Structure-function Relationships Research Articles

Synergistic oxidation of levofloxacin in electro-peroxone system with enhanced in-situ N, P self-standing carbon cathode derived from Chlorella: In-situ H2O2 generation, peroxone activation and catalytic ozonation

An intrinsic N, P self-standing carbon block (NP SSCB) cathode prepared with Chlorella as a precursor was used to enhance the electro-peroxone (EP) process under acidic conditions through a temperature modulation strategy. The results exhibited that NP SSCB-800-EP had a synergistic effect on levofloxacin (LEV) degradation by 42.1% and produced in situ H2O2 up to 70 mg L−1. Quenching and EPR tests confirmed that ·OH, HO2·/O2·- and 1O2 were responsible for degradation of LEV. The quantitative results showed that EP process produced radicals in the order of 1O2> HO2·/O2·->·OH. X-ray photoelectron spectroscopy (XPS) and density functional theory (DFT) calculations under acidic conditions confirmed that pyridinic N, pyrrolic N, C-O-P and the defects were active sites for in-situ H2O2 production. Furthermore, pyridinic N, pyrrolic N and C-P-O groups contributed to the reaction of H2O2 with O3 to form HO2/O2·- and O3·- intermediates, free from overcoming the limitations associated with traditional HO2− production. Graphitic N and C3-PO were possible active sites for triggering the 1O2 and *Oad production. This work provides a new strategy for the preparation of self-standing carbon-based cathodes and achieves multiple functions of EP process under acidic conditions, presenting new insights into structure-functional relationships in carbon-based advanced oxidation processes.

Revealing binding mechanism of β-casein to chrysin, apigenin, and luteolin and locating its binding pockets by molecular docking and molecular dynamics

Revealing the interaction mechanism of proteins with bioactive molecules and the location of their binding pockets is crucial for predicting the structure-function relationship of proteins in drug discovery and design. Despite some published papers on the interaction of β-casein with small bioactive molecules, the ambiguity of the location and constituent amino acids of β-casein binding pockets prompted us to identify them by in silico simulation of its interaction with three polyphenols, chrysin, apigenin, and luteolin. Molecular docking revealed that the primary β-casein binding pocket for chrysin consists of five nonpolar amino acids (Leu73, Phe77, Pro80, Ile89, and Pro196), three polar neutral amino acids (Ser137, Gln138, and Gln197), and two polar charged amino acids (Glu136, and Arg198). For β-casein/apigenin and β-casein/luteolin complexes, Asn83 also contributes to forming the pocket. Molecular dynamics provided more details, such as the relative contribution of determinative amino acids and the role of various forces. For example, we found that Glu210, Glu132, and Glu35 are the most destructive residues in the binding of chrysin, apigenin, and luteolin to β-casein, respectively. Also, we observed that hydrophobic forces mainly stabilize β-casein/chrysin and β-casein/apigenin, and polar solvation (including hydrogen bonds) stabilizes β-casein/luteolin, all by spontaneous processes.

Cysteines in β-lactoglobulin affects its interfacial adsorption and protein film stabilization

HypothesisDisulfide bonds in proteins are strong chemical bonds forming the secondary and tertiary structure like in the dairy protein β-lactoglobulin. We hypothesize that the partial or complete removal of disulfide bonds affects the structural rearrangement of proteins caused by intra- and intermolecular interactions that in turn define the interfacial activity of proteins at oil/water interfaces. The experimental and numerical investigations contribute to the mechanistic understanding of the structure-function relationship, especially for the interfacial adsorption behavior of proteins. Experimental and numericalSystematically, the 5 cysteines of β-lactoglobulin were recombinantly exchanged by alanine. First, the protein structure of the variants in bulk was analyzed with Fourier-transform-infrared-spectroscopy and molecular dynamic simulations. Second, the structural changes after adsorption to the interface have been also analyzed by molecular dynamic simulations. The adsorption behavior was investigated by pendant drop analysis and the interfacial film properties by dilatational rheology. FindingsThe structural flexibility of β-lactoglobulin with no cysteines encourages its unfolding at the interface, and accelerates the interfacial protein film formation that results in more visco-elastic films in comparison to the reference.

