Folding Characteristics Research Articles

Dynamic barriers modulate cohesin positioning and genome folding at fixed occupancy.

In mammalian interphase cells, genomes are folded by cohesin loop extrusion limited by directional CTCF barriers. This interplay leads to the enrichment of cohesin at barriers, isolation between neighboring topologically associating domains, and elevated contact frequency between convergent CTCF barriers across the genome. However, recent in vivo measurements present a puzzle: reported residence times for CTCF on chromatin are in the range of a few minutes, while lifetimes for cohesin are much longer. Can the observed features of genome folding result from the action of relatively transient barriers? To address this question, we developed a dynamic barrier model, where CTCF sites switch between bound and unbound states with rates that can be directly compared with biophysical measurements. Using this model, we investigated how barrier dynamics would impact observables for a range of experimental genomic and imaging datasets, including ChIP-seq, Hi-C, and microscopy. We found the interplay of CTCF and cohesin binding timescales influence the strength of each of these features, leaving a signature of barrier dynamics even in the population-averaged snapshots offered by genomic datasets. First, in addition to barrier occupancy, barrier bound times are crucial for instructing features of genome folding. Second, the ratio of boundary to extruder lifetime greatly alters simulated ChIP-seq and simulated Hi-C. Third, large-scale changes in chromosome morphology observed experimentally after increasing extruder lifetime require dynamic barriers. By integrating multiple sources of experimental data, our biophysical model argues that CTCF barrier bound times effectively approach those of cohesin extruder lifetimes. Together, we demonstrate how models that are informed by biophysically measured protein dynamics broaden our understanding of genome folding.

Polymer Physics Models Reveal Structural Folding Features of Single-Molecule Gene Chromatin Conformations.

Here, we employ polymer physics models of chromatin to investigate the 3D folding of a 2 Mb wide genomic region encompassing the human LTN1 gene, a crucial DNA locus involved in key cellular functions. Through extensive Molecular Dynamics simulations, we reconstruct in silico the ensemble of single-molecule LTN1 3D structures, which we benchmark against recent in situ Hi-C 2.0 data. The model-derived single molecules are then used to predict structural folding features at the single-cell level, providing testable predictions for super-resolution microscopy experiments.

Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features.

Many vaccine design programs have been developed, including our own machine learning approaches Vaxign-ML and Vaxign-DL. Using deep learning techniques, Vaxign-DL predicts bacterial protective antigens by calculating 509 biological and biomedical features from protein sequences. In this study, we first used the protein folding ESM program to calculate a set of 1,280 features from individual protein sequences, and then utilized the new set of features separately or in combination with the traditional set of 509 features to predict protective antigens. Our result showed that the usage of ESM-derived features alone was able to accurately predict vaccine antigens with a performance similar to the orginal Vaxign-DL prediction method, and the usage of the combined ESM-derived and orginal Vaxign-DL features significantly improved the prediction performance according to a set of seven scores including specificity, sensitivity, and AUROC. To further evaluate the updated methods, we conducted a Leave-One-Pathogen-Out Validation (LOPOV) study, and found that the usage of ESM-derived features significantly improved the the prediction of vaccine antigens from 10 bacterial pathogens. This research is the first reported study demonstrating the added value of protein folding features for vaccine antigen prediction.

Dominant Chemical Interactions Governing the Folding Mechanism of Oligopeptides.

The hydrophobic effect is the main factor that drives the folding of polypeptide chains. In this study, we have examined the influence of the hydrophobic effect in the context of the main mechanical forces approach, mainly in relation to the establishment of specific interplays, such as hydrophobic and CH-π cloud interactions. By adopting three oligopeptides as model systems to assess folding features, we demonstrate herein that these finely tuned interactions dominate over electrostatic interactions, including H-bonds and electrostatic attractions/repulsions. The folding mechanism analysed here demonstrates cooperation at the single-residue level, for which we propose the terminology of "single residues cooperative folding". Overall, hydrophobic and CH-π cloud interactions produce the main output of the hydrophobic effect and govern the folding mechanism, as demonstrated in this study with small polypeptide chains, which in turn represent the main secondary structures in proteins.

