ORAU). The workshop brought experts from multiple structural biology disciplines together to discuss near-term steps toward the goal of a molecular atlas of the cell. The discussion focused on the types of structural data that should be represented, how this data should be represented, and how the time domain might be incorporated into such an atlas. The consensus was that an explorable, map-like Virtual Cell, containing both spatial and temporal data bridging the atomic and cellular length scales obtained by multiple experimental methods, represents the best path toward a complete atlas of the cell.

Direct observation of unstained biological samples in water using newly developed impedance scanning electron microscopy

Nanometre-scale observation of specimens in water is indispensable in several scientific fields, such as biology, chemistry, materials science and nanotechnology. Scanning electron microscopy (SEM) obtains high-resolution images of biological samples under high vacuum conditions but requires specific sample-preparation protocols. Observations of unstained biological samples in water require more convenient and less invasive methods. Herein, we have developed a new type of impedance microscopy, namely impedance SEM (IP-SEM), which allows the imaging and sub-micrometer scale examination of various specimens in water. By varying the frequency of the input signal, the proposed system can detect the impedance properties of the sample’s composition at sub-micrometer scale resolution. Besides examining various unstained biological specimens and material samples in water. Furthermore, the proposed system can be used for diverse liquid samples across a broad range of scientific fields, such as nanoparticles, nanotubes and organic and catalytic materials.

Archiving and disseminating integrative structure models

Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev ( https://pdb-dev.wwpdb.org ) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format.

In Situ Imaging and Structure Determination of Bacterial Toxin Delivery Systems Using Electron Cryotomography

Determining the three-dimensional structure of biomacromolecules at high resolution in their native cellular environment is a major challenge for structural biology. Toward this end, electron cryotomography (ECT) allows large bio-macromolecular assemblies to be imaged directly in their hydrated physiological milieu to ~4 nm resolution. Combining ECT with other techniques like fluorescent imaging, immunogold labeling, and genetic manipulation has allowed the in situ investigation of complex biological processes at macromolecular resolution. Furthermore, the advent of cryogenic focused ion beam (FIB) milling has extended the domain of ECT to include regions even deep within thick eukaryotic cells. Anticipating two audiences (scientists who just want to understand the potential and general workflow involved and scientists who are learning how to do the work themselves), here we present both a broad overview of this kind of work and a step-by-step example protocol for ECT and subtomogram averaging using the Legionella pneumophila Dot/Icm type IV secretion system (T4SS) as a case study. While the general workflow is presented in step-by-step detail, we refer to online tutorials, user's manuals, and other training materials for the essential background understanding needed to perform each step.

Modeling Biological Complexes Using Integrative Modeling Platform

Integrative structure modeling provides 3D models of macromolecular systems that are based on information from multiple types of experiments, physical principles, statistical inferences, and prior structural models. Here, we provide a hands-on realistic example of integrative structure modeling of the quaternary structure of the actin, tropomyosin, and gelsolin protein assembly based on electron microscopy, solution X-ray scattering, and chemical crosslinking data for the complex as well as excluded volume, sequence connectivity, and rigid atomic X-ray structures of the individual subunits. We follow the general four-stage process for integrative modeling, including gathering the input information, converting the input information into a representation of the system and a scoring function, sampling alternative model configurations guided by the scoring function, and analyzing the results. The computational aspects of this approach are implemented in our open-source Integrative Modeling Platform (IMP), a comprehensive and extensible software package for integrative modeling ( https://integrativemodeling.org ). In particular, we rely on the Python Modeling Interface (PMI) module of IMP that provides facile mixing and matching of macromolecular representations, restraints based on different types of information, sampling algorithms, and analysis including validations of the input data and output models. Finally, we also outline how to deposit an integrative structure and corresponding experimental data into PDB-Dev, the nascent worldwide Protein Data Bank (wwPDB) resource for archiving and disseminating integrative structures ( https://pdb-dev.wwpdb.org ). The example application provides a starting point for a user interested in using IMP for integrative modeling of other biomolecular systems.

