Cryo electron microscopy to determine the structure of macromolecular complexes

Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN

Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformation of each macromolecule. However, the image's signal-to-noise ratio is low, and some form of classification is usually performed at the image processing level to allow structural modeling. Classical classification methods imply the existence of a discrete number of structural conformations. However, new heterogeneity algorithms introduce a novel reconstruction paradigm, where every state is represented by a lower number of particles, potentially just one, allowing the estimation of conformational landscapes representing the different structural states a biomolecule explores. In this work, we present a novel deep learning-based method called HetSIREN. HetSIREN can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. The unique characteristics that set HetSIREN apart start with the definition of the approach as a real space-based only method, a fact that allows spatially focused analysis, but also the introduction of a novel network architecture specifically designed to make use of meta-sinusoidal activations, with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the pose parameters of the images at the same time that it conditions the network with prior information/constraints on the maps, such as Total Variation andL 1 denoising, ultimately yielding cleaner volumes with high-quality structural features. Finally, but very importantly, HetSIREN addresses one of the most confusing issues in heterogeneity analysis, as it is the fact that real structural heterogeneity estimation is entangled with pose estimation (and to a lesser extent with CTF estimation), in this way, HetSIREN introduces a novel encoding architecture able to decouple pose and CTF information from the conformational landscape, resulting in more accurate and interpretable conformational latent spaces. We present results on computer-simulated data, public data from EMPIAR, and data from experimental systems currently being studied in our laboratories. An important finding is the sensitivity of the structure and dynamics of the SARS-CoV-2 Spike protein on the storage temperature.

Toward Reliable Conformational Energies of Amino Acids and Dipeptides─The DipCONFS Benchmark and DipCONL Datasets

Simulating peptides and proteins is becoming increasingly important, leading to a growing need for efficient computational methods. These are typically semiempirical quantum mechanical (SQM) methods, force fields (FFs), or machine-learned interatomic potentials (MLIPs), all of which require a large amount of accurate data for robust training and evaluation. To assess potential reference methods and complement the available data, we introduce two sets, DipCONFL and DipCONFS, which cover large parts of the conformational space of 17 amino acids and their 289 possible dipeptides in aqueous solution. The conformers were selected from the exhaustive PeptideCS dataset by Andris et al. [ J. Phys. Chem. B 2022, 126, 5949-5958]. The structures, originally generated with GFN2-xTB, were reoptimized using the accurate r2SCAN-3c density functional theory (DFT) composite method including the implicit CPCM water solvation model. The DipCONFS benchmark set contains 918 conformers and is one of the largest sets with highly accurate coupled cluster conformational energies so far. It is employed to evaluate various DFT and wave function theory (WFT) methods, especially regarding whether they are accurate enough to be used as reliable reference methods for larger datasets intended for training and testing more approximated SQM, FF, and MLIP methods. The results reveal that the originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies are not sufficiently accurate. Among the DFT methods tested as an alternative reference level, the revDSD-PBEP86-D4 double hybrid performs best with a mean absolute error (MAD) of 0.2 kcal mol-1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient r2SCAN-3c composite method also shows excellent results, with an MAD of 0.3 kcal mol-1, similar to the best-tested hybrid ωB97M-D4. With these findings, we compiled the large DipCONFL set, which includes over 29,000 realistic conformers in solution with reasonably accurate r2SCAN-3c reference conformational energies, gradients, and further properties potentially relevant for training MLIP methods. This set, also in comparison to DipCONFS, is used to assess the performance of various SQM, FF, and MLIP methods robustly and can complement training sets for those.

Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling

Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairsN r that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints,N r / N ≲ 0.08 , where N is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo.

A Visual Environment for Data Driven Protein Modeling and Validation

In structural biology, validation and verification of new atomic models are crucial and necessary steps which limit the production of reliable molecular models for publications and databases. An atomic model is the result of meticulous modeling and matching and is evaluated using a variety of metrics that provide clues to improve and refine the model so it fits our understanding of molecules and physical constraints. In cryo electron microscopy (cryo-EM) the validation is also part of an iterative modeling process in which there is a need to judge the quality of the model during the creation phase. A shortcoming is that the process and results of the validation are rarely communicated using visual metaphors. This work presents a visual framework for molecular validation. The framework was developed in close collaboration with domain experts in a participatory design process. Its core is a novel visual representation based on 2D heatmaps that shows all available validation metrics in a linear fashion, presenting a global overview of the atomic model and provide domain experts with interactive analysis tools. Additional information stemming from the underlying data, such as a variety of local quality measures, is used to guide the user's attention toward regions of higher relevance. Linked with the heatmap is a three-dimensional molecular visualization providing the spatial context of the structures and chosen metrics. Additional views of statistical properties of the structure are included in the visual framework. We demonstrate the utility of the framework and its visual guidance with examples from cryo-EM.

