A large-scale systematic survey reveals recurring molecular features of public antibody responses to SARS-CoV-2.

Global research to combat the COVID-19 pandemic has led to the isolation and characterization of thousands of human antibodies to the SARS-CoV-2 spike protein, providing an unprecedented opportunity to study the antibody response to a single antigen. Using the information derived from 88 research publications and 13 patents, we assembled a dataset of ∼8,000 human antibodies to the SARS-CoV-2 spike protein from >200 donors. By analyzing immunoglobulin V and D gene usages, complementarity-determining region H3 sequences, and somatic hypermutations, we demonstrated that the common (public) responses to different domains of the spike protein were quite different. We further used these sequences to train a deep-learning model to accurately distinguish between the human antibodies to SARS-CoV-2 spike protein and those to influenza hemagglutinin protein. Overall, this study provides an informative resource for antibody research and enhances our molecular understanding of public antibody responses.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Structural basis for Mis18 complex assembly and its implications for centromere maintenance.

The centromere, defined by the enrichment of CENP-A (a Histone H3 variant) containing nucleosomes, is a specialised chromosomal locus that acts as a microtubule attachment site. To preserve centromere identity, CENP-A levels must be maintained through active CENP-A loading during the cell cycle. A central player mediating this process is the Mis18 complex (Mis18α, Mis18ß and Mis18BP1), which recruits the CENP-A-specific chaperone HJURP to centromeres for CENP-A deposition. Here, using a multi-pronged approach, we characterise the structure of the Mis18 complex and show that multiple hetero- and homo-oligomeric interfaces facilitate the hetero-octameric Mis18 complex assembly composed of 4 Mis18α, 2 Mis18ß and 2 Mis18BP1. Evaluation of structure-guided/separation-of-function mutants reveals structural determinants essential for cell cycle controlled Mis18 complex assembly and centromere maintenance. Our results provide new mechanistic insights on centromere maintenance, highlighting that while Mis18α can associate with centromeres and deposit CENP-A independently of Mis18ß, the latter is indispensable for the optimal level of CENP-A loading required for preserving the centromere identity.

Atomic-number (Z)-correlated atomic sizes for deciphering electron microscopic molecular images.

SignificanceAtomic resolution transmission electron microscopy (TEM) has opened up a new era of molecular science by providing atomic video images of dynamic motions of single organic and inorganic molecules. However, the images often look different from the images of molecular models, because these models are designed to visualize the electronic properties of the molecule instead of nuclear electrostatic potentials that are felt by the e-beam in TEM imaging. Here, we propose a molecular model that reproduces TEM images using atomic radii correlated to atomic number (Z). The model serves to provide a priori a useful idea of how a single molecule, molecular assemblies, and thin crystals of organic or inorganic materials look in TEM.

iMAX FRET (Information Maximized FRET) for Multipoint Single-Molecule Structural Analysis.

Understanding the structure of biomolecules is vital for deciphering their roles in biological systems. Single-molecule techniques have emerged as alternatives to conventional ensemble structure analysis methods for uncovering new biology in molecular dynamics and interaction studies, yet only limited structural information could be obtained experimentally. Here, we address this challenge by introducing iMAX FRET, a one-pot method that allows ab initio 3D profiling of individual molecules using two-color FRET measurements. Through the stochastic exchange of fluorescent weak binders, iMAX FRET simultaneously assesses multiple distances on a biomolecule within a few minutes, which can then be used to reconstruct the coordinates of up to four points in each molecule, allowing structure-based inference. We demonstrate the 3D reconstruction of DNA nanostructures, protein quaternary structures, and conformational changes in proteins. With iMAX FRET, we provide a powerful approach to advance the understanding of biomolecular structure by expanding conventional FRET analysis to three dimensions.

Genome-wide characterization of plant CTP:phosphocholine cytidylyltransferases through evolutionary, biochemical, and structural analyses.