Design and analysis of rigid-elastic coupling origami flashers with bistable characteristics

Origami flashers are frequently used in the design of deployable structures due to their large storage ratio and simple structure. However, origami flashers are not foldable without the flexible deformation of the structural components. On the other hand, conventional origami flashers could not be maintained in the deployed configuration with enough supporting stiffness because of the flexibility of the facets and creases, which limits their practical applications. By analyzing the mobility of the rigid origami flashers, we find that the origami flashers have a bistability-like property. Based on this, we propose a method to design rigid-elastic coupling origami flashers with bistable characteristics. The rigid-elastic coupling origami flashers consist of rigid facets, zero-stiffness creases, and elastic ropes. The bistable folding characteristics are analyzed by theoretical models, and demonstrated by numerical methods and experiments. Such bistable characteristics allow for the deployment of the origami with less energy consumption, while achieving greater supporting stiffness in the deployed configuration. The effect of different parameters on the dynamic performance is also investigated. A fabrication method of the novel system is proposed and verified by a physical prototype. The rigid-elastic coupling origami flashers facilitate their potential applications in space engineering.

Peptides En Route from Prebiotic to Biotic Catalysis.

ConspectusIn the quest to understand prebiotic catalysis, different molecular entities, mainly minerals, metal ions, organic cofactors, and ribozymes, have been implied as key players. Of these, inorganic and organic cofactors have gained attention for their ability to catalyze a wide array of reactions central to modern metabolism and frequently participate in these reactions within modern enzymes. Nevertheless, bridging the gap between prebiotic and modern metabolism remains a fundamental question in the origins of life.In this Account, peptides are investigated as a potential bridge linking prebiotic catalysis by minerals/cofactors to enzymes that dominate modern life's chemical reactions. Before ribosomal synthesis emerged, peptides of random sequences were plausible on early Earth. This was made possible by different sources of amino acid delivery and synthesis, as well as their condensation under a variety of conditions. Early peptides and proteins probably exhibited distinct compositions, enriched in small aliphatic and acidic residues. An increase in abundance of amino acids with larger side chains and canonical basic groups was most likely dependent on the emergence of their more challenging (bio)synthesis. Pressing questions thus arise: how did this composition influence the early peptide properties, and to what extent could they contribute to early metabolism?Recent research from our group and colleagues shows that highly acidic peptides/proteins comprising only the presumably "early" amino acids are in fact competent at secondary structure formation and even possess adaptive folding characteristics such as spontaneous refoldability and chaperone independence to achieve soluble structures. Moreover, we showed that highly acidic proteins of presumably "early" composition can still bind RNA by utilizing metal ions as cofactors to bridge carboxylate and phosphoester functional groups. And finally, ancient organic cofactors were shown to be capable of binding to sequences from amino acids considered prebiotically plausible, supporting their folding properties and providing functional groups, which would nominate them as catalytic hubs of great prebiotic relevance.These findings underscore the biochemical plausibility of an early peptide/protein world devoid of more complex amino acids yet collaborating with other catalytic species. Drawing from the mechanistic properties of protein-cofactor catalysis, it is speculated here that the early peptide/protein-cofactor ensemble could facilitate a similar range of chemical reactions, albeit with lower catalytic rates. This hypothesis invites a systematic experimental test.Nonetheless, this Account does not exclude other scenarios of prebiotic-to-biotic catalysis or prioritize any specific pathways of prebiotic syntheses. The objective is to examine peptide availability, composition, and functional potential among the various factors involved in the emergence of early life.

Polymer physics models reveal structural folding features of single-molecule gene chromatin conformations.

Here, we employ polymer physics models of chromatin to investigate the 3D folding of a 2Mb wide genomic region encompassing the human LTN1 gene, a crucial DNA locus involved in key cellular functions. Through extensive Molecular Dynamics simulations, we reconstruct in-silico the ensemble of single-molecule LTN1 3D structures, which we benchmark against recent in-situ Hi-C 2.0 data. The model-derived single molecules are then used to predict structural folding features at the single-cell level, providing testable predictions for super-resolution microscopy experiments.

