Cellular Electron Cryotomography: Toward Structural Biology In Situ.

Electron cryotomography (ECT) provides three-dimensional views of macromolecular complexes inside cells in a native frozen-hydrated state. Over the last two decades, ECT has revealed the ultrastructure of cells in unprecedented detail. It has also allowed us to visualize the structures of macromolecular machines in their native context inside intact cells. In many cases, such machines cannot be purified intact for in vitro study. In other cases, the function of a structure is lost outside the cell, so that the mechanism can be understood only by observation in situ. In this review, we describe the technique and its history and provide examples of its power when applied to cell biology. We also discuss the integration of ECT with other techniques, including lower-resolution fluorescence imaging and higher-resolution atomic structure determination, to cover the full scale of cellular processes.

[Recanalization of colon in the treatment of patients with acute malignant obstructive colonic obstruction]. / Metody rekanalizatsii tolstoi kishki v lechenii bol'nykh ostroi obturatsionnoi tolstokishechnoi neprokhodimost'yu zlokachestvennogo geneza.

OBJECTIVE: To analyze available literature data on the methods of colon recanalization in patients with acute malignant obstructive colonic obstruction. MATERIAL AND METHODS: We retrospectively analyzed literature data on the treatment of acute neoplastic colonic obstruction. RESULTS: We reviewed available national and foreign literature data on various methods of colon recanalization including various modern and hybrid techniques. CONCLUSION: Methods of colon recanalization with subsequent stenting are the most optimal for preoperative decompression of colon. These measures are effective and allow postponing radical surgery or avoiding it altogether without worsening the prognosis of the underlying pathology. However, there is a small amount of literature data on modern hybrid methods of recanalization.

A Hybrid Structural Method for Investigating Low Molecular Weight Oligomeric Structures of Amyloid Beta.

Spurred in part by the failure of recent therapeutics targeting amyloid ß plaques in Alzheimer's Disease (AD), attention is increasingly turning to the oligomeric forms of this peptide that form early in the aggregation process. However, while numerous amyloid ß fibril structures have been characterized, primarily by NMR spectroscopy and cryo-EM, obtaining structural information on the low molecular weight forms of amyloid ß that presumably precede and/or seed fibril formation has proved challenging. These transient forms are heterogeneous, and depend heavily on experimental conditions such as buffer, temperature, concentration, and degree of quiescence during measurement. Here, we present the concept for a new approach to delineating structural features of early-stage low molecular weight amyloid ß oligomers, using a solvent accessibility assay in conjunction with simultaneous fluorescence measurements.

Advanced approaches for elucidating structures of large RNAs using NMR spectroscopy and complementary methods.

NMR spectroscopy is a key technique that has significantly advanced our understanding of RNA structure and dynamics. However, determination of large RNA structures by NMR spectroscopy remains a significant technical challenge. In this review, we highlight advances that facilitate NMR studies of large RNAs, including methods for sample preparation, isotope labeling strategies, and data acquisition. In addition, we review hybrid approaches that have been instrumental in the structure determination of large RNAs.

Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree.

Episodes of frequent flooding continue to increase, often causing serious damage and tools to identify areas affected by such disasters have become indispensable in today's society. Using the latest techniques can make very accurate flood predictions. In this study, we introduce four effective methods to evaluate the flood susceptibility of Poyang County, in China, by integrating two independent models of frequency ratio and index of entropy with multilayer perceptron and classification and regression tree models. The flood locations of the study area were identified through the flood inventory process, and 12 flood conditioning factors were used in the training and validation processes. According to the results of the linear support vector machine, elevation, slope angle, and soil have the highest predictive ability. The experimental results of the four hybrid models demonstrate that between 20% and 50% of the study area has high and very high flood susceptibility. The multilayer perceptron-probability density hybrid model is the most effective among the six comparative methods.

Simulation-Based Methods for Model Building and Refinement in Cryoelectron Microscopy.

Advances in cryoelectron microscopy (cryo-EM) have revolutionized the structural investigation of large macromolecular assemblies. In this review, we first provide a broad overview of modeling methods used for flexible fitting of molecular models into cryo-EM density maps. We give special attention to approaches rooted in molecular simulations-atomistic molecular dynamics and Monte Carlo. Concise descriptions of the methods are given along with discussion of their advantages, limitations, and most popular alternatives. We also describe recent extensions of the widely used molecular dynamics flexible fitting (MDFF) method and discuss how different model-building techniques could be incorporated into new hybrid modeling schemes and simulation workflows. Finally, we provide two illustrative examples of model-building and refinement strategies employing MDFF, cascade MDFF, and RosettaCM. These examples come from recent cryo-EM studies that elucidated transcription preinitiation complexes and shed light on the functional roles of these assemblies in gene expression and gene regulation.