Phosphatidylcholine has essential functions in many eukaryotic cells, and its de novo biosynthesis is rate-limited by cytidine triphosphate:phosphocholine cytidylyltransferase (CCT). Although the biological and biochemical functions of CCT have been reported in mammals and several plants, this key enzyme has yet to be examined at a genome-wide level. As such, certain fundamental questions remain unanswered, including the evolutionary history, genetic and functional relationships, and structural variations among CCTs in the green lineage. In the current study, in-depth phylogenetic analysis, as well as the conservation and diversification in CCT gene structure and motif patterns, indicated that CCTs exist broadly in chlorophytes, bryophytes, lycophytes, monilophytes, gymnosperms, early-diverging angiosperms, monocots, and eudicots, and form eight relatively conserved clades. To further explore the potential function of selection pressure, we conducted extensive selection pressure analysis with a representative CCT gene, CCT1 from the model plant Arabidopsis thaliana (AthCCT1), and identified two positive selection sites, L59 and Q156. Site-directed mutagenesis and in vitro enzyme assays demonstrated that these positively selected sites were indeed important for the activity and substrate affinity of AthCCT1, and subsequent 3D structure analyses explained the potential biochemical mechanism. Taken together, our results unraveled the evolution and diversity of CCTs in the green lineage, as well as their association with the enzyme's biochemical and structural properties, and expanded our understanding of this important enzyme at the genome-wide level.

Computational identification of key residues regulating fluorescence emission in a red/green cyanobacteriochrome.

Cyanobacteriochromes (CBCRs) are linear tetrapyrrole bilin-binding photoreceptors of cyanobacteria that exhibit high spectral diversity, gaining attention in optogenetics and bioimaging applications. Several engineering studies on CBCRs were attempted, especially for designing near-infrared (NIR) fluorescent proteins with longer fluorescence wavelengths. However, despite continuous efforts, a key component regulating fluorescence emission property in CBCRs is still poorly understood. As a model system, we focused on red/green CBCR Slr1393g3, from the unicellular cyanobacterium Synechocystis sp. PCC 6803 to engineer Pr to get far-red light-emitting property. Energy profiling and pairwise structural comparison of Slr1393g3 variants effectively reveal the mutations that are critical to the fluorescence changes. H497 seems to play a key role in stabilizing the chromophore environment, especially the α3 helix, while H495, T499, and Q502 are potential key residues determining fluorescence emission peak wavelength. We also found that mutations of α2 and α4 helical regions are closely related to the chromophore binding stability and likely affect fluorescence properties. Taken together, our computational analysis suggests that the fluorescence of Slr1393g3 is mainly controlled by the stabilization of the chromophore binding pocket. The predicted key residues potentially regulating the fluorescence emission property of a red/green CBCR will be advantageous for designing improved NIR fluorescent protein when combined with in vitro molecular evolution approaches.

Orthogonal effect of exchange and correlation parameters in density functional theory to compute geometric and spectroscopic quantities.

The influence of the composition of the functional used for density functional theory computations on one structural parameter (a dihedral angle) and a spectroscopic parameter (absorption wavelength) is assessed in this study on the basis of two molecules (flavonols). In this kind of molecules, these two parameters should be correlated according to the nature of the electronic transition involved. However, it is shown herein that by varying the proportion of true exchange and correlation while building a functional, it is possible to obtain independently a large range of values for these parameters without any relation with the underlying real values. Therefore, it is concluded that the choice of a functional after a benchmark, especially using user-defined functionals, should be carried out with great care to avoid such effects.

Targeting JAK2/STAT3 for the treatment of cancer: A review on recent advancements in molecular development using structural analysis and SAR investigations.

Cancer is indeed considered a hazardous and potentially life-threatening disorder. The JAK/STAT pathway is an important intracellular signaling cascade essential for many physiological functions, such as immune response, cell proliferation, and differentiation. Dysregulation of this pathway aids in the progression and development of cancer. The downstream JAK2/STAT3 signaling cascades are legitimate targets against which newer anticancer drugs can be developed to prevent and treat cancer. Understanding the mechanisms behind JAK2/STAT3 participation in cancer has paved the way for developing innovative targeted medicines with the potential to improve cancer treatment outcomes. This article provides information on the current scenario and recent advancements in the design and development of anticancer drugs targeting JAK2/STAT3, including structural analysis and SAR investigations of synthesized molecules. Numerous preclinical and clinical trials are ongoing on these inhibitors, which are highlighted to gain more insight into the broader development prospects of inhibitors of JAK2/STAT3.