Mosaic quadrivalent influenza vaccine single nanoparticle characterization

Recent work by our laboratory and others indicates that co-display of multiple antigens on protein-based nanoparticles may be key to induce cross-reactive antibodies that provide broad protection against disease. To reach the ultimate goal of a universal vaccine for seasonal influenza, a mosaic influenza nanoparticle vaccine (FluMos-v1) was developed for clinical trial (NCT04896086). FluMos-v1 is unique in that it is designed to co-display four recently circulating haemagglutinin (HA) strains; however, current vaccine analysis techniques are limited to nanoparticle population analysis, thus, are unable to determine the valency of an individual nanoparticle. For the first time, we demonstrate by total internal reflection fluorescence microscopy and supportive physical-chemical methods that the co-display of four antigens is indeed achieved in single nanoparticles. Additionally, we have determined percentages of multivalent (mosaic) nanoparticles with four, three, or two HA proteins. The integrated imaging and physicochemical methods we have developed for single nanoparticle multivalency will serve to further understand immunogenicity data from our current FluMos-v1 clinical trial.

Simcryocluster: a semantic similarity clustering method of cryo-EM images by adopting contrastive learning

BACKGROUND

Cryo-electron microscopy (Cryo-EM) plays an increasingly important role in the determination of the three-dimensional (3D) structure of macromolecules. In order to achieve 3D reconstruction results close to atomic resolution, 2D single-particle image classification is not only conducive to single-particle selection, but also a key step that affects 3D reconstruction. The main task is to cluster and align 2D single-grain images into non-heterogeneous groups to obtain sharper single-grain images by averaging calculations. The main difficulties are that the cryo-EM single-particle image has a low signal-to-noise ratio (SNR), cannot manually label the data, and the projection direction is random and the distribution is unknown. Therefore, in the low SNR scenario, how to obtain the characteristic information of the effective particles, improve the clustering accuracy, and thus improve the reconstruction accuracy, is a key problem in the 2D image analysis of single particles of cryo-EM.

RESULTS

Aiming at the above problems, we propose a learnable deep clustering method and a fast alignment weighted averaging method based on frequency domain space to effectively improve the class averaging results and improve the reconstruction accuracy. In particular, it is very prominent in the feature extraction and dimensionality reduction module. Compared with the classification method based on Bayesian and great likelihood, a large amount of single particle data is required to estimate the relative angle orientation of macromolecular single particles in the 3D structure, and we propose that the clustering method shows good results.

CONCLUSIONS

SimcryoCluster can use the contrastive learning method to perform well in the unlabeled high-noise cryo-EM single particle image classification task, making it an important tool for cryo-EM protein structure determination.

PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN

Revealing the tertiary structure of proteins holds huge significance as it unveils their vital properties and functions. These intricate three-dimensional configurations comprise diverse interactions including ionic, hydrophobic, and disulfide forces. In certain instances, these structures exhibit missing regions, necessitating the reconstruction of specific segments, thereby resulting in challenges in protein design, which encompasses loop modeling, circular permutation, and interface prediction. To address this problem, we present two pioneering models: pix2pix generative adversarial network (GAN) and PLM-GAN. The pix2pix GAN model is adept at generating and inpainting distance matrices of protein structures, whereas the PLM-GAN model incorporates residual blocks into the U-Net network of the GAN, building upon the foundation of the pix2pix GAN model. To bolster the models' performance, we introduce a novel loss function named the "missing to real regions loss" (LMTR) within the GAN framework. Additionally, we introduce a distinctive approach of pairing two different distance matrices: one representing the native protein structure and the other representing the same structure with a missing region that undergoes changes in each successive epoch. Moreover, we extend the reconstruction of missing regions, encompassing up to 30 amino acids and increase the protein length by 128 amino acids. The evaluation of our pix2pix GAN and PLM-GAN models on a random selection of natural proteins (4ZCB, 3FJB, and 2REZ) demonstrated promising experimental results. Our models constitute significant contributions to addressing intricate challenges in protein structure design. These contributions hold immense potential to propel advancements in protein-protein interactions, drug design, and further innovations in protein engineering. Data, code, trained models, examples, and measurements are available on https://github.com/mena01/PLM-GAN-A-Large-Scale-Protein-Loop-Modeling-Using-pix2pix-GAN_.

