Flexible Macromolecules Research Articles

Gallic acid melanin pigment hydrogel as a flexible macromolecule for articular motion sensing

In this study, water-soluble melanin was synthesized through the genetic recombination of Escherichia coli using gallic acid as a substrate. The recombinant host produced 2.83 g/L of gallic acid-based melanin (GA melanin) from 20 mM gallic acid. Notably, the isolated GA melanin demonstrated exceptional antioxidant and antimicrobial activities, exhibiting a 25.7 % inhibition ratio against Candida albicans. The structure and composition of GA melanin were analyzed using Fourier-transform infrared (FT-IR) spectroscopy, scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and X-ray diffraction (XRD). Remarkably, GA melanin displayed high thermal stability, maintaining integrity up to 1000 °C. Additionally, it exhibited unique electrical properties in terms of conductivity and resistivity compared to other common types of melanin. Subsequently, GA melanin was cross-linked with hydrogel to create a sensing template. The resulting GA melanin hydrogel demonstrated lower resistance (80.08 ± 3.0 kohm) compared to conventional hydrogels (108.62 ± 10.4 kohm), indicating an approximately 1.77-fold improvement in adhesion. Given its physical, biological, and electrical properties, the GA melanin hydrogel was further utilized as a flexible motion-sensing material to detect resistivity changes induced by knee, wrist, and finger bending, as well as vocal cord vibrations. In all cases, the sensing module displayed notable sensitivity to motion-induced resistivity variations.

Entanglements of Macromolecules and Their Influence on Rheological and Mechanical Properties of Polymers.

Flexible macromolecules easily become entangled with neighboring macromolecules. The resulting network determines many polymer properties, including rheological and mechanical properties. Therefore, a number of experimental and modeling studies were performed to describe the relationship between the degree of entanglement of macromolecules and polymer properties. The introduction presents general information about the entanglements of macromolecule chains, collected on the basis of studies of equilibrium entangled polymers. It is also shown how the density of entanglements can be reduced. The second chapter presents experiments and models leading to the description of the movement of a single macromolecule. The next part of the text discusses how the rheological properties change after partial disentangling of the polymer. The results on the influence of the degree of chain entanglement on mechanical properties are presented.

Structure of Aquifex aeolicus lumazine synthase by cryo-electron microscopy to 1.42 Å resolution.

Single-particle cryo-electron microscopy (cryo-EM) has become an essential structural determination technique with recent hardware developments making it possible to reach atomic resolution, at which individual atoms, including hydrogen atoms, can be resolved. In this study, we used the enzyme involved in the penultimate step of riboflavin biosynthesis as a test specimen to benchmark a recently installed microscope and determine if other protein complexes could reach a resolution of 1.5 Å or better, which so far has only been achieved for the iron carrier ferritin. Using state-of-the-art microscope and detector hardware as well as the latest software techniques to overcome microscope and sample limitations, a 1.42 Å map of Aquifex aeolicus lumazine synthase (AaLS) was obtained from a 48 h microscope session. In addition to water molecules and ligands involved in the function of AaLS, we can observe positive density for ∼50% of the hydrogen atoms. A small improvement in the resolution was achieved by Ewald sphere correction which was expected to limit the resolution to ∼1.5 Å for a molecule of this diameter. Our study confirms that other protein complexes can be solved to near-atomic resolution. Future improvements in specimen preparation and protein complex stabilization may allow more flexible macromolecules to reach this level of resolution and should become a priority of study in the field.

Carbon Fiber Film with Multi-Hollow Channels to Expedite Oxygen Electrocatalytic Reaction Kinetics for Flexible Zn-Air Battery.