Consistent prediction of GO protein localization

The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automated GO-CC annotation of proteins suffer from the inconsistency of individual GO-CC term predictions. Here, we present FGGA-CC⁺, a class of hierarchical graph-based classifiers for the consistent GO-CC annotation of protein coding genes at the subcellular compartment or macromolecular complex levels. Aiming to boost the accuracy of GO-CC predictions, we make use of the protein localization knowledge in the GO-Biological Process (GO-BP) annotations to boost the accuracy of GO-CC prediction. As a result, FGGA-CC⁺ classifiers are built from annotation data in both the GO-CC and GO-BP ontologies. Due to their graph-based design, FGGA-CC⁺ classifiers are fully interpretable and their predictions amenable to expert analysis. Promising results on protein annotation data from five model organisms were obtained. Additionally, successful validation results in the annotation of a challenging subset of tandem duplicated genes in the tomato non-model organism were accomplished. Overall, these results suggest that FGGA-CC⁺ classifiers can indeed be useful for satisfying the huge demand of GO-CC annotation arising from ubiquitous high throughout sequencing and proteomic projects.

Phloem Sap Sampling from Brassica napus for 3D-PAGE of Protein and Ribonucleoprotein Complexes

Sampling the phloem of higher plants is often laborious and significantly dependent on the plant species. However, proteome studies under denaturing conditions could be achieved in different plant species. Native protein:protein and protein:nucleic acid complexes from phloem samples have as yet scarcely been analyzed, although they might play important roles in maintenance of this specialized compartment or in long-distance signaling. Large molecular assemblies can be isolated using a blue native gel electrophoresis (BN-PAGE). Their protein components can be separated by a subsequent sodium dodecyl sulfate PAGE (SDS-PAGE). However, proteins with similar molecular weights co-migrate, what can hinder protein identification by mass spectrometry. Combining BN-PAGE with two different denaturing gel electrophoresis steps, namely Tris-Tricine-urea and SDS-PAGE, enables the additional separation of proteins according to their hydrophilicity/hydrophobicity and thus increases resolution and the success of protein identification. It even allows distinguishing proteins that only differ in their posttranslational modifications. In addition, blue native northern blotting can be applied to identify the RNA components in macromolecular complexes. We show that our protocol is suitable to unravel the protein and RNA components of native protein:protein and ribonucleoprotein (RNP) complexes occurring in phloem samples. Combining a blue native PAGE with two different denaturing PAGE steps can help to separate different kinds of large protein complexes, and also enables an increased identification rate of their components by mass spectrometry. Furthermore, the protocol is robust enough to simultaneously detect potentially bound nucleic acids within single protein complexes.

Quantitative Determination of Interacting Protein Surfaces in Prokaryotes and Eukaryotes by Using In-Cell NMR Spectroscopy

This paper describes three protocols for identifying interacting surfaces on ¹⁵N-labeled target proteins of known structure by using in-cell NMR spectroscopy. The first protocol describes how to identify protein quinary structure interaction surfaces in prokaryotes by using cross-relaxation-induced polarization transfer, CRIPT, based in-cell NMR. The second protocol describes how to introduce labeled protein into eukaryotic (HeLa) cells via electroporation for CRIPT-based in-cell studies. The third protocol describes how to quantitatively map protein interacting surfaces by utilizing singular value decomposition, SVD, analysis of STructural INTeractions by in-cell NMR, STINT-NMR, data.

Integrative structure modeling with the Integrative Modeling Platform

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.

Nanoscale observation of the natural structure of milk-fat globules and casein micelles in the liquid condition using a scanning electron assisted dielectric microscopy

Recently, aqueous nanoparticles have been used in drug-delivery systems for new type medicines. In particular, milk-casein micelles have been used as drug nanocarriers for targeting cancer cells. Therefore, nanostructure observation of particles and micelles in their native liquid condition is indispensable for analysing their function and mechanisms. However, traditional optical and scanning electron microscopy have difficulty observing the nanostructures of aqueous micelles. Recently, we developed a novel imaging technique called scanning electron-assisted dielectric microscopy (SE-ADM) that enables observation of various biological specimens in water with very little radiation damage and high-contrast imaging without staining or fixation at an 8-nm spatial resolution. In this study, for the first time, we show that the SE-ADM system is capable of high-resolution observation of whole-milk specimens in their natural state. Moreover, we successfully observe the casein micelles and milk-fat globules in an intact liquid condition. Our SE-ADM system can be applied to various biological particles and micelles in a native liquid state.