[Complete myocardial revascularization in patients with multiple-vessel coronary artery disease and partial or complete absence of the grafts for coronary artery bypass surgery]. / Sovremennye podkhody k polnoi revaskulyarizatsii miokarda u patsientov s mnogososudistym porazheniem koronarnykh arterii i chastichnym ili polnym otsutstviem transplantatov dlya koronarnogo shuntirovaniya.

Complete revascularization in patients with multiple-vessel coronary artery disease and partial or complete absence of the grafts is still actual problem for cardiac surgeons. The main causes of the absence of conduits for coronary artery bypass surgery are aging of population, increased incidence of repeated coronary artery bypass surgery and prevalence of varicose vein disease of the lower extremities. The most perspective approaches characterized by acceptable early and long-term postoperative outcomes are bilateral internal mammary artery grafting, sequential bypass including autoarterial grafts, as well as hybrid revascularization methods. However, treatment strategy is individualized in each patient.

Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review.

Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.

A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data.

While 3D structure determination of small (<15 kDa) proteins by solution NMR is largely automated and routine, structural analysis of larger proteins is more challenging. An emerging hybrid strategy for modeling protein structures combines sparse NMR data that can be obtained for larger proteins with sequence co-variation data, called evolutionary couplings (ECs), obtained from multiple sequence alignments of protein families. This hybrid "EC-NMR" method can be used to accurately model larger (15-60 kDa) proteins, and more rapidly determine structures of smaller (5-15 kDa) proteins using only backbone NMR data. The resulting structures have accuracies relative to reference structures comparable to those obtained with full backbone and sidechain NMR resonance assignments. The requirement that evolutionary couplings (ECs) are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, potentially also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

Integrative/Hybrid Methods Structural Biology: Role of Macromolecular Crystallography.

Macromolecular crystallography has been central to the emergence and development of structural biology as a scientific discipline. Approximately 90% of the more than 138,000 three-dimensional structures currently available in the Protein Data Bank (PDB) archive, the single, global open access data resource for macromolecular structure data, were determined using X-ray crystallography. MX, the enormous variety of PDB structures of proteins, DNA, and RNA, and computational models derived therefrom will be central to the growth of integrative or hybrid (I/H) methods structural studies of macromolecular assemblies and other complex biological systems.

Structural rearrangements in the phage head-to-tail interface during assembly and infection.

Many icosahedral viruses use a specialized portal vertex to control genome encapsidation and release from the viral capsid. In tailed bacteriophages, the portal system is connected to a tail structure that provides the pipeline for genome delivery to the host cell. We report the first, to our knowledge, subnanometer structures of the complete portal-phage tail interface that mimic the states before and after DNA release during phage infection. They uncover structural rearrangements associated with intimate protein-DNA interactions. The portal protein gp6 of bacteriophage SPP1 undergoes a concerted reorganization of the structural elements of its central channel during interaction with DNA. A network of protein-protein interactions primes consecutive binding of proteins gp15 and gp16 to extend and close the channel. This critical step that prevents genome leakage from the capsid is achieved by a previously unidentified allosteric mechanism: gp16 binding to two different regions of gp15 drives correct positioning and folding of an inner gp16 loop to interact with equivalent loops of the other gp16 subunits. Together, these loops build a plug that closes the channel. Gp16 then fastens the tail to yield the infectious virion. The gatekeeper system opens for viral genome exit at the beginning of infection but recloses afterward, suggesting a molecular diaphragm-like mechanism to control DNA efflux. The mechanisms described here, controlling the essential steps of phage genome movements during virus assembly and infection, are likely to be conserved among long-tailed phages, the largest group of viruses in the Biosphere.

Structure, dynamics and biophysics of the cytoplasmic protein-protein complexes of the bacterial phosphoenolpyruvate: sugar phosphotransferase system.

The bacterial phosphotransferase system (PTS) couples phosphoryl transfer, via a series of bimolecular protein-protein interactions, to sugar transport across the membrane. The multitude of complexes in the PTS provides a paradigm for studying protein interactions, and for understanding how the same binding surface can specifically recognize a diverse array of targets. Fifteen years of work aimed at solving the solution structures of all soluble protein-protein complexes of the PTS has served as a test bed for developing NMR and integrated hybrid approaches to study larger complexes in solution and to probe transient, spectroscopically invisible states, including encounter complexes. We review these approaches, highlighting the problems that can be tackled with these methods, and summarize the current findings on protein interactions.