Coenzyme-binding pathway on glutamate dehydrogenase suggested from multiple-binding sites visualized by cryo-electron microscopy

The structure of hexameric glutamate dehydrogenase (GDH) in the presence of the coenzyme nicotinamide adenine dinucleotide phosphate (NADP) was visualized using cryogenic transmission electron microscopy to investigate the ligand-binding pathways to the active site of the enzyme. Each subunit of GDH comprises one hexamer-forming core domain and one nucleotide-binding domain (NAD domain), which spontaneously opens and closes the active-site cleft situated between the two domains. In the presence of NADP, the potential map of GDH hexamer, assuming D3 symmetry, was determined at a resolution of 2.4 Å, but the NAD domain was blurred due to the conformational variety. After focused classification with respect to the NAD domain, the potential maps interpreted as NADP molecules appeared at five different sites in the active-site cleft. The subunits associated with NADP molecules were close to one of the four metastable conformations in the unliganded state. Three of the five binding sites suggested a pathway of NADP molecules to approach the active-site cleft for initiating the enzymatic reaction. The other two binding modes may rarely appear in the presence of glutamate, as demonstrated by the reaction kinetics. Based on the visualized structures and the results from the enzymatic kinetics, we discussed the binding modes of NADP to GDH in the absence and presence of glutamate.

The cryo-EM 3D image reconstruction of isolated Lethocerus indicus Z-discs

The Z-disk of striated muscle defines the ends of the sarcomere, which repeats many times within the muscle fiber. Here we report application of cryoelectron tomography and subtomogram averaging to Z-disks isolated from the flight muscles of the large waterbug Lethocerus indicus. We use high salt solutions to remove the myosin containing filaments and use gelsolin to remove the actin filaments of the A- and I-bands leaving only the thin filaments within the Z-disk which were then frozen for cryoelectron microscopy. The Lethocerus Z-disk structure is similar in many ways to the previously studied Z-disk of the honeybee Apis mellifera. At the corners of the unit cell are positioned trimers of paired antiparallel F-actins defining a large solvent channel, whereas at the trigonal positions are positioned F-actin trimers converging slowly towards their (+) ends defining a small solvent channel through the Z-disk. These near parallel F-actins terminate at different Z-heights within the Z-disk. The two types of solvent channel in Lethocerus are similar in size compared to those of Apis which are very different in size. Two types of α-actinin crosslinks were observed between oppositely oriented actin filaments. In one of these, the α-actinin long axis is almost parallel to the F-actins it crosslinks. In the other, the α-actinins are at a small but distinctive angle with respect to the crosslinked actin filaments. The utility of isolated Z-disks for structure determination is discussed.

Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels

Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.

ZART: A novel multiresolution reconstruction algorithm with motion-blur correction for single particle analysis

One of the main purposes of CryoEM Single Particle Analysis is to reconstruct the three-dimensional structure of a macromolecule thanks to the acquisition of many particle images representing different poses of the sample. By estimating the orientation of each projected particle, it is possible to recover the underlying 3D volume by multiple 3D reconstruction methods, usually working either in Fourier or in real space. However, the reconstruction from the projected images works under the assumption that all particles in the dataset correspond to the same conformation of the macromolecule. Although this requisite holds for some macromolecules, it is not true for flexible specimens, leading to motion-induced artefacts in the reconstructed CryoEM maps. In this work, we introduce a new Algebraic Reconstruction Technique called ZART, which is able to include continuous flexibility information during the reconstruction process to improve local resolution and reduce motion blurring. The conformational changes are modelled through Zernike3D polynomials. Our implementation allows for a multiresolution description of the macromolecule adapting itself to the local resolution of the reconstructed map. In addition, ZART has also proven to be a useful algorithm in cases where flexibility is not so dominant, as it improves the overall aspect of the reconstructed maps by improving their local and global resolution.

Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials

The new developments in Cryo-EM Single Particle Analysis are helping us to understand how the macromolecular structure and function meet to drive biological processes. By capturing many states at the particle level, it is possible to address how macromolecules explore different conformations, information that is classically extracted through 3D classification. However, the limitations of classical approaches prevent us from fully understanding the complete conformational landscape due to the reduced number of discrete states accurately reconstructed. To characterize the whole structural spectrum of a macromolecule, we propose an extension of our Zernike3D approach, able to extract per-image continuous flexibility information directly from a particle dataset. Also, our method can be seamlessly applied to images, maps or atomic models, opening integrative possibilities. Furthermore, we introduce the ZART reconstruction algorithm, which considers the Zernike3D deformation fields to revert particle conformational changes during the reconstruction process, thus minimizing the blurring induced by molecular motions.

Correct Identification of the Core-Shell Structure of Cell Membrane-Coated Polymeric Nanoparticles

Transmission electron microscopy (TEM) observations of negatively stained cell membrane (CM)-coated polymeric nanoparticles (NPs) reveal a characteristic core-shell structure. However, negative staining agents can create artifacts that complicate the determination of the actual NP structure. Herein, it is demonstrated with various bare polymeric core NPs, such as poly(lactic-co-glycolic acid) (PLGA), poly(ethylene glycol) methyl ether-block-PLGA, and poly(caprolactone), that certain observed core-shell structures are actually artifacts caused by the staining process. To address this issue, fluorescence quenching was applied to quantify the proportion of fully coated NPs and statistical TEM analysis was used to identify and differentiate whether the observed core-shell structures of CM-coated PLGA (CM-PLGA) NPs are due to artifacts or to the CM coating. Integrated shells in TEM images of negatively stained CM-PLGA NPs are identified as artifacts. The present results challenge current understanding of the structure of CM-coated polymeric NPs and encourage researchers to use the proposed characterization approach to avoid misinterpretations.