Real-time shape approximation and fingerprinting of single proteins using a nanopore

Established methods for characterizing proteins typically require physical or chemical modification steps or cannot be used to examine individual molecules in solution. Ionic current measurements through electrolyte-filled nanopores can characterize single native proteins in an aqueous environment, but currently offer only limited capabilities. Here we show that the zeptolitre sensing volume of bilayer-coated solid-state nanopores can be used to determine the approximate shape, volume, charge, rotational diffusion coefficient and dipole moment of individual proteins. To do this, we developed a theory for the quantitative understanding of modulations in ionic current that arise from the rotational dynamics of single proteins as they move through the electric field inside the nanopore. The approach allows us to measure the five parameters simultaneously, and we show that they can be used to identify, characterize and quantify proteins and protein complexes with potential implications for structural biology, proteomics, biomarker detection and routine protein analysis.

Molecular Imprinting of Macromolecules for Sensor Applications

Molecular recognition has an important role in numerous living systems. One of the most important molecular recognition methods is molecular imprinting, which allows host compounds to recognize and detect several molecules rapidly, sensitively and selectively. Compared to natural systems, molecular imprinting methods have some important features such as low cost, robustness, high recognition ability and long term durability which allows molecularly imprinted polymers to be used in various biotechnological applications, such as chromatography, drug delivery, nanotechnology, and sensor technology. Sensors are important tools because of their ability to figure out a potentially large number of analytical difficulties in various areas with different macromolecular targets. Proteins, enzymes, nucleic acids, antibodies, viruses and cells are defined as macromolecules that have wide range of functions are very important. Thus, macromolecules detection has gained great attention in concerning the improvement in most of the studies. The applications of macromolecule imprinted sensors will have a spacious exploration according to the low cost, high specificity and stability. In this review, macromolecules for molecularly imprinted sensor applications are structured according to the definition of molecular imprinting methods, developments in macromolecular imprinting methods, macromolecular imprinted sensors, and conclusions and future perspectives. This chapter follows the latter strategies and focuses on the applications of macromolecular imprinted sensors. This allows discussion on how sensor strategy is brought to solve the macromolecules imprinting.

Hematite-Based Photoelectrode Materials for Photoelectrocatalytic Inhibition of Alzheimer's β-Amyloid Self-Assembly

A visible light-active, hematite-based photoelectrode platform for suppressing β-amyloid (Aβ) self-assembly in vitro is reported. Upon illumination of a light-emitting diode with an anodic bias, the hematite photoanode generates reactive radical species, such as superoxide ions and hydroxyl radicals, via photoelectrocatalytic process. According to our analyses, the hematite photoanode exhibited a strong inhibitory effect on Aβ aggregation under visible light illumination and anodic bias. We found that hole-derived radicals played a significant role of oxidizing Aβ peptides, which effectively blocked further aggregation. The efficacy of photoelectrocatalytic inhibition on Aβ aggregation was enhanced by introducing cobalt phosphate (Co-Pi) as a co-catalyst on the hematite photoanode, which facilitated the separation of electron-hole pairs. We verified that both bare and Co-Pi@hematite photoanodes are biocompatible and effective in reducing Aβ aggregation-induced cytotoxicity.

Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps

A growing array of experimental techniques allows us to characterize the three-dimensional structure of large biological assemblies at increasingly higher resolution. In addition to X-ray crystallography and nuclear magnetic resonance in solution, new structure determination methods such cryo-electron microscopy (cryo-EM), crosslinking/mass spectrometry and solid-state NMR have emerged. Often it is not sufficient to use a single experimental method, but complementary data need to be collected by using multiple techniques. The integration of all datasets can only be achieved by computational means. This article describes Inferential structure determination, a Bayesian approach to integrative modeling of biomolecular complexes with hybrid structural data. I will introduce probabilistic models for cryo-EM maps and outline Markov chain Monte Carlo algorithms for sampling model structures from the posterior distribution. I will focus on rigid and flexible modeling with cryo-EM data and discuss some of the computational challenges of Bayesian inference in the context of biomolecular modeling.