A structural and biochemical approach to effect of temperature and pH on the Molten Globule phenomenon in a thermophilic protease: Molecular dynamics simulation, fluorescence spectroscopy and circular dichroism

Proteins in the molten globule (MG) stage have a reasonably stable secondary structure content, but changes in side-chain packing cause a noticeable decrease in the number of tertiary structures. Here, we investigated serine protease as a suitable model to study the folding route and characteristics of a protein in the MG state. Upon induction with IPTG, the protein exhibited a molecular weight of 42 kDa and achieved a final concentration of 1.7 mg/mL after the concentration process. Serine protease's intrinsic and extrinsic fluorescence was examined in the pH range of 1–12 using fluorescence spectroscopy with maximum intrinsic fluorescence in 335 nm and a blue shift in 510 nm for extrinsic fluorescence. It was suggested that the protein transitions probably to an MG state at pH 3. Evidences showed that serine protease's secondary structure using far-UV circular dichroism (CD) showed a little shift in the MG state intermediate, pH 3, relative to the original state, pH 8, which is typical for MG. Maximum decreases was observed in 228 to 300 nm for UV-near CD. Also revealed a notable alteration in the pH 3 of the enzyme compared to pH 8, which is the pH at which the protein takes on its natural structure. By using acrylamide to quench the fluorescence, the Stern-Volmer constant (KSV) was determined at pH values of 8 and 3, yielding values of 20 and 14 M−1, respectively. This is likely correlated with an increase in the enzyme's molecular volume in the MG state. Molecular dynamics (MD) simulation showed that although the thermal stability of this enzyme at 90° was confirmed in the Root-Mean-Square-Deviation (RMSD), Root-Mean-Square-Fluctuation (RMSF), Radius of gyration (Rg) as well as Solvent-Accessible-Surface-Area (SASA) graphs, but pH 3 may significantly disturb the tertiary structure and secondary structure. These in-silico results may also be analyzed in line with the occurrence of MG at pH 3.

Principles of bacterial genome organization, a conformational point of view.

Bacterial chromosomes are large molecules that need to be highly compacted to fit inside the cells. Chromosome compaction must facilitate and maintain key biological processes such as gene expression and DNA transactions (replication, recombination, repair, and segregation). Chromosome and chromatin 3D-organization in bacteria has been a puzzle for decades. Chromosome conformation capture coupled to deep sequencing (Hi-C) in combination with other "omics" approaches has allowed dissection of the structural layers that shape bacterial chromosome organization, from DNA topology to global chromosome architecture. Here we review the latest findings using Hi-C and discuss the main features of bacterial genome folding.

Late Pleistocene Fault Slip Rate within the Bole Basin: Insights into deformation kinematics in the Central Tian Shan

Tian Shan is a vast and highly seismically active intracontinental mountain range. GPS measurements and field studies have shown that E-W and NW-SE trending thrusts and fault-related folds are distributed across central Tian Shan. However, few studies have determined the fault and fault-related-fold activity in the Bole Basin, the northernmost part of central Tian Shan. In this study, we focus on the Alashak Fold, which is situated along the southern margin of the Bole Basin. Using high-resolution uncrewed aerial vehicle-Digital Elevation Model data, field observations, and detailed mapping, we determined that the Alashak Fold is characterized by a trishear fault-propagation fold. Along the Alashak River, we identified five levels of fluvial surfaces, the T3 terrace was continuously preserved across the fold. Radiocarbon and surficial 10Be dating were used to determine the exposure age of the deformed T3 terrace. The folding characteristics of T3, trishear inversed modeling, and abandonment age indicate that the Alashak Fault has an uplift rate of 0.8 ± 0.3 mm/yr, a dip-slip rate of 4.2 ± 1.2 mm/yr, and a crustal shortening rate of 4.0 ± 1.2 mm/yr since the late Pleistocene, respectively. The Alashak and Latgan Faults intersect at a depth of 1.2 km, enclosing a wedge-shaped block. Our study emphasizes that tectonic activity along the southern margin of the Bole Basin has played a significant role in north-south shortening within central Tian Shan, as measured by geological measurements since the late Pleistocene. The Alashak Fault can potentially generate moderate–strong (Mw 5.0–7.0) earthquakes, while the Alashak and Latgan Faults can generate major (Mw 7.2) earthquakes.

