The Mechanical Properties of Nanocomposites Reinforced with PA6 Electrospun Nanofibers.

Electrospun nanofibers are very popular in polymer nanocomposites because they have a high aspect ratio, a large surface area, and good mechanical properties, which gives them a broad range of uses. The application of nonwoven structures of electrospun nanofiber mats has historically been limited to enhancing the interlaminar responses of fiber-reinforced composites. However, the potential of oriented nanofibers to improve the characteristics of bulk matrices cannot be overstated. In this research, a multilayered laminate composite was created by introducing polyamide (PA6)-oriented nanofibers into an epoxy matrix in order to examine the effect of the nanofibers on the tensile and thermal characteristics of the nanocomposite. The specimens' fracture surfaces were examined using scanning electron microscopy (SEM). Using differential scanning calorimetry (DSC) analysis, the thermal characteristics of the nanofiber-layered composites were investigated. The results demonstrated a 10.58% peak in the nanocomposites' elastic modulus, which was compared to the numerical simulation and the analytical model. This work proposes a technique for the development of lightweight high-performance nanocomposites.

Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities.

Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques. This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs.

Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature.

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.

The fauna of aquatic invertebrates in the river impacted by wastewaters from the pulp and paper industry (Komi Republic).

BACKGROUND: Invertebrates are important elements of aquatic ecosystems and play a crucial role in the transformation of matter and energy in continental water bodies. Communities of aquatic invertebrates are characterised by high sensitivity to pollution by nutrients and toxic substances and acidification of water bodies; they serve as good bioindicators of the quality of the aquatic environment and impacts on hydroecosystems. All hydrobionts participate in the processes of self-purification of water bodies.The presented dataset provides information on the aquatic invertebrate community of a large northern river. During 2018-2020, we collected data on changes in the quantitative indicators of the development of benthic and planktonic communities, as well as the species diversity of their fauna. The dataset combines information about the occurrence and abundance of benthic and planktonic invertebrates and summarises data of aquatic invertebrate species found in the Vychegda River in the zone of influence from the pulp and paper mill. NEW INFORMATION: The presented dataset is part of a monitoring programme of the river ecosystems in the production area of Mondi Syktyvkar JSC (the European North-East of Russia, Komi Republic). The dataset describes the structure of benthic invertebrate and plankton communities in the Northern Dvina River Basin. The data on the finding and abundance of large taxa of aquatic invertebrates and species of some groups: Oligochaeta, Cladocera, Copepoda, Rotifera, Ephemeroptera, Plecoptera and Trichoptera are presented. In total, the resource includes 8720 findings of invertebrates, of which 6041 are for zoobenthos organisms and 2679 for zooplankton organisms.

Data on taxa composition of freshwater zooplankton and meiobenthos across Arctic regions of Russia.

We present the presence/absence species list (Table 1) of rotifer, cladoceran, and copepod (Calanoida, Harpacticoida, and Cyclopoida) fauna from seven Arctic regions of Russia (the Kola Peninsula, the Pechora River Delta, the Bolshezemelskaya tundra, the Polar Ural, the Putorana Plateau, the Lena River Delta, and the Indigirka River Basin) based on our own and literature data. Our own records were obtained by analyzing samples of zooplankton, meiobenthos, and two cores of bottom sediments (from the Kola Peninsula and the Bolshezemelskaya tundra lakes) that we collected once in July or August in 1992, 1995-2017. To supplement the list, we used relevant literature with periods of research from the 1960s to the 2010s. The list is almost identical to "Dataset 2: Zooplankton and Meiofauna across Arctic Regions of Russia", which was analyzed but not published in [1]. The detailed analysis of this list revealed the specific composition of the aquatic fauna associated with the climatic and geographical factors [1]. The data provide information on the current state of biodiversity and species richness in Arctic fresh waters and can serve as the basis for monitoring these environments and predicting how they are likely to change in the future.

Botulinum neurotoxin inhibitor binding dynamics and kinetics relevant for drug design.

BACKGROUND: A natural product analog, 3-(4-nitrophenyl)-7H-furo[3,2-g]chromen-7-one, which is a nitrophenyl psoralen (NPP) was found to be an effective inhibitor of botulinum neurotoxin type A (BoNT/A). METHODS: In this work, we performed enzyme inhibition kinetics and employed biochemical techniques such as isothermal calorimetry (ITC) and fluorescence spectroscopy as well as molecular modeling to examine the kinetics and binding mechanism of NPP inhibitor with BoNT/A LC. RESULTS: Studies of inhibition mechanism and binding dynamics of NPP to BoNT/A light chain (BoNT/A LC) showed that NPP is a mixed type inhibitor for the zinc endopeptidase activity, implying that at least part of the inhibitor-enzyme binding site may be different from the substrate-enzyme binding site. By using biochemical techniques, we demonstrated NPP forms a stable complex with BoNT/A LC. These observations were confirmed by Molecular Dynamics (MD) simulation, which demonstrates that NPP binds to the site near the active site. CONCLUSION: The NPP binding interferes with BoNT/A LC binding to the SNAP-25, hence, inhibits its cleavage. Based on these results, we propose a modified strategy for designing a molecule to enhance the efficiency of the inhibition against the neurotoxic effect of BoNT. GENERAL SIGNIFICANCE: Insights into the interactions of NPP with BoNT/A LC using biochemical and computational approaches will aid in the future development of effective countermeasures and better pharmacological strategies against botulism.