A Two-Phase Improved Correlation Method for Automatic Particle Selection in Cryo-EM

Particle selection from cryo-electron microscopy (Cryo-EM) images is very important for high-resolution reconstruction of macromolecular structure. The methods of particle selection can be roughly grouped into two classes, template-matching methods and feature-based methods. In general, template-matching methods usually generate better results than feature-based methods. However, the accuracy of template-matching methods is restricted by the noise and low contrast of Cryo-EM images. Moreover, the processing speed of template-matching methods, restricted by the random orientation of particles, further limits their practical applications. In this paper, combining the advantages of feature-based methods and template-matching methods, we present a two-phase improved correlation method for automatic, fast particle selection. In Phase I, we generate a preliminary particle set using rotation-invariant features of particles. In Phase II, we filter the preliminary particle set using a correlation method to reduce the interference of the high noise background and improve the precision of particle selection. We apply several optimization strategies, including a modified adaboost algorithm, Divide and Conquer technique, cascade strategy and graphics processing unit parallel technique, to improve feature recognition ability and reduce processing time. In addition, we developed two correlation score functions for different correlation situations. Experimental results on the benchmark of Cryo-EM images show that our method can improve the accuracy and processing speed of particle selection significantly.

In Situ Environmental TEM in Imaging Gas and Liquid Phase Chemical Reactions for Materials Research

Gas and liquid phase chemical reactions cover a broad range of research areas in materials science and engineering, including the synthesis of nanomaterials and application of nanomaterials, for example, in the areas of sensing, energy storage and conversion, catalysis, and bio-related applications. Environmental transmission electron microscopy (ETEM) provides a unique opportunity for monitoring gas and liquid phase reactions because it enables the observation of those reactions at the ultra-high spatial resolution, which is not achievable through other techniques. Here, the fundamental science and technology developments of gas and liquid phase TEM that facilitate the mechanistic study of the gas and liquid phase chemical reactions are discussed. Combined with other characterization tools integrated in TEM, unprecedented material behaviors and reaction mechanisms are observed through the use of the in situ gas and liquid phase TEM. These observations and also the recent applications in this emerging area are described. The current challenges in the imaging process are also discussed, including the imaging speed, imaging resolution, and data management.

Towards the Analysis of High Molecular Weight Proteins and Protein complexes using TIMS-MS

In the present work, we demonstrate the potential and versatility of TIMS for the analysis of proteins, DNA-protein complexes and protein-protein complexes in their native and denatured states. In addition, we show that accurate CCS measurement are possible and in good agreement with previously reported CCS values using other IMS analyzers (<5% difference). The main challenges for the analysis of high mass proteins and protein complexes in the mobility and m/z domain are described. That is, the analysis of high molecular weight systems in their native state may require the use of higher electric fields or a compromise in the TIMS mobility resolution by reducing the bath gas velocity in order to effectively trap at lower electric fields. This is the first report of CCS measurements of high molecular weight biomolecules and biomolecular complexes (~ 150 kDa) using TIMS-MS.

Tuning water transport through nanochannels by changing the direction of an external electric field

The transport properties of water through a nanochannel influenced by the direction of an external electric field has been investigated by using molecular dynamics simulations. Water molecules flow unidirectionally across the nanochannel under a uniform external electric field without an osmotic pressure. It is found that the direction of the external field plays an important role in the interactions and dipole orientations of water molecules in the nanochannel, accordingly changing the net water flux dramatically. Most importantly, a critical angle (θC) between the external field and the nanochannel axis is found. The average net water flux increases as θ increases for θ≤θC but decreases sharply to a near-zero value for a further increase of θ. The maximum value of the average net water flux is 7.33 times as high as the value when the electric field is along the nanochannel axis. Our findings are of great practical importance for nanomolecular engineering, which provide a possible strategy for designing novel controllable water nanopumps.

Nanoscale imaging of untreated mammalian cells in a medium with low radiation damage using scanning electron-assisted dielectric microscopy

Imaging of untreated living cells in a medium at a nanometre-scale resolution under physiological conditions is a significant challenge. Scanning electron microscopy (SEM) is widely used to observe cells in various atmospheric holders or special equipment. However, untreated biological specimens in aqueous solution generally incur heavy radiation damage from the direct electron beam (EB); and these images exhibit very poor contrast. Therefore, a new method for generating high-contrast images of living cells under physiological conditions without radiation damage has been strongly desired. Here, we demonstrate the first nanoscale observation of living cultured mammalian cells using our newly developed scanning-electron assisted dielectric microscopy (SE-ADM) method with a culture dish holder. Using the difference in relative permittivity between water and specimens, our SE-ADM system aids in the visualisation of untreated biological samples in aqueous solution. In addition, specimens incurred only a low level of radiation damage because the tungsten (W)-coated silicon nitride (SiN) film absorbs irradiated electrons. Untreated cells and organelles are clearly visible in high-contrast and high-resolution images without staining and fixation. Furthermore, our method enables the detection of changes in organelle structures within cells via time-lapse imaging with minimal radiation damage.