Development of Novel Protein-Rich Foods: Studies of Composite Hydrogels Formed from Potato Proteins and Gellan Gums with Different Degrees of Acylation

The textural attributes of mixed hydrogels formed from potato protein (PP) and gellan gum (GG) were examined for their potential application as novel protein-rich foods (NPFs). Initially, the properties of gels formed by the individual biopolymers were characterized using dynamic shear rheology, compression testing, and confocal fluorescence microscopy. Rheological analysis revealed that PP dispersions formed irreversible heat-set gels, whereas GG formed reversible cold-set gels at lower temperatures. The effects of adding PP to high acyl gellan gum (HA-GG) or low acyl gellan gum (LA-GG) solutions on the microstructure and rheology of the mixed gels formed were then examined. The PP/LA-GG composite gels had a highly heterogeneous microstructure consisting of protein-rich and polysaccharide-rich regions. In contrast, the PP/HA-GG composite gels had a more uniform microstructure. Interestingly, the incorporation of the PP weakened the PP/LA-GG composite gels (antagonism) but strengthened the PP/HA-GG composite gels (synergism), which was mainly attributed to their different impacts on gel microstructure. This study shows that semi-solid plant protein-dietary fiber composites can be formed with a variety of textural attributes by altering the nature and concentration of biopolymers used to formulate them. These mixed gels may be useful for the creation of NPFs. This study also highlights the complexity of the interactions between different food biopolymers and their impacts on food properties, highlighting the need for further research on understanding structure-function relationships in multicomponent biopolymer materials.

Effects of packetization on communication dynamics in brain networks

Abstract Computational studies in network neuroscience build models of communication dynamics in the connectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks can be associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission completion time of propagation strategies when simulating signal propagation in mammalian brain networks. The results show that packetization in the connectome with hubs increases the time of the random walk strategy and does not change that of the shortest path strategy, but decreases that of more plausible strategies for brain networks that balance between communication speed and information requirements. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.

S100A1's single cysteine is an indispensable redox switch for the protection against diastolic calcium waves in cardiomyocytes.

The EF-hand calcium (Ca2+) sensor protein S100A1 combines inotropic with antiarrhythmic potency in cardiomyocytes (CMs). Oxidative posttranslational modification (ox-PTM) of S100A1's conserved, single-cysteine residue (C85) via reactive nitrogen species (i.e., S-nitrosylation or S-glutathionylation) has been proposed to modulate conformational flexibility of intrinsically disordered sequence fragments and to increase the molecule's affinity toward Ca2+. Considering the unknown biological functional consequence, we aimed to determine the impact of the C85 moiety of S100A1 as a potential redox switch. We first uncovered that S100A1 is endogenously glutathionylated in the adult heart in vivo. To prevent glutathionylation of S100A1, we generated S100A1 variants that were unresponsive to ox-PTMs. Overexpression of wild-type (WT) and C85-deficient S100A1 protein variants in isolated CM demonstrated equal inotropic potency, as shown by equally augmented Ca2+ transient amplitudes under basal conditions and β-adrenergic receptor (βAR) stimulation. However, in contrast, ox-PTM defective S100A1 variants failed to protect against arrhythmogenic diastolic sarcoplasmic reticulum (SR) Ca2+ waves and ryanodine receptor 2 (RyR2) hypernitrosylation during βAR stimulation. Despite diastolic performance failure, C85-deficient S100A1 protein variants exerted similar Ca2+-dependent interaction with the RyR2 than WT-S100A1. Dissecting S100A1's molecular structure-function relationship, our data indicate for the first time that the conserved C85 residue potentially acts as a redox switch that is indispensable for S100A1's antiarrhythmic but not its inotropic potency in CMs. We, therefore, propose a model where C85's ox-PTM determines S100A1's ability to beneficially control diastolic but not systolic RyR2 activity.NEW & NOTEWORTHY S100A1 is an emerging candidate for future gene-therapy treatment of human chronic heart failure. We aimed to study the significance of the conserved single-cysteine 85 (C85) residue in cardiomyocytes. We show that S100A1 is endogenously glutathionylated in the heart and demonstrate that this is dispensable to increase systolic Ca2+ transients, but indispensable for mediating S100A1's protection against sarcoplasmic reticulum (SR) Ca2+ waves, which was dependent on the ryanodine receptor 2 (RyR2) nitrosylation status.