Structural Impact Assessment of Cytochrome P450 2A13 Polymorphisms Using Molecular Dynamics Simulations.

One of the members of CYP, a monooxygenase, CYP2A13 is involved in the metabolism of nicotine, coumarin, and tobacco-specific nitrosamine. Genetic polymorphisms have been identified in CYP2A13, with reported loss or reduction in enzymatic activity in CYP2A13 allelic variants. This study aimed to unravel the mechanism underlying the diminished enzymatic activity of CYP2A13 variants by investigating their three-dimensional structures through molecular dynamics (MD) simulations. For each variant, MD simulations of 1000 ns were performed, and the obtained results were compared with those of the wild type. The findings indicated alterations in the interaction with heme in CYP2A13.4, .6, .8, and .9. In the case of CYP2A13.5, observable effects on the helix structure related to the interaction with the redox partner were identified. These conformational changes were sufficient to cause a decrease in enzyme activity in the variants. Our findings provide valuable insights into the molecular mechanisms associated with the diminished activity in the CYP2A13 polymorphisms.

Characterization of Drug Delivery Systems by Transmission Electron Microscopy.

The contribution of electron microscopy, and here, in particular transmission electron microscopy (TEM), to the formulation and understanding of the biological action of drug delivery systems has led to a better insight into the design principles of drug delivery systems. TEM can be applied for particle characterization, for the visualization of the uptake and intracellular pathways of drug vehicles in cells and tissues and more recently can be also applied for the high-resolution investigation of drug-receptor interactions with near-atomic resolution. This chapter introduces basic techniques to optimize imaging quality of soft matter samples, highlights possibilities to study certain aspects of drug delivery applications, and finally provides a short introduction to high-resolution characterization possibilities which recently emerged.

Transiently structured head domains control intermediate filament assembly.

Low complexity (LC) head domains 92 and 108 residues in length are, respectively, required for assembly of neurofilament light (NFL) and desmin intermediate filaments (IFs). As studied in isolation, these IF head domains interconvert between states of conformational disorder and labile, ß-strand-enriched polymers. Solid-state NMR (ss-NMR) spectroscopic studies of NFL and desmin head domain polymers reveal spectral patterns consistent with structural order. A combination of intein chemistry and segmental isotope labeling allowed preparation of fully assembled NFL and desmin IFs that could also be studied by ss-NMR. Assembled IFs revealed spectra overlapping with those observed for ß-strand-enriched polymers formed from the isolated NFL and desmin head domains. Phosphorylation and disease-causing mutations reciprocally alter NFL and desmin head domain self-association yet commonly impede IF assembly. These observations show how facultative structural assembly of LC domains via labile, ß-strand-enriched self-interactions may broadly influence cell morphology.

Effects on brain structural and functional in deaf children after aerobic exercise training: a pilot cluster randomized controlled study.

Purpose: To examine effects of aerobic exercise interventions on brain via the structural Magnetic Resonance Imaging (MRI), as well as functional change during working memory (WM) task using fMRI in deaf children.Method: The study applied a cluster randomized controlled design. Twelve deaf children in the intervention group were required to complete an eleven-week aerobic exercise intervention, while other twelve age and gender matched deaf children in the control group were required to keep their normal daily life. Task fMRI images of each participant were acquired in the baseline and post intervention period. The surface-based morphometry (SBM) analysis and functional activation analysis were employed to probe the effects of 11-week aerobic exercise on cerebral structural and functional in deaf children, respectively.Results: The 11-week aerobic exercise intervention did not change brain structure in deaf children. However, behavior performance (reaction time and mean accuracy rate) presented significant improvements after the 11-week aerobic exercise intervention. Compared to the control group, the intervention group showed decreased reaction time in the 2-back (p < 0.001) and 2-0 back (p < 0.001), and increased mean accuracy rate during 2-back (p = 0.034). Furthermore, enhanced brain activations in the left supplementary motor cortex (p < 0.05, FDR-corrected) and left paracentral lobule (p < 0.05, FDR-corrected) were observed in the intervention group.Conclusion: 11-week aerobic exercise intervention may not be able to modulate brain structure in deaf children, but may have significantly positive effects on behavior performance and brain functional activation during WM task.