The high oxygen electrocatalytic overpotential of flexible cathodes due to sluggish reaction kinetics result in low energy conversion efficiency of wearable zinc-air batteries (ZABs). Herein, lignin, as a 3D flexible carbon-rich macromolecule, is employed for partial replacement of polyacrylonitrile and constructing flexible freestanding air electrodes (FFAEs) with large amount of mesopores and multi-hollow channels via electrospinning combined with annealing strategy. The presence of lignin with disordered structure decreases the graphitization of carbon fibers, increases the structural defects, and optimizes the pore structure, facilitating the enhancement of electron-transfer kinetics. This unique structure effectively improves the accessibility of graphitic-N/pyridinic-N with oxygen reduction reaction (ORR) activity and pyridinic-N with oxygen evolution reaction (OER) activity for FFAEs, accelerating the mass transfer process of oxygen-active species. The resulting N-doped hollow carbon fiber films (NHCFs) exhibit superior bifunctional ORR/OER performance with a low potential difference of only 0.60V. The rechargeable ZABs with NHCFs as metal-free cathodes possess a long-term cycling stability. Furthermore, the NHCFs can be used as FFAEs for flexible ZABs which have a high specific capacity and good cycling stability under different bending states. This work paves the way to design and produce highly active metal-free bifunctional FFAEs for electrochemical energy devices.

Quality Assessment of Selected Protein Structures Derived from Homology Modeling and AlphaFold.

With technology advancing, many prediction algorithms have been developed to facilitate the modeling of inherently dynamic and flexible macromolecules such as proteins. Improvements in the prediction of protein structures have attracted a great deal of attention due to the advantages they offer, e.g., in drug design. While trusted experimental methods, such as X-ray crystallography, NMR spectroscopy, and electron microscopy, are preferred structure analysis techniques, in silico approaches are also being widely used. Two computational methods, which are on opposite ends of the spectrum with respect to their modus operandi, i.e., homology modeling and AlphaFold, have been established to provide high-quality structures. Here, a comparative study of the quality of structures either predicted by homology modeling or by AlphaFold is presented based on the characteristics determined by experimental studies using structure validation servers to fulfill the purpose. Although AlphaFold is able to predict high-quality structures, high-confidence parts are sometimes observed to be in disagreement with experimental data. On the other hand, while the structures obtained from homology modeling are successful in incorporating all aspects of the experimental structure used as a template, this method may struggle to accurately model a structure in the absence of a suitable template. In general, although both methods produce high-quality models, the criteria by which they are superior to each other are different and thus discussed in detail.

Crystallizing Self-Standing Covalent Organic Framework Membranes for Ultrafast Proton Transport in Flow Batteries.

Covalent organic frameworks (COFs) display great potential to be assembled into proton conductive membranes for their uniform and controllable pore structure, yet constructing self-standing COF membrane with high crystallinity to fully exploit their ordered crystalline channels for efficient ionic conduction remains a great challenge. Here, a macromolecular-mediated crystallization strategy is designed to manipulate the crystallization of self-standing COF membrane, where the -SO3 H groups in introduced sulfonated macromolecule chains function as the sites to interact with the precursors of COF and thus offer long-range ordered template for membrane crystallization. The optimized self-standing COF membrane composed of highly-ordered nanopores exhibits high proton conductivity (75 mS cm-1 at 100 % relative humidity and 20 °C) and excellent flow battery performance, outperforming Nafion 212 and reported membranes. Meanwhile, the long-term run of membrane is achieved with the help of the anchoring effect of flexible macromolecule chains. Our work provides inspiration to design self-standing COF membranes with ordered channels for permselective application.

Crystallizing Self‐Standing Covalent Organic Framework Membranes for Ultrafast Proton Transport in Flow Batteries

AbstractCovalent organic frameworks (COFs) display great potential to be assembled into proton conductive membranes for their uniform and controllable pore structure, yet constructing self‐standing COF membrane with high crystallinity to fully exploit their ordered crystalline channels for efficient ionic conduction remains a great challenge. Here, a macromolecular‐mediated crystallization strategy is designed to manipulate the crystallization of self‐standing COF membrane, where the −SO3H groups in introduced sulfonated macromolecule chains function as the sites to interact with the precursors of COF and thus offer long‐range ordered template for membrane crystallization. The optimized self‐standing COF membrane composed of highly‐ordered nanopores exhibits high proton conductivity (75 mS cm−1 at 100 % relative humidity and 20 °C) and excellent flow battery performance, outperforming Nafion 212 and reported membranes. Meanwhile, the long‐term run of membrane is achieved with the help of the anchoring effect of flexible macromolecule chains. Our work provides inspiration to design self‐standing COF membranes with ordered channels for permselective application.