Recent advances in RNA structurome

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

Approximating deformation fields for the analysis of continuous heterogeneity of biological macromolecules by 3D Zernike polynomials

Structural biology has evolved greatly due to the advances introduced in fields like electron microscopy. This image-capturing technique, combined with improved algorithms and current data processing software, allows the recovery of different conformational states of a macromolecule, opening new possibilities for the study of its flexibility and dynamic events. However, the ensemble analysis of these different conformations, and in particular their placement into a common variable space in which the differences and similarities can be easily recognized, is not an easy matter. To simplify the analysis of continuous heterogeneity data, this work proposes a new automatic algorithm that relies on a mathematical basis defined over the sphere to estimate the deformation fields describing conformational transitions among different structures. Thanks to the approximation of these deformation fields, it is possible to describe the forces acting on the molecules due to the presence of different motions. It is also possible to represent and compare several structures in a low-dimensional mapping, which summarizes the structural characteristics of different states. All these analyses are integrated into a common framework, providing the user with the ability to combine them seamlessly. In addition, this new approach is a significant step forward compared with principal component analysis and normal mode analysis of cryo-electron microscopy maps, avoiding the need to select components or modes and producing localized analysis.

Biophysical applications in structural and molecular biology

The main objective of structural biology is to model proteins and other biological macromolecules and link the structural information to function and dynamics. The biological functions of protein molecules and nucleic acids are inherently dependent on their conformational dynamics. Imaging of individual molecules and their dynamic characteristics is an ample source of knowledge that brings new insights about mechanisms of action. The atomic-resolution structural information on most of the biomolecules has been solved by biophysical techniques; either by X-ray diffraction in single crystals or by nuclear magnetic resonance (NMR) spectroscopy in solution. Cryo-electron microscopy (cryo-EM) is emerging as a new tool for analysis of a larger macromolecule that couldn't be solved by X-ray crystallography or NMR. Now a day's low-resolution Cryo-EM is used in combination with either X-ray crystallography or NMR. The present review intends to provide updated information on applications like X-ray crystallography, cryo-EM and NMR which can be used independently and/or together in solving structures of biological macromolecules for our full comprehension of their biological mechanisms.

The Two-Step Clustering Approach for Metastable States Learning

Understanding the energy landscape and the conformational dynamics is crucial for studying many biological or chemical processes, such as protein-protein interaction and RNA folding. Molecular Dynamics (MD) simulations have been a major source of dynamic structure. Although many methods were proposed for learning metastable states from MD data, some key problems are still in need of further investigation. Here, we give a brief review on recent progresses in this field, with an emphasis on some popular methods belonging to a two-step clustering framework, and hope to draw more researchers to contribute to this area.

Advanced Methods for Studying Structure and Interactions of Macrolide Antibiotics

Macrolide antibiotics are macrocyclic compounds that are clinically used and prescribed for the treatment of upper and lower respiratory tract infections. They inhibit the synthesis of bacterial proteins by reversible binding to the 23S rRNA at or near the peptidyl transferase center. However, their excellent antibacterial profile was largely compromised by the emergence of bacterial resistance. Today, fighting resistance to antibiotics is one of the greatest challenges in medicinal chemistry. Considering various physicochemical properties of macrolides, understanding their structure and interactions with macromolecular targets is crucial for the design of new antibiotics efficient against resistant pathogens. The solid-state structures of some macrolide-ribosome complexes have recently been solved, throwing new light on the macrolide binding mechanisms. On the other hand, a combination of NMR spectroscopy and molecular modeling calculations can be applied to study free and bound conformations in solution. In this article, a description of advanced physicochemical methods for elucidating the structure and interactions of macrolide antibiotics in solid state and solution will be provided, and their principal advantages and drawbacks will be discussed.