Applying Machine Learning to Design Delicate Amorphous Micro-Nano Materials for Rechargeable Batteries

As modern society evolves, the global importance of energy requirements has grown significantly. Thus, exploring new materials for renewable energy storage is urgently needed. Due to its intriguing characteristics, amorphous micro-nanomaterials demonstrate exceptional performance in energy storage. The long experimental periods and high costs associated with traditional methods make it challenging to meet the requirements of materials science. Currently, machine learning (ML) is emerging as a novel research paradigm with the potential to revolutionize the exploration of materials. This review provides an overview of the application of ML in studying amorphous micro-nanomaterials for rechargeable batteries. First, a comprehensive introduction to the fundamentals of amorphous materials and ML is summarized. Then, we highlight a series of representative works to elaborate the relationship between the ML for amorphous nanomaterials. Finally, an insightful perspective on utilization of amorphous materials to establish mature energy storage systems and future directions in ML for amorphous materials are presented. Through this review, we aim to unveil the underlying amorphous structure-function relationship and fuel further research in the design of amorphous materials with outstanding performance across diverse fields.

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper we first introduced an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), which learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, we selected interpolating data points in the learned latent space that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-β 1-42 (Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. This approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.

Mapping Individual Differences in Intermodal Coupling in Neurodevelopment.

Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling. We propose a new method, called CIDeR, which is designed to simultaneously perform hypothesis testing in a way that limits false positive results and improve detection of true positive results. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate the utility of CIDeR in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort.

Electron-Withdrawing Substituents Enhance the Type I PDT and NIR-II Fluorescence of BODIPY J Aggregates for Bioimaging and Cancer Therapy.

Organic dyes with simultaneously boosted near-infrared-II (NIR-II) fluorescence, type I photodynamic therapy (PDT), and photothermal therapy (PTT) in the aggregate state are still elusive due to the unclear structure-function relationship. Herein, electron-withdrawing substituents are introduced at the 5-indolyl positions of BODIPY dyes to form tight J-aggregates for enhanced NIR-II fluorescence and type I PDT/PTT. The introduction of an electron-rich julolidine group at the meso position and an electron-withdrawing substituent (-F) at the indolyl moiety can enhance intermolecular charge transfer and the hydrogen bonding effect, contributing to the efficient generation of superoxide radicals in the aggregate state. The nanoparticles of BDP-F exhibit NIR-II fluorescence at 1000 nm, good superoxide radical generation ability, and a high photothermal conversion efficiency (50.9%), which enabled NIR-II fluorescence-guided vasculature/tumor imaging and additive PDT/PTT. This work provides a strategy for constructing phototheranostic agents with enhanced NIR-II fluorescence and type I PDT/PTT for broad biomedical applications.

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper first an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), is developed that learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, interpolating data points in the learned latent space are selected that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-β1-42 (Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. The proposed approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.

Progress in the Computer-Aided Analysis in Multiple Aspects of Nanocatalysis Research.

Making the utmost of the differences and advantages of multiple disciplines, interdisciplinary integration breaks the science boundaries and accelerates the progress in mutual quests. As an organic connection of material science, enzymology, and biomedicine, nanozyme-related research is further supported by computer technology, which injects in new vitality, and contributes to in-depth understanding, unprecedented insights, and broadened application possibilities. Utilizing computer-aided first-principles method, high-speed and high-throughput mathematic, physic, and chemic models are introduced to perform atomic-level kinetic analysis for nanocatalytic reaction process, and theoretically illustrate the underlying nanozymetic mechanism and structure-function relationship. On this basis, nanozymes with desirable properties can be designed and demand-oriented synthesized without repeated trial-and-error experiments. Besides that, computational analysis and device also play an indispensable role in nanozyme-based detecting methods to realize automatic readouts with improved accuracy and reproducibility. Here, this work focuses on the crossing of nanocatalysis research and computational technology, to inspire the research in computer-aided analysis in nanozyme field to a greater extent.