Revealing the Effect of Stereocontrol on Intermolecular Interactions between Abiotic, Sequence-Defined Polyurethanes and a Ligand.

The development of precision polymer synthesis has facilitated access to a diverse library of abiotic structures wherein chiral monomers are positioned at specific locations within macromolecular chains. These structures are anticipated to exhibit folding characteristics similar to those of biotic macromolecules and possess comparable functionalities. However, the extensive sequence space and numerous variables make selecting a sequence with the desired function challenging. Therefore, revealing sequence-function dependencies and developing practical tools are necessary to analyze their conformations and molecular interactions. In this study, we investigate the effect of stereochemistry, which dictates the spatial location of backbone and pendant groups, on the interaction between sequence-defined oligourethanes and bisphenol A ligands. Various methods are explored to analyze the receptor-like properties of model oligomers and the ligand. The accuracy of molecular dynamics simulations and experimental techniques is assessed to uncover the impact of discrete changes in stereochemical arrangements on the structures of the resulting complexes and their binding strengths. Detailed computational investigations providing atomistic details show that the formed complexes demonstrate significant structural diversity depending on the sequence of stereocenters, thus affecting the oligomer-ligand binding strength. Among the tested techniques, the fluorescence spectroscopy data, fitted to the Stern-Volmer equation, are consistently aligned with the calculations, thus validating the developed simulation methodology. The developed methodology opens a way to engineer the structure of sequence-defined oligomers with receptor-like functionality to explore their practical applications, e.g., as sensory materials.

Higher surface folding of the human premotor cortex is associated with better long-term learning capability

The capacity to learn enabled the human species to adapt to various challenging environmental conditions and pass important achievements on to the next generation. A growing body of research suggests links between neocortical folding properties and numerous aspects of human behavior, but their impact on enhanced human learning capacity remains unexplored. Here we leverage three training cohorts to demonstrate that higher levels of premotor cortical folding reliably predict individual long-term learning gains in a challenging new motor task, above and beyond initial performance differences. Individual folding-related predisposition to motor learning was found to be independent of cortical thickness and intracortical microstructure, but dependent on larger cortical surface area in premotor regions. We further show that learning-relevant features of cortical folding occurred in close spatial proximity to practice-induced structural brain plasticity. Our results suggest a link between neocortical surface folding and human behavioral adaptability.

Design and analysis of a solid surface deployable antenna mechanism based on flasher rigid origami

This study proposes a mechanism for solid surface deployable antennas based on flasher rigid origami. The mechanism scheme design and degree-of-freedom analysis were conducted utilizing a Kirigami release constraint strategy. Subsequently, a spatial geometric model of the key points was established incorporating the folding and unfolding characteristics and geometric relationships within the mechanism. Furthermore, a comprehensive 3D model of the antenna was developed by determining the hinge locations, geometric parameters, and driving mechanism, followed by a kinematic simulation analysis. Finally, a prototype was constructed, and a microgravity deployment experiment was conducted to verify the feasibility of the mechanism.

Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals

The detection of Parkinson’s disease (PD) is vital as it affects the population worldwide and decreases the quality of life. The disability and death rate due to PD is increasing at an unprecedented rate, more than any other neurological disorder. To this date, no diagnostic procedures exist for this disease. However, several computational approaches have proven successful in detecting PD at early stages, overcoming the disadvantages of traditional methods of diagnosis. In this study, a machine learning (ML) detection system based on the voice signals of PD patients is proposed. The AdaBoost classifier has been utilized to construct the model and trained on a dataset obtained from the machine learning repository of the University of California, Irvine (UCI). This dataset includes voice attributes such as time-frequency features, Mel frequency cepstral coefficients, wavelet transform features, vocal fold features, and tremor waveform quality time. The model demonstrated promising performance, achieving high accuracy, precision, recall, F1 score, and AUC score of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. Furthermore, the robustness of the proposed model is rigorously assessed through cross-validation, revealing consistent performance across all iterations. The overarching objective of this study is to contribute to the scientific community by furnishing a robust system for the detection of PD.