WITHDRAWN: Structural studies of vitrified biological proteins and macromolecules - A review on the microimaging aspects of cryo-electron microscopy

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Using NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD Protein Structure Refinement

Cryo-EM has become one of the prime methods for protein structure elucidation, frequently yielding density maps with near-atomic or medium resolution. If protein structures cannot be deduced unambiguously from the density maps, computational structure refinement tools are needed to generate protein structural models. We have previously developed an iterative Rosetta-MDFF protocol that used cryo-EM densities to refine protein structures. Here we show that, in addition to cryo-EM densities, incorporation of other experimental restraints into the Rosetta-MDFF protocol further improved refined structures. We used NMR chemical shift (CS) data integrated with cryo-EM densities in our hybrid protocol in both the Rosetta step and the molecular dynamics (MD) simulations step. In 15 out of 18 cases for all MD rounds, the refinement results obtained when density maps and NMR chemical shift data were used in combination outperformed those of density map-only refinement. Notably, the improvement in refinement was highest when medium and low-resolution density maps were used. With our hybrid method, the RMSDs of final models obtained were always better than the RMSDs obtained by our previous protocol with just density refinement for both medium (6.9 Å) and low (9 Å) resolution maps. For all the six test proteins with medium resolution density maps (6.9 Å), the final refined structure RMSDs were lower for the hybrid method than for the cryo-EM only refinement. The final refined RMSDs were less than 1.5 Å when our hybrid protocol was used with 4 Å density maps. For four out of the six proteins the final RMSDs were even less than 1 Å. This study demonstrates that by using a combination of cryo-EM and NMR restraints, it is possible to refine structures to atomic resolution, outperforming single restraint refinement. This hybrid protocol will be a valuable tool when only low-resolution cryo-EM density data and NMR chemical shift data are available to refine structures.

Collision-Induced Unfolding Studies of Proteins and Protein Complexes using Drift Tube Ion Mobility-Mass Spectrometer

Elucidating the structures and stabilities of proteins and their complexes is paramount to understanding their biological functions in cellular processes. Native mass spectrometry (MS) coupled with ion mobility spectrometry (IMS) is emerging as an important biophysical technique owing to its high sensitivity, rapid analysis time, and ability to interrogate sample complexity or heterogeneity and the ability to probe protein structure dynamics. Here, a commercial IMS-MS platform has been modified for static native ESI emitters and an extended mass-to-charge range (20 kDa m/z) and its performance capabilities and limits were explored for a range of protein and protein complexes. The results show new potential for this instrument platform for studies of large protein and protein complexes and provides a roadmap for extending the performance metrics for studies of even larger, more complex systems, namely, membrane protein complexes and their interactions with ligands.

Methods and application of coherent X-ray diffraction imaging of noncrystalline particles

Microscopic imaging techniques have been developed to visualize events occurring in biological cells. Coherent X-ray diffraction imaging is one of the techniques applicable to structural analyses of cells and organelles, which have never been crystallized. In the experiment, a single noncrystalline particle is illuminated by an X-ray beam with almost complete spatial coherence. The structure of the particle projected along the direction of the beam is, in principle, retrieved from a finely recorded diffraction pattern alone by using iterative phase-retrieval algorithms. Here, we describe fundamental theory and experimental methods of coherent X-ray diffraction imaging and the recent application in structural studies of noncrystalline specimens by using X-rays available at Super Photon Ring of 8-Gev and SPring-8 Angstrom Compact Free Electron Laser in Japan.

Energy landscape of domain motion in glutamate dehydrogenase deduced from cryo-electron microscopy

Analysis of the conformational changes of protein is important to elucidate the mechanisms of protein motions correlating with their function. Here, we studied the spontaneous domain motion of unliganded glutamate dehydrogenase from Thermococcus profundus using cryo-electron microscopy and proposed a novel method to construct free-energy landscape of protein conformations. Each subunit of the homo-hexameric enzyme comprises nucleotide-binding domain (NAD domain) and hexamer-forming core domain. A large active-site cleft is situated between the two domains and varies from open to close according to the motion of a NAD domain. A three-dimensional map reconstructed from all cryo-electron microscopy images displayed disordered volumes of NAD domains, suggesting that NAD domains in the collected images adopted various conformations in domain motion. Focused classifications on NAD domain of subunits provided several maps of possible conformations in domain motion. To deduce what kinds of conformations appeared in EM images, we developed a novel analysis method that describe the EM maps as a linear combination of representative conformations appearing in a 200-ns molecular dynamics simulation as reference. The analysis enabled us to estimate the appearance frequencies of the representative conformations, which illustrated a free-energy landscape in domain motion. In the open/close domain motion, two free-energy basins hindered the direct transformation from open to closed state. Structure models constructed for representative EM maps in classifications demonstrated the correlation between the energy landscape and conformations in domain motion. Based on the results, the domain motion in glutamate dehydrogenase and the analysis method to visualize conformational changes and free-energy landscape were discussed. DATABASE: The EM maps of the four conformations were deposited to Electron Microscopy Data Bank (EMDB) as accession codes EMD-9845 (open), EMD-9846 (half-open1), EMD-9847 (half-open2), and EMD-9848 (closed), respectively. In addition, the structural models built for the four conformations were deposited to the Protein Data Bank (PDB) as accession codes 6JN9 (open), 6JNA (half-open1), 6JNC (half-open2), and 6JND (closed), respectively.