EEG Ictal Power Dynamics, Function-Structure Associations, and Epilepsy Surgical Outcomes.

Postoperative seizure control in drug-resistant temporal lobe epilepsy (TLE) remains variable, and the causes for this variability are not well understood. One contributing factor could be the extensive spread of synchronized ictal activity across networks. Our study used novel quantifiable assessments from intracranial EEG (iEEG) to test this hypothesis and investigated how the spread of seizures is determined by underlying structural network topological properties. We evaluated iEEG data from 157 seizures in 27 patients with TLE: 100 seizures from 17 patients with postoperative seizure control (Engel score I) vs 57 seizures from 10 patients with unfavorable surgical outcomes (Engel score II-IV). We introduced a quantifiable method to measure seizure power dynamics within anatomical regions, refining existing seizure imaging frameworks and minimizing reliance on subjective human decision-making. Time-frequency power representations were obtained in 6 frequency bands ranging from theta to gamma. Ictal power spectrums were normalized against a baseline clip taken at least 6 hours away from ictal events. Electrodes' time-frequency power spectrums were then mapped onto individual T1-weighted MRIs and grouped based on a standard brain atlas. We compared spatiotemporal dynamics for seizures between groups with favorable and unfavorable surgical outcomes. This comparison included examining the range of activated brain regions and the spreading rate of ictal activities. We then evaluated whether regional iEEG power values were a function of fractional anisotropy (FA) from diffusion tensor imaging across regions over time. Seizures from patients with unfavorable outcomes exhibited significantly higher maximum activation sizes in various frequency bands. Notably, we provided quantifiable evidence that in seizures associated with unfavorable surgical outcomes, the spread of beta-band power across brain regions is significantly faster, detectable as early as the first second after seizure onset. There was a significant correlation between beta power during seizures and FA in the corresponding areas, particularly in the unfavorable outcome group. Our findings further suggest that integrating structural and functional features could improve the prediction of epilepsy surgical outcomes. Our findings suggest that ictal iEEG power dynamics and the structural-functional relationship are mechanistic factors associated with surgical outcomes in TLE.

Consistent decrease in conifer embolism resistance from the stem apex to base resulting from axial trends in tracheid and pit traits.

Drought-induced embolism formation in conifers is associated with several tracheid and pit traits, which vary in parallel from stem apex to base. We tested whether this axial anatomical variability is associated with a progressive variation in embolism vulnerability along the stem from apex to base. We assessed the tracheid hydraulic diameter (Dh), mean pit membrane area (PMA) and the xylem pressure at 50% loss of conductivity (P50) on longitudinal stem segments extracted at different distances from the stem apex (DFA) in a Picea abies and an Abies alba tree. In both trees, Dh and PMA scaled with DFA 0.2. P50 varied for more than 3 MPa from the treetop to the stem base, according to a scaling of -P50 with DFA-0.2 . The largest Dh, PMA and P50 variation occurred for DFA<1.5 m. PMA and Dh scaled more than isometrically (exponent b=1.2). Pit traits vary proportionally with tracheid lumen diameter. Apex-to-base trends in tracheid and pit traits, along with variations in P50, suggest a strong structure-function relationship that is influenced by DFA. Although the effect of DFA on P50 has not been extensively explored previously, we propose that analyzing the relationship between P50 and DFA could be crucial for a comprehensive assessment of embolism vulnerability at the individual level.

Biomimetic Approaches in Scaffold-Based Blood Vessel Tissue Engineering.