A Computer Vision Framework for Structural Analysis of Hand-Drawn Engineering Sketches.

Structural engineers are often required to draw two-dimensional engineering sketches for quick structural analysis, either by hand calculation or using analysis software. However, calculation by hand is slow and error-prone, and the manual conversion of a hand-drawn sketch into a virtual model is tedious and time-consuming. This paper presents a complete and autonomous framework for converting a hand-drawn engineering sketch into an analyzed structural model using a camera and computer vision. In this framework, a computer vision object detection stage initially extracts information about the raw features in the image of the beam diagram. Next, a computer vision number-reading model transcribes any handwritten numerals appearing in the image. Then, feature association models are applied to characterize the relationships among the detected features in order to build a comprehensive structural model. Finally, the structural model generated is analyzed using OpenSees. In the system presented, the object detection model achieves a mean average precision of 99.1%, the number-reading model achieves an accuracy of 99.0%, and the models in the feature association stage achieve accuracies ranging from 95.1% to 99.5%. Overall, the tool analyzes 45.0% of images entirely correctly and the remaining 55.0% of images partially correctly. The proposed framework holds promise for other types of structural sketches, such as trusses and frames. Moreover, it can be a valuable tool for structural engineers that is capable of improving the efficiency, safety, and sustainability of future construction projects.

Integration of Finite Element Analysis and Laboratory Analysis on 3D Models for Methodology Calibration.

To better address mechanical behavior, it is necessary to make use of modern tools through which it is possible to run predictions, simulate scenarios, and optimize decisions. sources integration. This will increase the capability of detecting material modifications that forerun damage and/or to forecast the stage in the future when very likely fatigue is initiating and propagating cracks. Early warning outcomes obtained by the synergetic implementation of NDE-based protocols for studying mechanical and fatigue and fracture behavior will enhance the preparedness toward economically sustainable future damage control scenarios. Specifically, these early warning outcomes will be developed in the form of retopologized models to be used coupled with FEA. This paper presents the first stage of calibration and the combination of a system of different sensors (photogrammetry, laser scanning and strain gages) for the creation of volumetric models suitable for the prediction of failure of FEA software. The test objects were two components of car suspension to which strain gauges were attached to measure its deformation under cyclic loading. The calibration of the methodology was carried out using models obtained from photogrammetry and experimental strain gauge measurements.

Tailoring the Structural and Optical Properties of Cerium Oxide Nanoparticles Prepared by an Ecofriendly Green Route Using Plant Extracts.

The present study explores an environmentally friendly green approach to obtain cerium oxide nanoparticles via a biomediated route using Mellisa officinalis and Hypericum perforatum plant extracts as reducing agents. The as-prepared nanoparticles were studied for their structural and morphological characteristics using XRD diffractometry, scanning electron microscopy, Raman, fluorescence and electronic absorption spectra, and X-ray photoelectron spectroscopy (XPS). The XRD pattern has shown the centered fluorite crystal structure of cerium oxide nanoparticles with average crystallite size below 10 nm. These observations were in agreement with the STEM data. The cubic fluorite structure of the cerium oxide nanoparticles was confirmed by the vibrational mode around 462 cm-1 due to the Ce-08 unit. The optical band gap was estimated from UV-Vis reflectance spectra, which was found to decrease from 3.24 eV to 2.98 eV. A higher specific area was determined for the sample using M. officinalis aqueous extract. The EDX data indicated that only cerium and oxygen are present in the green synthesized nanoparticles.

Advances in Mass Spectrometry of Gangliosides Expressed in Brain Cancers.

Gangliosides are highly abundant in the human brain where they are involved in major biological events. In brain cancers, alterations of ganglioside pattern occur, some of which being correlated with neoplastic transformation, while others with tumor proliferation. Of all techniques, mass spectrometry (MS) has proven to be one of the most effective in gangliosidomics, due to its ability to characterize heterogeneous mixtures and discover species with biomarker value. This review highlights the most significant achievements of MS in the analysis of gangliosides in human brain cancers. The first part presents the latest state of MS development in the discovery of ganglioside markers in primary brain tumors, with a particular emphasis on the ion mobility separation (IMS) MS and its contribution to the elucidation of the gangliosidome associated with aggressive tumors. The second part is focused on MS of gangliosides in brain metastases, highlighting the ability of matrix-assisted laser desorption/ionization (MALDI)-MS, microfluidics-MS and tandem MS to decipher and structurally characterize species involved in the metastatic process. In the end, several conclusions and perspectives are presented, among which the need for development of reliable software and a user-friendly structural database as a search platform in brain tumor diagnostics.