Characterizing monoclonal antibody structure by carboxyl group footprinting

Structural characterization of proteins and their antigen complexes is essential to the development of new biologic-based medicines. Amino acid-specific covalent labeling (CL) is well suited to probe such structures, especially for cases that are difficult to examine by alternative means due to size, complexity, or instability. We present here a detailed account of carboxyl group labeling (with glycine ethyl ester (GEE) tagging) applied to a glycosylated monoclonal antibody therapeutic (mAb). The experiments were optimized to preserve the structural integrity of the mAb, and experimental conditions were varied and replicated to establish the reproducibility of the technique. Homology-based models were generated and used to compare the solvent accessibility of the labeled residues, which include aspartic acid (D), glutamic acid (E), and the C-terminus (i.e., the target probes), with the experimental data in order to understand the accuracy of the approach. Data from the mAb were compared to reactivity measures of several model peptides to explain observed variations in reactivity. Attenuation of reactivity in otherwise solvent accessible probes is documented as arising from the effects of positive charge or bond formation between adjacent amine and carboxyl groups, the latter accompanied by observed water loss. A comparison of results with previously published data by Deperalta et al using hydroxyl radical footprinting showed that 55% (32/58) of target residues were GEE labeled in this study whereas the previous study reported 21% of the targets were labeled. Although the number of target residues in GEE labeling is fewer, the two approaches provide complementary information. The results highlight advantages of this approach, such as the ease of use at the bench top, the linearity of the dose response plots at high levels of labeling, reproducibility of replicate experiments (<2% variation in modification extent), the similar reactivity of the three target probes, and significant correlation of reactivity and solvent accessible surface area.

Systems Proteomics View of the Endogenous Human Claudin Protein Family

Claudins are the major transmembrane protein components of tight junctions in human endothelia and epithelia. Tissue-specific expression of claudin members suggests that this protein family is not only essential for sustaining the role of tight junctions in cell permeability control but also vital in organizing cell contact signaling by protein-protein interactions. How this protein family is collectively processed and regulated is key to understanding the role of junctional proteins in preserving cell identity and tissue integrity. The focus of this review is to first provide a brief overview of the functional context, on the basis of the extensive body of claudin biology research that has been thoroughly reviewed, for endogenous human claudin members and then ascertain existing and future proteomics techniques that may be applicable to systematically characterizing the chemical forms and interacting protein partners of this protein family in human. The ability to elucidate claudin-based signaling networks may provide new insight into cell development and differentiation programs that are crucial to tissue stability and manipulation.

Applying mass spectrometry to study non-covalent biomolecule complexes

Non-covalent interactions are essential for the structural organization of biomacromolecules and play an important role in molecular recognition processes, such as the interactions between proteins, glycans, lipids, DNA, and RNA. Mass spectrometry (MS) is a powerful tool for studying of non-covalent interactions, due to the low sample consumption, high sensitivity, and label-free nature. Nowadays, native-ESI MS is heavily used in studies of non-covalent interactions and to understand the architecture of biomolecular complexes. However, MALDI-MS is also becoming increasingly useful. It is challenging to detect the intact complex without fragmentation when analyzing non-covalent interactions with MALDI-MS. There are two methodological approaches to do so. In the first approach, different experimental and instrumental parameters are fine-tuned in order to find conditions under which the complex is stable, such as applying non-acidic matrices and collecting first-shot spectra. In the second approach, the interacting species are "artificially" stabilized by chemical crosslinking. Both approaches are capable of studying non-covalently bound biomolecules even in quite challenging systems, such as membrane protein complexes. Herein, we review and compare native-ESI and MALDI MS for the study of non-covalent interactions.