Cardiovascular diseases remain a leading cause of mortality globally, with atherosclerosis representing a significant pathological means, often leading to myocardial infarction. Coronary artery bypass surgery, a common procedure used to treat coronary artery disease, presents challenges due to the limited autologous tissue availability or the shortcomings of synthetic grafts. Consequently, there is a growing interest in tissue engineering approaches to develop vascular substitutes. This review offers an updated picture of the state of the art in vascular tissue engineering, emphasising the design of scaffolds and dynamic culture conditions following a biomimetic approach. By emulating native vessel properties and, in particular, by mimicking the three-layer structure of the vascular wall, tissue-engineered grafts can improve long-term patency and clinical outcomes. Furthermore, ongoing research focuses on enhancing biomimicry through innovative scaffold materials, surface functionalisation strategies, and the use of bioreactors mimicking the physiological microenvironment. Through a multidisciplinary lens, this review provides insight into the latest advancements and future directions of vascular tissue engineering, with particular reference to employing biomimicry to create systems capable of reproducing the structure-function relationships present in the arterial wall. Despite the existence of a gap between benchtop innovation and clinical translation, it appears that the biomimetic technologies developed to date demonstrate promising results in preventing vascular occlusion due to blood clotting under laboratory conditions and in preclinical studies. Therefore, a multifaceted biomimetic approach could represent a winning strategy to ensure the translation of vascular tissue engineering into clinical practice.

Exploring the Structurally Conserved Regions and Functional Significance in Bacterial N-Terminal Nucleophile (Ntn) Amide-Hydrolases.

The initial adoption of penicillin as an antibiotic marked the start of exploring other compounds essential for pharmaceuticals, yet resistance to penicillins and their side effects has compromised their efficacy. The N-terminal nucleophile (Ntn) amide-hydrolases S45 family plays a key role in catalyzing amide bond hydrolysis in various compounds, including antibiotics like penicillin and cephalosporin. This study comprehensively analyzes the structural and functional traits of the bacterial N-terminal nucleophile (Ntn) amide-hydrolases S45 family, covering penicillin G acylases, cephalosporin acylases, and D-succinylase. Utilizing structural bioinformatics tools and sequence analysis, the investigation delineates structurally conserved regions (SCRs) and substrate binding site variations among these enzymes. Notably, sixteen SCRs crucial for substrate interaction are identified solely through sequence analysis, emphasizing the significance of sequence data in characterizing functionally relevant regions. These findings introduce a novel approach for identifying targets to enhance the biocatalytic properties of N-terminal nucleophile (Ntn) amide-hydrolases, while facilitating the development of more accurate three-dimensional models, particularly for enzymes lacking structural data. Overall, this research advances our understanding of structure-function relationships in bacterial N-terminal nucleophile (Ntn) amide-hydrolases, providing insights into strategies for optimizing their enzymatic capabilities.

From Voxels to Physiology: A Review of Diffusion Magnetic Resonance Imaging Applications in Skeletal Muscle.

Skeletal muscle has a classic structure function relationship; both skeletal muscle microstructure and architecture are directly related to force generating capacity. Biopsy, the gold standard for evaluating muscle microstructure, is highly invasive, destructive to muscle, and provides only a small amount of information about the entire volume of a muscle. Similarly, muscle fiber lengths and pennation angles, key features of muscle architecture predictive of muscle function, are traditionally studied via cadaveric dissection. Noninvasive techniques such as diffusion magnetic resonance imaging (dMRI) offer quantitative approaches to study skeletal muscle microstructure and architecture. Despite its prevalence in applications for musculoskeletal research, clinical adoption is hindered by a lack of understanding regarding its sensitivity to clinically important biomarkers such as muscle fiber cross-sectional area. This review aims to elucidate how dMRI has been utilized to study skeletal muscle, covering fundamentals of muscle physiology, dMRI acquisition techniques, dMRI modeling, and applications where dMRI has been leveraged to noninvasively study skeletal muscle changes in response to disease, aging, injury, and human performance. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Elucidating the Enzymatic Mechanism of Dihydrocoumarin Degradation: Insight into the Functional Evolution of Methyl-Parathion Hydrolase from QM/MM and MM MD Simulations.