3DFlex: determining structure and motion of flexible proteins from cryo-EM

Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network model for continuous molecular heterogeneity for cryo-EM data. 3DFlex exploits knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and tend to preserve local geometry. From two-dimensional image data, 3DFlex enables the determination of high-resolution 3D density, and provides an explicit model of a flexible protein’s motion over its conformational landscape. Experimentally, for large molecular machines (tri-snRNP spliceosome complex, translocating ribosome) and small flexible proteins (TRPV1 ion channel, αVβ8 integrin, SARS-CoV-2 spike), 3DFlex learns nonrigid molecular motions while resolving details of moving secondary structure elements. 3DFlex can improve 3D density resolution beyond the limits of existing methods because particle images contribute coherent signal over the conformational landscape.

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM.

Spectral decomposition of atomic structures in heterogeneous cryo-EM

We consider the problem of recovering the three-dimensional atomic structure of a flexible macromolecule from a heterogeneous cryogenic electron microscopy (cryo-EM) dataset. The dataset contains noisy tomographic projections of the electrostatic potential of the macromolecule, taken from different viewing directions, and in the heterogeneous case, each cryo-EM image corresponds to a different conformation of the macromolecule. Under the assumption that the macromolecule can be modelled as a chain, or discrete curve (as it is for instance the case for a protein backbone with a single chain of amino-acids), we introduce a method to estimate the deformation of the atomic model with respect to a given conformation, which is assumed to be known a priori. Our method consists on estimating the torsion and bond angles of the atomic model in each conformation as a linear combination of the eigenfunctions of the Laplace operator in the manifold of conformations. These eigenfunctions can be approximated by means of a well-known technique in manifold learning, based on the construction of a graph Laplacian using the cryo-EM dataset. Finally, we test our approach with synthetic datasets, for which we recover the atomic model of two-dimensional and three-dimensional flexible structures from simulated cryo-EM images.

Effect of Slip-Spring Parameters on the Dynamics and Rheology of Soft, Coarse-Grained Polymer Models.

Highly coarse-grained (hCG) linear polymer models allow for accessing long time and length scales by dissipative particle dynamics (DPD). This top-down strategy exploits the universal equilibrium behavior of long, flexible macromolecules by accounting only for the relevant interactions, such as molecular connectivity, and by parametrizing their strength via coarse-grained invariants, such as the mean-squared end-to-end distance. The description of the dynamics of long, entangled polymers, however, poses a challenge because (i) the noncrossability of the molecular backbones is not enforced by the soft interactions of an hCG model and (ii) the rheology involves multiple time and length scales, such as the Rouse-like dynamics on short scales and the reptation dynamics on long scales. One popular technique to effectively mimic the effect of entanglements in linear polymer melts via hCG models is slip-springs, and quantitative agreement with simulations that explicitly account for the noncrossability of molecular contours, experiments, and theoretical predictions has been achieved by identifying the time, length, and energy scales of the hCG model and adjusting the number of slip-springs per macromolecule. In the present work, we study how the spatial extent and the mobility of slip-springs affect the dynamics and discuss their implications in the choice of the degree of coarse-graining in computationally efficient hCG models.

Advances in experimental studies of internal motions of non-linear macromolecular systems in solutions