Modeling Structural Constraints on Protein Evolution via Side-Chain Conformational States

Few models of sequence evolution incorporate parameters describing protein structure, despite its high conservation, essential functional role and increasing availability. We present a structurally aware empirical substitution model for amino acid sequence evolution in which proteins are expressed using an expanded alphabet that relays both amino acid identity and structural information. Each character specifies an amino acid as well as information about the rotamer configuration of its side-chain: the discrete geometric pattern of permitted side-chain atomic positions, as defined by the dihedral angles between covalently linked atoms. By assigning rotamer states in 251,194 protein structures and identifying 4,508,390 substitutions between closely related sequences, we generate a 55-state “Dayhoff-like” model that shows that the evolutionary properties of amino acids depend strongly upon side-chain geometry. The model performs as well as or better than traditional 20-state models for divergence time estimation, tree inference, and ancestral state reconstruction. We conclude that not only is rotamer configuration a valuable source of information for phylogenetic studies, but that modeling the concomitant evolution of sequence and structure may have important implications for understanding protein folding and function.

Refinement of organic crystal structures with multipolar electron scattering factors

A revolution in resolution is occurring now in electron microscopy arising from the development of methods for imaging single particles at cryogenic temperatures and obtaining electron diffraction data from nanocrystals of small organic molecules or macromolecules. Near-atomic or even atomic resolution of molecular structures can be achieved. The basis of these methods is the scattering of an electron beam due to the electrostatic potential of the sample. To analyse these high-quality experimental data, it is necessary to use appropriate atomic scattering factors. The independent atom model (IAM) is commonly used although various more advanced models, already known from X-ray diffraction, can also be applied to enhance the analysis. In this study a comparison is presented of IAM and TAAM (transferable aspherical atom model), the latter with the parameters of the Hansen-Coppens multipole model transferred from the University at Buffalo Databank (UBDB). By this method, TAAM takes into account the fact that atoms in molecules are partially charged and are not spherical. Structure refinements were performed on a carbamazepine crystal using electron structure-factor amplitudes determined experimentally [Jones et al. (2018). ACS Cent. Sci. 4, 1587-1592] or modelled with theoretical quantum-mechanical methods. The results show the possibilities and limitations of the TAAM method when applied to electron diffraction. Among others, the method clearly improves model fitting statistics, when compared with IAM, and allows for reliable refinement of atomic thermal parameters. The improvements are more pronounced with poorer-resolution diffraction data.

In Situ Structural Restraints from Cross-Linking Mass Spectrometry in Human Mitochondria

The field of structural biology is increasingly focusing on studying proteins in situ, i.e., in their greater biological context. Cross-linking mass spectrometry (CLMS) is contributing to this effort, typically through the use of mass spectrometry (MS)-cleavable cross-linkers. Here, we apply the popular noncleavable cross-linker disuccinimidyl suberate (DSS) to human mitochondria and identify 5518 distance restraints between protein residues. Each distance restraint on proteins or their interactions provides structural information within mitochondria. Comparing these restraints to protein data bank (PDB)-deposited structures and comparative models reveals novel protein conformations. Our data suggest, among others, substrates and protein flexibility of mitochondrial heat shock proteins. Through this study, we bring forward two central points for the progression of CLMS towards large-scale in situ structural biology: First, clustered conflicts of cross-link data reveal in situ protein conformation states in contrast to error-rich individual conflicts. Second, noncleavable cross-linkers are compatible with proteome-wide studies.

Theoretical and Experimental Approaches Aimed at Drug Design Targeting Neurodegenerative Diseases

In recent years, green chemistry has been strengthening, showing how basic and applied sciences advance globally, protecting the environment and human health. A clear example of this evolution is the synergy that now exists between theoretical and computational methods to design new drugs in the most efficient possible way, using the minimum of reagents and obtaining the maximum yield. The development of compounds with potential therapeutic activity against multiple targets associated with neurodegenerative diseases/disorders (NDD) such as Alzheimer’s disease is a hot topic in medical chemistry, where different scientists from various disciplines collaborate to find safe, active, and effective drugs. NDD are a public health problem, affecting mainly the population over 60 years old. To generate significant progress in the pharmacological treatment of NDD, it is necessary to employ different experimental strategies of green chemistry, medical chemistry, and molecular biology, coupled with computational and theoretical approaches such as molecular simulations and chemoinformatics, all framed in the rational drug design targeting NDD. Here, we review how green chemistry and computational approaches have been used to develop new compounds with the potential application against NDD, as well as the challenges and new directions of the drug development multidisciplinary process.