Exploring the Potential of Deep Learning in the Classification and Early Detection of Parkinson's Disease

INTRODUCTION: Parkinson's Disease (PD) is a progressive neurological disorder affecting a significant portion of the global population, leading to profound impacts on daily life and imposing substantial burdens on healthcare systems. Early identification and precise classification are crucial for effectively managing this disease. This research investigates the potential of deep learning techniques in facilitating early recognition and accurate classification of PD.
 OBJECTIVES: The primary objective of this study is to leverage advanced deep learning techniques for the early detection and precise classification of Parkinson's Disease. By utilizing a rich dataset comprising speech signal features extracted from 3000 PD patients, including Time Frequency Features, Mel Frequency Cepstral Coefficients (MFCCs), Wavelet Transform based Features, Vocal Fold Features, and TWQT features, this research aims to evaluate the performance of various deep learning models in PD classification.
 METHODS: The dataset containing diverse speech signal features from PD patients' recordings serves as the foundation for training and evaluating five different deep learning models: ResNet50, VGG16, Inception v2, AlexNet, and VGG19. Each model undergoes training and assessment to determine its capability in accurately classifying PD patients. Performance metrics such as accuracy are employed to evaluate the models' effectiveness.
 RESULTS: The results demonstrate promising potential, with overall accuracies ranging from 89% to 95% across the different deep learning models. Notably, AlexNet emerges as the top-performing model, achieving an accuracy of 95% and demonstrating balanced performance in accurately identifying both true and false PD cases.
 CONCLUSION: This research highlights the significant potential of deep learning in facilitating the early detection and classification of Parkinson's Disease. Leveraging speech signal features offers a non-invasive and cost-effective approach to PD assessment. The findings contribute to the growing body of evidence supporting the integration of artificial intelligence in healthcare, particularly in the realm of neurodegenerative disorders. Further exploration into the application of deep learning in this domain holds promise for advancing PD diagnosis and management.

Post-translational modification sites are present in hydrophilic cavities of alpha-synuclein, tau, FUS, and TDP-43 fibrils: A molecular dynamics study.

Hydration plays a crucial role in the refolding of intrinsically disordered proteins into amyloid fibrils; however, the specific interactions between water and protein that may contribute to this process are still unknown. In our previous studies of alpha-synuclein (aSyn), we have shown that waters confined in fibril cavities are stabilizing features of this pathological fold; and that amino acids that hydrogen bond with these confined waters modulate primary and seeded aggregation. Here, we extend our aSyn molecular dynamics (MD) simulations with three new polymorphs and correlate MD trajectory information with known post-translational modifications (PTMs) and experimental data. We show that cavity residues are more evolutionarily conserved than non-cavity residues and are enriched with PTM sites. As expected, the confinement within hydrophilic cavities results in more stably hydrated amino acids. Interestingly, cavity PTM sites display the longest protein-water hydrogen bond lifetimes, three-fold greater than non-PTM cavity sites. Utilizing the deep mutational screen dataset by Newberry et al. and the Thioflavin T aggregation review by Pancoe et al. parsed using a fibril cavity/non-cavity definition, we show that hydrophobic changes to amino acids in cavities have a larger effect on fitness and aggregation rate than residues outside cavities, supporting our hypothesis that these sites are involved in the inhibition of aSyn toxic fibrillization. Finally, we expand our study to include analysis of fibril structures of tau, FUS, TDP-43, prion, and hnRNPA1; all of which contained hydrated cavities, with tau, FUS, and TDP-43 recapitulating our PTM results in aSyn fibril cavities.