The internal motions of macromolecular systems in solutions are closely related with the microscopic relaxation and macroscopic rheological behavior, therefore, this topic has been extensively studied both theoretically and experimentally. It is well-known that for a flexible or semi-flexible macromolecules with sufficient large chain size, there are two basic modes of motions in dilute solution, namely, translational motion of center of mass of a chain, and internal motions of segments with respect to the center of mass of a chain. In the past few decades, the advancement in characterization and analysis methods make it possible to distinguish both modes of motions experimentally. Although a significant experimental progress has been achieved to understand the intrachain dynamics and test the predictions by classical theory, previously published reviews have been only focused on the linear chain system with the simplest architecture feature. Herein, we aim to provide a comprehensive review on the internal motions of complex macromolecular systems of non-linear architecture. The review is organized as follows: firstly, we will briefly discuss the theoretical and experimental background of internal motions of linear chains; secondly, we will introduce the research progress of internal motions of typical complex macromolecular systems, including hyperbranched polymers, microgels/macrogels, graft polymers and polymeric assemblies/clusters; in the end, a few unresolved questions are enumerated for future studies. We hope this review can afford useful information for understanding the intrachain dynamics of complex macromolecular systems and stimulate the development of universal theory for these systems.

Stabilizing MXene-based nanofiltration membrane by forming analogous semi-interpenetrating network architecture using flexible poly(acrylic acid) for effective wastewater treatment

Two-dimension MXene (Ti3C2Tx) membrane has attracted widespread interest in water treatment to relieve the problem of clean water shortage. Nevertheless, notorious swelling and subsequent instability problems of the MXene-based membrane when immersing in water hinder its wide application, owing to the weak interaction between the adjacent MXene laminates. Herein, a feasible strategy is proposed to stabilize and tune the nanochannels of MXene-based membranes and achieve superior nanofiltration efficiency. Poly(acrylic acid) (PAA) macromolecules are firstly inserted into the MXene nanosheets and form PAA-modified MXene (PAA-MXene) dispersion. Then, the dispersion is vacuum filtered on the electrospun polyacrylonitrile (PAN) nanofibrous substrate to form an ultra-thin PAA-MXene hydrogel barrier layer, thus assembling into PAA-MXene/PAN composite membrane. The MXene laminates will be intertwined by the long chain of flexible PAA macromolecules and thus form homologous semi-interpenetrating network structure with MXene as physical crosslinker through strong hydrogen-bond interaction, which will restrict the obvious structural change of MXene nanosheets and effectively adjust their d-spacing at ∼14.28 Å with MXene: PAA mass ratio of 1:0.5. The synergic effect of PAA and MXene nanosheets, as well as electrospun PAN nanofibrous substrate, endows the PAA-MXene/PAN membrane with superior nanofiltration performance to various dyes. The optimum PAA-MXene/PAN membrane possesses permeate fluxes of 271.26, 516.34, 300.83 L m−2 h−1 and rejections of 98.92%, 99.52%, 98.12% to DR80, AB8GX and AB90 at 0.1 MPa, respectively, which exhibit distinct advantage than the MXene-based membrane in literature. In addition, the PAA-MXene/PAN membrane has good long-time durability and is more suitable for low pressure nanofiltration. All these make PAA-MXene/PAN membrane a promising nanofiltration membrane for wastewater treatment.

Docking Ligands into Flexible and Solvated Macromolecules. 8. Forming New Bonds─Challenges and Opportunities.

Over the years, structure-based design programs and specifically docking small molecules to proteins have become prominent in drug discovery. However, many of these computational tools have been developed to primarily dock enzyme inhibitors (and ligands to other protein classes) relying heavily on hydrogen bonds and electrostatic and hydrophobic interactions. In reality, many drug targets either feature metal ions, can be targeted covalently, or are simply not even proteins (e.g., nucleic acids). Herein, we describe several new features that we have implemented into Fitted to broaden its applicability to a wide range of covalent enzyme inhibitors and to metalloenzymes, where metal coordination is essential for drug binding. This updated version of our docking program was tested for its ability to predict the correct binding mode of drug-sized molecules in a large variety of proteins. We also report new datasets that were essential to demonstrate areas of success and those where additional efforts are required. This resource could be used by other program developers to assess their own software.

From peg to plasmid: system building for MD simulations of flexible macromolecules

Molecular dynamics simulations are increasingly used to characterize static and dynamic processes in large biological systems such as organelles or virions, with the ultimate goal of simulating a whole cell. Programs for constructing the initial coordinates of the envelope as well as packing the interior with proteins and small molecules exist. However, generating input parameters and realistic coordinates for more flexible macromolecules such as DNA, RNA, or carbohydrates, especially close to all-atom resolution, is less routinely done.