Complementary uses of small angle X-ray scattering and X-ray crystallography.

Most proteins function within networks and, therefore, protein interactions are central to protein function. Although stable macromolecular machines have been extensively studied, dynamic protein interactions remain poorly understood. Small-angle X-ray scattering probes the size, shape and dynamics of proteins in solution at low resolution and can be used to study samples in a large range of molecular weights. Therefore, it has emerged as a powerful technique to study the structure and dynamics of biomolecular systems and bridge fragmented information obtained using high-resolution techniques. Here we review how small-angle X-ray scattering can be combined with other structural biology techniques to study protein dynamics. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

Blended particle filters for large-dimensional chaotic dynamical systems.

A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below.

Blind testing of cross-linking/mass spectrometry hybrid methods in CASP11.

Hybrid approaches combine computational methods with experimental data. The information contained in the experimental data can be leveraged to probe the structure of proteins otherwise elusive to computational methods. Compared with computational methods, the structures produced by hybrid methods exhibit some degree of experimental validation. In spite of these advantages, most hybrid methods have not yet been validated in blind tests, hampering their development. Here, we describe the first blind test of a specific cross-link based hybrid method in CASP. This blind test was coordinated by the CASP organizers and utilized a novel, high-density cross-linking/mass-spectrometry (CLMS) approach that is able to collect high-density CLMS data in a matter of days. This experimental protocol was developed in the Rappsilber laboratory. This approach exploits the chemistry of a highly reactive, photoactivatable cross-linker to produce an order of magnitude more cross-links than homobifunctional cross-linkers. The Rappsilber laboratory generated experimental CLMS data based on this protocol, submitted the data to the CASP organizers which then released this data to the CASP11 prediction groups in a separate, CLMS assisted modeling experiment. We did not observe a clear improvement of assisted models, presumably because the properties of the CLMS data-uncertainty in cross-link identification and residue-residue assignment, and uneven distribution over the protein-were largely unknown to the prediction groups and their approaches were not yet tailored to this kind of data. We also suggest modifications to the CLMS-CASP experiment and discuss the importance of rigorous blind testing in the development of hybrid methods. Proteins 2016; 84(Suppl 1):152-163. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Crystal structure and solution characterization of the thioredoxin-2 from Plasmodium falciparum, a constituent of an essential parasitic protein export complex.

Survival of the malaria parasite Plasmodium falciparum when it infects red blood cells depends upon its ability to export hundreds of its proteins beyond an encasing vacuole. Protein export is mediated by a parasite-derived protein complex, the Plasmodium translocon of exported proteins (PTEX), and requires unfolding of the different cargos prior to their translocation across the vacuolar membrane. Unfolding is performed by the AAA+protein unfoldase HSP101/ClpB2 and the thioredoxin-2 enzyme (TRX2). Protein trafficking is dramatically impaired in parasites with defective HSP101 or lacking TRX2. These two PTEX subunits drive export and are targets for the design of a novel class of antimalarials: protein export inhibitors. To rationalize inhibitor design, we solved the crystal structure of Pfal-TRX2 at 2.2-Å resolution. Within the asymmetric unit, the three different copies of this protein disulfide reductase sample its two redox catalytic states. Size exclusion chromatography and small-angle X-ray scattering (SAXS) analyses demonstrate that Pfal-TRX2 is monomeric in solution. A non-conserved N-terminal extension precedes the canonical thioredoxin-fold; although it is not observed in our structure, our solution analysis suggests it is flexible in contrast to Plasmodium thioredoxin-1. This represents a first step towards the reconstitution of the entire PTEX for mechanistic and structural studies.

Application of SAXS for the Structural Characterization of IDPs.

Small-angle X-ray scattering (SAXS) is a powerful structural method allowing one to study the structure, folding state and flexibility of native particles and complexes in solution and to rapidly analyze structural changes in response to variations in external conditions. New high brilliance sources and novel data analysis methods significantly enhanced resolution and reliability of structural models provided by the technique. Automation of the SAXS experiment, data processing and interpretation make solution SAXS a streamline tool for large scale structural studies in molecular biology. The method provides low resolution macromolecular shapes ab initio and is readily combined with other structural and biochemical techniques in integrative studies. Very importantly, SAXS is sensitive to macromolecular flexibility being one of the few structural techniques applicable to flexible systems and intrinsically disordered proteins (IDPs). A major recent development is the use of SAXS to study particle dynamics in solution by ensemble approaches, which allow one to quantitatively characterize flexible systems. Of special interest is the joint use of SAXS with solution NMR, given that both methods yield highly complementary structural information, in particular, for IDPs. In this chapter, we present the basics of SAXS and also consider protocols of the experiment and data analysis for different scenarios depending on the type of the studied object. These include ab initio shape reconstruction, validation of available high resolution structures and rigid body modelling for folded macromolecules and also characterisation of flexible proteins with the ensemble methods. The methods are illustrated by examples of recent applications and further perspectives of the integrative use of SAXS with NMR in the studies of IDPs are discussed.