Identification of rare cortical folding patterns using unsupervised deep learning

Abstract Like fingerprints, cortical folding patterns are unique to each brain even though they follow a general species-specific organization. Some folding patterns have been linked with neurodevelopmental disorders. However, due to the high inter-individual variability, the identification of rare folding patterns that could become biomarkers remains a very complex task. This paper proposes a novel unsupervised deep learning approach to identify rare folding patterns and assess the degree of deviations that can be detected. To this end, we preprocess the brain MR images to focus the learning on the folding morphology and train a beta variational auto-encoder (β−VAE) on the inter-individual variability of the folding to identify outliers. We compare the detection power of the latent space and of the reconstruction errors, using synthetic benchmarks and one actual rare configuration related to the central sulcus. Finally, we assess the generalization of our method on a developmental anomaly located in another region and we validate the relevance of our approach on patients suffering from drug-resistant epilepsy. Our results suggest that this method enables encoding relevant folding characteristics that can be enlightened and better interpreted based on the generative power of the β−VAE. The latent space and the reconstruction errors bring complementary information and enable the identification of rare patterns of different nature. This method generalizes well to a different region on another dataset and demonstrates promising results on the epileptic patients. Code is available at https://github.com/neurospin-projects/2022_lguillon_rare_folding_detection.

Bottom-up data integration in polymer models of chromatin organisation

Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus. Despite the availability of large data sets probing this process from multiple angles, bottom-up frameworks which allow the incorporation of the sequence-specific nature of biochemistry in a unified model of 3D chromatin structure remain scarce. Here we propose SEMPER (Sequence Enhanced Magnetic PolymER), a novel stochastic polymer model which naturally incorporates observational data about sequence-driven biochemical processes, such as binding of transcription factor proteins, in a 3D model of chromatin structure. We introduce a novel approximate Bayesian algorithm to quantify a posteriori the relative importance of various factors, including the polymeric nature of DNA, in determining chromatin epigenetic state, thus providing a transparent way to generate biological hypotheses. While accurate prediction of contact frequencies (a problem already extensively studied in the literature) is not our main aim, as a byproduct of the inference procedure and without additional input from the genome 3D structure, our model can predict with reasonable accuracy some notable and nontrivial conformational features of chromatin folding within the nucleus. Our work highlights the importance of introducing physically realistic statistical models for predicting chromatin states from epigenetic data and opens the way to a new class of more systematic approaches to interpreting epigenomic data.

Enhancing the Toughness and Strength of Polymers Using Mechanically Interlocked Hydrogen Bonds.

The energy dissipative features of hydrogen bonds under conditions of mechanical strain have provided an ongoing incentive to explore hydrogen bonding units for the purpose of controlling and customizing the mechanical properties of polymeric materials. However, there remains a need for hydrogen bond units that (1) possess directionality, (2) provide selectivity, (3) dissipate energy effectively, and (4) can be incorporated readily into polymeric materials to regulate their mechanical properties. Here, we report mechanically interlocked hydrogen bond units that incorporate multiple hydrogen bonds within a [2]catenane structure. The conformational flexibility and associated spatial folding characteristics of the [2]catenane units allow for molecular scale motion under external stress, while the interlocked structure serves as a pivot that maintains the directionality and selectivity of the resultant hydrogen bonding units. When incorporated into polymers, these interlocked hydrogen bond motifs serve to strengthen and toughen the resulting materials. This study not only presents a novel hydrogen bond unit for creating polymeric materials with improved mechanical properties but also underscores the unique opportunities that mechanically interlocked hydrogen bond structures may provide across a diverse range of applications.

Dynamical reverse folding and residual gas expansion models of flexible thin films

To examine the folding and deployment characteristics of flexible thin film structures in a vacuum environment, a dynamic reverse folding method based on the target structure and an inflation-based deployment method based on the residual gas expansion are proposed. The dynamic characteristics of the flexible thin film structure during the folding process are characterized using the finite element method. Further, the residual gas is used to inflate and unfold the folded structure, and the response characteristics of gas pressure and film volume are obtained. Compared with the thin film unfolding test in a vacuum tank, for a spherical thin film of the same size, under the action of residual gas at 0.05 MPa, when unfolding for 0.34 s, the unfolded volume of the thin film is obtained to be approximately 0.0367 m3, reaching 90.2% of the total unfolded volume of the spherical film in the experiment. Overall, the proposed methods can serve as a design reference for the analysis and optimization of flexible thin films.