Template-based modeling by ClusPro in CASP13 and the potential for using co-evolutionary information in docking

As a participant in the joint CASP13-CAPRI46 assessment, the ClusPro server debuted its new template-based modeling functionality. The addition of this feature, called ClusPro TBM, was motivated by the previous CASP-CAPRI assessments and by the proven ability of template-based methods to produce higher-quality models, provided templates are available. In prior assessments, ClusPro submissions consisted of models that were produced via free docking of pre-generated homology models. This method was successful in terms of the number of acceptable predictions across targets; however, analysis of results showed that purely template-based methods produced a substantially higher number of medium-quality models for targets for which there were good templates available. The addition of template-based modeling has expanded ClusPro's ability to produce higher accuracy predictions, primarily for homomeric but also for some heteromeric targets. Here we review the newest additions to the ClusPro web server and discuss examples of CASP-CAPRI targets that continue to drive further development. We also describe ongoing work not yet implemented in the server. This includes the development of methods to improve template-based models and the use of co-evolutionary information for data-assisted free docking.

Protein Complexes in the Nucleus: The Control of Chromosome Segregation

Mistakes in the process of cell division can lead to the loss, gain or rearrangement of chromosomes. Significant chromosomal abnormalities are usually lethal to the cells and cause spontaneous miscarriages. However, in some cases, defects in the spindle assembly checkpoint lead to severe diseases, such as cancer and birth and development defects, including Down's syndrome. The timely and accurate control of chromosome segregation in mitosis relies on the spindle assembly checkpoint (SAC), an evolutionary conserved, self-regulated signalling system present in higher organisms. The spindle assembly checkpoint is orchestrated by dynamic interactions between spindle microtubules and the kinetochore , a multiprotein complex that constitutes the site for attachment of chromosomes to microtubule polymers to pull sister chromatids apart during cell division. This chapter discusses the current molecular understanding of the essential, highly dynamic molecular interactions underpinning spindle assembly checkpoint signalling and how the complex choreography of interactions can be coordinated in time and space to finely regulate the process. The potential of targeting this signalling pathway to interfere with the abnormal segregation of chromosomes, which occurs in diverse malignancies and the new opportunities that recent technological developments are opening up for a deeper understanding of the spindle assembly checkpoint are also discussed.

Principles and characteristics of biological assemblies in experimentally determined protein structures

More than half of all structures in the PDB are assemblies of two or more proteins, including both homooligomers and heterooligomers. Structural information on these assemblies comes from X-ray crystallography, NMR, and cryo-EM spectroscopy. The correct assembly in an X-ray structure is often ambiguous, and computational methods have been developed to identify the most likely biologically relevant assembly based on physical properties of assemblies and sequence conservation in interfaces. Taking advantage of the large number of structures now available, some of the most recent methods have relied on similarity of interfaces and assemblies across structures of homologous proteins.

Simultaneous Determination of Protein Structure and Dynamics Using Cryo-Electron Microscopy

Cryo-electron microscopy is rapidly emerging as a powerful technique to determine the structures of complex macromolecular systems elusive to other techniques. Because many of these systems are highly dynamical, characterizing their movements is also a crucial step to unravel their biological functions. To achieve this goal, we report an integrative modeling approach to simultaneously determine structure and dynamics of macromolecular systems from cryo-electron microscopy density maps. By quantifying the level of noise in the data and dealing with their ensemble-averaged nature, this approach enables the integration of multiple sources of information to model ensembles of structures and infer their populations. We illustrate the method by characterizing structure and dynamics of the integral membrane receptor STRA6, thus providing insights into the mechanisms by which it interacts with retinol binding protein and translocates retinol across the membrane.

Ras-Specific GTPase-Activating Proteins-Structures, Mechanisms, and Interactions

Ras-specific GTPase-activating proteins (RasGAPs) down-regulate the biological activity of Ras proteins by accelerating their intrinsic rate of GTP hydrolysis, basically by a transition state stabilizing mechanism. Oncogenic Ras is commonly not sensitive to RasGAPs caused by interference of mutants with the electronic or steric requirements of the transition state, resulting in up-regulation of activated Ras in respective cells. RasGAPs are modular proteins containing a helical catalytic RasGAP module surrounded by smaller domains that are frequently involved in the subcellular localization or contributing to regulatory features of their host proteins. In this review, we summarize current knowledge about RasGAP structure, mechanism, regulation, and dual-substrate specificity and discuss in some detail neurofibromin, one of the most important negative Ras regulators in cellular growth control and neuronal function.

Trapping the Complex Molecular Machinery of Polyketide and Fatty Acid Synthases with Tunable Silylcyanohydrin Crosslinkers

Many families of natural products are synthesized by large multidomain biological machines commonly referred to as megasynthases. While the advance of mechanism-based tools has opened new windows into the structural features within the protein-protein interfaces guiding carrier protein dependent enzymes, there is an immediate need for tools that can be engaged to link co-translated domains in a site-selective manner. Now, the use of silylcyanohydrins is demonstrated in a two-step, two-site selective crosslinking for the trapping of carrier-protein interactions within megasynthases. This advance provides a new tool to trap intermediate states within multimodular systems, a key step toward understanding the specificities within fatty acid (FAS) and polyketide (PKS) synthases.