Microgels react to force: mechanical properties, syntheses, and force-activated functions.

Microgels are colloidal polymer networks with high molar mass and properties between rigid particles, flexible macromolecules, and micellar aggregates. Their unique stimuli-responsiveness in conjunction with their colloidal phase behavior render them useful for many applications ranging from engineering to biomedicine. In many scenarios either the microgel's mechanical properties or its interactions with mechanical force play an important role. Here, we firstly explain microgel mechanical properties and how these are measured by atomic force microscopy (AFM), then we equip the reader with the synthetic background to understand how specific architectures and chemical functionalities enable these mechanical properties, and eventually we elucidate how the interaction of force with microgels can lead to the activation of latent functionality. Since the interaction of microgels with force is a multiscale and multidisciplinary subject, we introduce and interconnect the different research areas that contribute to the understanding of this emerging field in this Tutorial Review.

Theoretical estimate of the probability for macromole formation

We estimate the polymerization probability of a macromolecule, where the macromolecule is made of distinct monomers; and there are different values of the fugacity for the addition of the monomers in the chain to form an infinitely long linear macromolecule of distinct monomers. The lattice model of the random walk has been used to mimic the conformations of an ideal chain in two and three dimensions, and this ideal chain is the macromolecules of distinct monomers. It has been shown through analytical estimates that the flexible macromolecules may be easily formed than the stiff macromolecules for both two and three dimensional cases. In the case of stiff chain, the ratio of the critical value of monomer fugacity is nothing but the log-log ratio of the Boltzmann’s weight corresponding to the monomers affinity corresponding to its conjugate monomer pair; and it is due to a fact that the stiff chain has small value of the entropy.

On gyration radius distributions of star-like macromolecules

Using the path integral approach, we obtain the characteristic functions of the gyration radius distributions for Gaussian star and Gaussian rosette macromolecules. We derive the analytical expressions for cumulants of both distributions. Applying the steepest descent method, we estimate the probability distribution functions (PDFs) of the gyration radius in the limit of a large number of star and rosette arms in two limiting regimes: for strongly expanded and strongly collapsed macromolecules. We show that in both cases, in the regime of a large gyration radius relative to its mean-square value, the PDFs can be described by the Gaussian functions. In the shrunk macromolecule regime, both distribution functions tend to zero faster than any power of the gyration radius. Based on the asymptotic behavior of the distribution functions and the behavior of statistical dispersions, we demonstrate that the PDF for the rosette is more densely localized near its maximum than that for the star polymer. We construct the interpolation formula for the gyration radius distribution of the Gaussian star macromolecule which can help to take into account the conformational entropy of the flexible star macromolecules within the Flory-type mean-field theories.

Stochastic Modelling of 13C NMR Spin Relaxation Experiments in Oligosaccharides.

A framework for the stochastic description of relaxation processes in flexible macromolecules including dissipative effects has been recently introduced, starting from an atomistic view, describing the joint relaxation of internal coordinates and global degrees of freedom, and depending on parameters recoverable from classic force fields (energetics) and medium modelling at the continuum level (friction tensors). The new approach provides a rational context for the interpretation of magnetic resonance relaxation experiments. In its simplest formulation, the semi-flexible Brownian (SFB) model has been until now shown to reproduce correctly correlation functions and spectral densities related to orientational properties obtained by direct molecular dynamics simulations of peptides. Here, for the first time, we applied directly the SFB approach to the practical evaluation of high-quality 13C nuclear magnetic resonance relaxation parameters, and , and the heteronuclear NOE of several oligosaccharides, which were previously interpreted on the basis of refined ad hoc modelling. The calculated NMR relaxation parameters were in agreement with the experimental data, showing that this general approach can be applied to diverse classes of molecular systems, with the minimal usage of adjustable parameters.

HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps.

In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches. We present HOPMA, a novel method to predict protein functional states and transitions by using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures. The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.