Dietary patterns and the risk of diabetes in Korean adults: A cross-sectional and prospective cohort study.

OBJECTIVE: The aim of this study was to identify dietary patterns associated with diabetes in Korean adults and to investigate their association with diabetes risk in both a cross-sectional and prospective study. METHODS: Predefined food groups collected by the Korea National Health and Nutrition Examination Survey (KNHANES 2015-2018, n = 19 721) were entered in a reduced rank regression (RRR) model, followed by stepwise linear regression analyses to identify the most predictive dietary patterns. We evaluated the construct validity of dietary patterns in two independent samples from KNHANES 2019 to 2021 (n = 14 223) and the Health Examinees (HEXA) cohort study (n = 30 013). Associations between dietary patterns and diabetes risk were examined using multivariable regression and multivariable-adjusted Cox proportional hazard models, respectively. RESULTS: A dietary pattern was identified with high positive loadings for refined white rice, kimchi and salted vegetables, wheat flour and bread, and seasonings, and high negative loadings for whole grains, legumes with tofu and soymilk, poultry, eggs, and plant oils. The higher pattern scores were significantly associated with diabetes risk in KNHANES 2015 to 2018 (male: odds ratio [OR]: 1.59; 95% confidence interval [CI]: 1.35, 1.88; female: OR: 1.37; 95% CI: 1.18, 1.52), KNHANES 2019 to 2021 (male: OR: 1.47; 95% CI: 1.01, 1.69; female: OR: 1.37; 95% CI: 1.18, 1.54), and HEXA study (male: hazard ratio [HR]: 1.10; 95% CI: 1.01, 1.34; female: HR: 1.24; 95% CI: 1.02, 1.52). CONCLUSIONS: Dietary patterns derived by RRR followed by stepwise linear regression analyses were associated with increased risks of diabetes among Korean adults.

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques.

Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply-demand restrictions in the electrical grids. Due to the highly random nature of environmental conditions, accurate prediction of PV power has limitations, particularly on long and short periods. Thus, this research provides a new hybrid model for forecasting short PV power based on the fusing of multi-frequency information of different decomposition techniques that will allow a forecaster to provide reliable forecasts. We evaluate and provide insights into the performance of five multi-scale decomposition algorithms combined with a deep convolution neural network (CNN). Additionally, we compare the suggested combination approach's performance to that of existing forecast models. An exhaustive assessment is carried out using three grid-connected PV power plants in Algeria with a total installed capacity of 73.1 MW. The developed fusing strategy displayed an outstanding forecasting performance. The comparative analysis of the proposed combination method with the stand-alone forecast model and other hybridization techniques proves its superiority in terms of forecasting precision, with an RMSE varying in the range of [0.454-1.54] for the three studied PV stations.

An interpretable online prediction method for remaining useful life of lithium-ion batteries.

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is advantageous for maintaining the stability of electrical systems. In this paper, an interpretable online method which can reflect capacity regeneration is proposed to accurately estimate the RUL. Firstly, four health indicators (HIs) are extracted from the charging and discharging process for online prediction. Then, the HIs model is trained using support vector regression to obtain future features. And the capacity model of Gaussian process regression (GPR) is trained and analyzed by Shapley additive explanation (SHAP). Meanwhile, the state space for capacity prediction is constructed with the addition of Gaussian non-white noise to simulate the capacity regeneration. And the modified predicted HIs and noise are obtained by unscented Kalman filter. Finally, according to SHAP explainer, the predicted HIs acting as the baseline and the modified HIs containing information on capacity regeneration are chosen to predict RUL. In addition, the bounds of confidence intervals (CIs) are calculated separately to reflect the regenerated capacity. The experimental results demonstrate that the proposed online method can achieve high accuracy and effectively capture the capacity regeneration. The absolute error of failure RUL is below 5 and the minimum confidence interval is only 2.