Homology modeling in drug discovery: Overview, current applications, and future perspectives

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.

An update on the discovery and development of selective heat shock protein inhibitors as anti-cancer therapy

INTRODUCTION

Over the years, not a single HSP inhibitor has progressed into the post-market phase of drug development despite the success recorded in various pre-clinical and clinical studies. The inability of existing drugs to specifically target oncogenic HSPs has majorly accounted for these setbacks. Recent combinatorial strategies that incorporated computer-aided drug design (CADD) techniques are geared towards the development of highly specific HSP inhibitors with increased activities and minimal toxicities. Areas covered: In this review, strategic therapeutic approaches that have recently aided the development of selective HSP inhibitors were highlighted. Also, the significant contributions of CADD techniques over the years were discussed in detail. This article further describes promising computational paradigms and their applications towards the discovery of highly specific inhibitors of oncogenic HSPs. Expert opinion: The recent shift towards highly selective and specific HSP inhibition has shown great promise as evidenced by the development of paralog/isoform-selective HSP drugs. It could be further augmented with computer-aided drug design strategies, which incorporate reliable methods that would greatly enhance the design and optimization of novel inhibitors with improved activities and minimal toxicities.

Locating macromolecules and determining structures inside bacterial cells using electron cryotomography

Electron cryotomography (cryo-ET) is an imaging technique uniquely suited to the study of bacterial ultrastructure and cell biology. Recent years have seen a surge in structural and cell biology research on bacteria using cryo-ET. This research has driven major technical developments in the field, with applications emerging to address a wide range of biological questions. In this review, we explore the diversity of cryo-ET approaches used for structural and cellular microbiology, with a focus on in situ localization and structure determination of macromolecules. The first section describes strategies employed to locate target macromolecules within large cellular volumes. Next, we explore methods to study thick specimens by sample thinning. Finally, we review examples of macromolecular structure determination in a cellular context using cryo-ET. The examples outlined serve as powerful demonstrations of how the cellular location, structure, and function of any bacterial macromolecule of interest can be investigated using cryo-ET.

Concepts in Light Microscopy of Viruses

Viruses threaten humans, livestock, and plants, and are difficult to combat. Imaging of viruses by light microscopy is key to uncover the nature of known and emerging viruses in the quest for finding new ways to treat viral disease and deepening the understanding of virus–host interactions. Here, we provide an overview of recent technology for imaging cells and viruses by light microscopy, in particular fluorescence microscopy in static and live-cell modes. The review lays out guidelines for how novel fluorescent chemical probes and proteins can be used in light microscopy to illuminate cells, and how they can be used to study virus infections. We discuss advantages and opportunities of confocal and multi-photon microscopy, selective plane illumination microscopy, and super-resolution microscopy. We emphasize the prevalent concepts in image processing and data analyses, and provide an outlook into label-free digital holographic microscopy for virus research.

High-vacuum optical platform for cryo-CLEM (HOPE): A new solution for non-integrated multiscale correlative light and electron microscopy

Cryo-correlative light and electron microscopy (cryo-CLEM) offers a unique way to analyze the high-resolution structural information of cryo-vitrified specimen by cryo-electron microscopy (cryo-EM) with the guide of the search for unique events by cryo-fluorescence microscopy (cryo-FM). To achieve cryo-FM, a trade-off must be made between the temperature and performance of objective lens. The temperature of specimen should be kept below devitrification while the distance between the objective lens and specimen should be short enough for high resolution imaging. Although special objective lens was designed in many current cryo-FM approaches, the unavoided frosting and ice contamination are still affecting the efficiency of cryo-CLEM. In addition, the correlation accuracy between cryo-FM and cryo-EM would be reduced during the current specimen transfer procedure. Here, we report an improved cryo-CLEM technique (high-vacuum optical platform for cryo-CLEM, HOPE) based on a high-vacuum optical stage and a commercial cryo-EM holder. The HOPE stage comprises of a special adapter to suit the cryo-EM holder and a high-vacuum chamber with an anti-contamination system. It provides a clean and enduring environment for cryo specimen, while the normal dry objective lens in room temperature can be used via the optical windows. The 'touch-free' specimen transfer via cryo-EM holder allows least specimen deformation and thus maximizes the correlation accuracy between cryo-FM and cryo-EM. Besides, we developed a software to perform semi-automatic cryo-EM acquisition of the target region localized by cryo-FM. Our work provides a new solution for cryo-CLEM and can be adapted for different commercial fluorescence microscope and electron microscope.