Unveiling Novel ERCC1-XPF Complex Inhibitors: Bridging the Gap from In Silico Exploration to Experimental Design.

Modifications in DNA repair pathways are recognized as prognostic markers and potential therapeutic targets in various cancers, including non-small cell lung cancer (NSCLC). Overexpression of ERCC1 correlates with poorer prognosis and response to platinum-based chemotherapy. As a result, there is a pressing need to discover new inhibitors of the ERCC1-XPF complex that can potentiate the efficacy of cisplatin in NSCLC. In this study, we developed a structure-based virtual screening strategy targeting the inhibition of ERCC1 and XPF interaction. Analysis of crystal structures and a library of small molecules known to act against the complex highlighted the pivotal role of Phe293 (ERCC1) in maintaining complex stability. This residue was chosen as the primary binding site for virtual screening. Using an optimized docking protocol, we screened compounds from various databases, ultimately identifying more than one hundred potential inhibitors. Their capability to amplify cisplatin-induced cytotoxicity was assessed in NSCLC H1299 cells, which exhibited the highest ERCC1 expression of all the cell lines tested. Of these, 22 compounds emerged as promising enhancers of cisplatin efficacy. Our results underscore the value of pinpointing crucial molecular characteristics in the pursuit of novel modulators of the ERCC1-XPF interaction, which could be combined with cisplatin to treat NSCLC more effectively.

Quantum Chemical Investigation into the Structural Analysis and Calculated Raman Spectra of Amylose Modeled with Linked Glucose Molecules.

This study presents a quantum chemical investigation into the structural analysis and calculated Raman spectra of modeled amylose with varying units of linked glucose molecules. We systematically examined the rotation of hydroxymethyl groups and intramolecular hydrogen bonds within these amylose models. Our study found that as the number of linked glucose units increases, the linear structure becomes more complex, resulting in curled, cyclic, or helical structures facilitated by establishing various intramolecular interactions. The hydroxymethyl groups were confirmed to form interactions with oxygen atoms and with hydroxymethyl and hydroxyl groups from adjacent rings in the molecular structures. We identified distinct peaks and selected specific bands applicable in various analytical contexts by comparing their calculated Raman spectra. Representative vibrational modes within selected regions were identified across the different lengths of amylose models, serving as characteristic signatures for linear and more coiled structural conformations. Our findings contribute to a deeper understanding of amylose structures and spectroscopic signatures, with implications for theoretical studies and potential applications. This work provides valuable reference points for the detailed assignment of Raman peaks of amylose structure, facilitating their application in broader research on carbohydrate structures and their associated spectroscopic properties.

Phase Identification and Discovery of an Elusive Polymorph of Drug-Polymer Inclusion Complex Using Automated 3D Electron Diffraction.

3D electron diffraction (3D ED) has shown great potential in crystal structure determination in materials, small organic molecules, and macromolecules. In this work, an automated, low-dose and low-bias 3D ED protocol has been implemented to identify six phases from a multiple-phase melt-crystallisation product of an active pharmaceutical ingredient, griseofulvin (GSF). Batch data collection under low-dose conditions using a widely available commercial software was combined with automated data analysis to collect and process over 230 datasets in three days. Accurate unit cell parameters obtained from 3D ED data allowed direct phase identification of GSF Forms III, I and the known GSF inclusion complex (IC) with polyethylene glycol (PEG) (GSF-PEG IC-I), as well as three minor phases, namely GSF Forms II, V and an elusive new phase, GSF-PEG IC-II. Their structures were then directly determined by 3D ED. Furthermore, we reveal how the stabilities of the two GSF-PEG IC polymorphs are closely related to their crystal structures. These results demonstrate the power of automated 3D ED for accurate phase identification and direct structure determination of complex, beam-sensitive crystallisation products, which is significant for drug development where solid form screening is crucial for the overall efficacy of the drug product.