Methyl-parathion hydrolase (MPH), which evolved from dihydrocoumarin hydrolase, offers one of the most efficient enzymes for the hydrolysis of methyl-parathion. Interestingly, the substrate preference of MPH shifts from the methyl-parathion to the lactone dihydrocoumarin (DHC) after its mutation of five specific residues (R72L, L273F, L258H, T271I, and S193Δ, m5-MPH). Here, extensive QM/MM calculations and MM MD simulations have been used to delve into the structure-function relationship of MPH enzymes and plausible mechanisms for the chemical and nonchemical steps, including the transportation and binding of the substrate DHC to the active site, the hydrolysis reaction, and the product release. The results reveal that the five mutations remodel the active pocket and reposition DHC within the active site, leading to stronger enzyme-substrate interactions. The MM/GBSA-estimated binding free energies are about -20.7 kcal/mol for m5-MPH and -17.1 kcal/mol for wild-type MPH. Furthermore, this conformational adjustment of the protein may facilitate the chemical step of DHC hydrolysis and the product release, although there is a certain influence on the substrate transport. The hydrolytic reaction begins with the nucleophilic attack of the bridging OH- with the energy barriers of 22.0 and 18.0 kcal/mol for the wild-type and m5-MPH enzymes, respectively, which is rate-determining for the entire process. Unraveling these mechanistic intricacies may help in the understanding of the natural evolution of enzymes for diverse substrates and establish the enzyme structure-function relationship.

Mapping the structural landscape of the yeast Ty3 retrotransposon RNA genome.

Long terminal repeat (LTR)-retrotransposons are significant contributors to the evolution and diversity of eukaryotic genomes. Their RNA genomes (gRNA) serve as a template for protein synthesis and reverse transcription to a DNA copy, which can integrate into the host genome. Here, we used the SHAPE-MaP strategy to explore Ty3 retrotransposon gRNA structure in yeast and under cell-free conditions. Our study reveals the structural dynamics of Ty3 gRNA and the well-folded core, formed independently of the cellular environment. Based on the detailed map of Ty3 gRNA structure, we characterized the structural context of cis-acting sequences involved in reverse transcription and frameshifting. We also identified a novel functional sequence as a potential initiator for Ty3 gRNA dimerization. Our data indicate that the dimer is maintained by direct interaction between short palindromic sequences at the 5' ends of the two Ty3 gRNAs, resembling the model characteristic for other retroelements like HIV-1 and Ty1. This work points out a range of cell-dependent and -independent Ty3 gRNA structural changes that provide a solid background for studies on RNA structure-function relationships important for retroelement biology.

Dynamic Interchain Motion in 1D Tetrathiafulvalene-Based Coordination Polymers for Highly Sensitive Molecular Recognition.

The application of electrically conductive 1D coordination polymers (1D CPs) in nanoelectronic molecular recognition is theoretically promising yet rarely explored due to the challenges in their synthesis and optimization of electrical properties. In this regard, two tetrathiafulvalene-based 1D CPs, namely [Co(m-H2TTFTB)(DMF)2(H2O)]n (Co-m-TTFTB), and {[Ni(m-H2TTFTB)(CH3CH2OH)1.5(H2O)1.5]·(H2O)0.5}n (Ni-m-TTFTB) are successfully constructed. The shorter S···S contacts between the [M(solvent)3(m-H2TTFTB)]n chains contribute to a significant improvement in their electrical conductivities. The powder X-ray diffraction (PXRD) under different organic solvents reveals the flexible and dynamic structural characteristic of M-m-TTFTB, which, combined with the 1D morphology, lead to their excellent performance for sensitive detection of volatile organic compounds. Co-m-TTFTB achieves a limit of detection for ethanol vapor down to 0.5ppm, which is superior to the state-of-the-art chemiresistive sensors based on metal-organic frameworks or organic polymers at room temperature. In situ diffuse reflectance infrared Fourier transform spectroscopy, PXRD measurements and density functional theory calculations reveal the molecular insertion sensing mechanism and the corresponding structure-function relationship. This work expands the applicable scenario of 1D CPs and opens a new realm of 1D CP-based nanoelectronic sensors for highly sensitive room temperature gas detection.