Opportunities and challenges of text mining in aterials research.

Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications.

Fluctuating nonlinear spring theory: Strength, deformability, and toughness of biological nanoparticles from theoretical reconstruction of force-deformation spectra.

We developed the Fluctuating Nonlinear Spring (FNS) model to describe the dynamics of mechanical deformation of biological particles, such as virus capsids. The theory interprets the force-deformation spectra in terms of the "Hertzian stiffness" (non-linear regime of a particle's small-amplitude deformations), elastic constant (large-amplitude elastic deformations), and force range in which the particle's fracture occurs. The FNS theory enables one to quantify the particles' elasticity (Young's moduli for Hertzian and bending deformations), and the limits of their strength (critical forces, fracture toughness) and deformability (critical deformations) as well as the probability distributions of these properties, and to calculate the free energy changes for the particle's Hertzian, elastic, and plastic deformations, and eventual fracture. We applied the FNS theory to describe the protein capsids of bacteriophage P22, Human Adenovirus, and Herpes Simplex virus characterized by deformations before fracture that did not exceed 10-19% of their size. These nanoshells are soft (~1-10-GPa elastic modulus), with low ~50-480-kPa toughness - a regime of material behavior that is not well understood, and with the strength increasing while toughness decreases with their size. The particles' fracture is stochastic, with the average values of critical forces, critical deformations, and fracture toughness comparable with their standard deviations. The FNS theory predicts 0.7-MJ/mol free energy for P22 capsid maturation, and it could be extended to describe uniaxial deformation of cylindrical microtubules and ellipsoidal cellular organelles.

Botulinum Endopeptidase: SAXS Experiments and MD Simulations Reveal Extended Solution Structures That Account for Its Biochemical Properties.

Development of antidotes against botulism requires understanding of the enzymatically active conformations of Botulinum neurotoxin serotype A (BoNT/A) light chain (LCA). We performed small angle X-ray scattering (SAXS) to characterize the solution structures of truncated light chain (tLCA). The 34-37 Å radius of gyration of tLCA was 1.5-times greater than the averaged 22-23-Å radius from the crystal structures. The bimodal distribution of interatomic distances P(r) indicated the two-domain tLCA structure with 129-133 Å size, and Kratky plots indicated the tLCA partial unfolding in the 25-37 °C temperature range. To interpret these data, we employed molecular dynamics simulations and machine learning. Excellent agreement between experimental and theoretical P(r) profiles helped to resolve conformational subpopulations of tLCA in solution. Partial unfolding of the C-terminal portion of tLCA (residues 339-425) results in formation of extended conformations with the larger globular domain (residues 2-298) and the smaller unstructured domain (339-425). The catalytic domain, buried 20 Å-deep inside the crystal structure, becomes accessible in extended solution conformations (8-9 Å deep). The C- and N-termini containing different functional sequence motifs are maximally separated in the extended conformations. Our results offer physical insights into the molecular basis of BoNT/A function and stress the importance of reversible unfolding-refolding transitions and hydrophobic interactions.

Author Correction: Text-mined dataset of inorganic materials synthesis recipes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Text-mined dataset of inorganic materials synthesis recipes.

Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of "codified recipes" for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.

Unsupervised word embeddings capture latent knowledge from materials science literature.

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3-10, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11-13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

Regulatory element in fibrin triggers tension-activated transition from catch to slip bonds.

Fibrin formation and mechanical stability are essential in thrombosis and hemostasis. To reveal how mechanical load impacts fibrin, we carried out optical trap-based single-molecule forced unbinding experiments. The strength of noncovalent A:a knob-hole bond stabilizing fibrin polymers first increases with tensile force (catch bonds) and then decreases with force when the force exceeds a critical value (slip bonds). To provide the structural basis of catch-slip-bond behavior, we analyzed crystal structures and performed molecular modeling of A:a knob-hole complex. The movable flap (residues γ295 to γ305) containing the weak calcium-binding site γ2 serves as a tension sensor. Flap dissociation from the B domain in the γ-nodule and translocation to knob 'A' triggers hole 'a' closure, resulting in the increase of binding affinity and prolonged bond lifetimes. The discovery of biphasic kinetics of knob-hole bond rupture is quantitatively explained by using a theory, formulated in terms of structural transitions in the binding pocket between the low-affinity (slip) and high-affinity (catch) states. We provide a general framework to understand the mechanical response of protein pairs capable of tension-induced remodeling of their association interface. Strengthening of the A:a knob-hole bonds at 30- to 40-pN forces might favor formation of nascent fibrin clots subject to hydrodynamic shear in vivo.

TensorCalculator: exploring the evolution of mechanical stress in the CCMV capsid.

A new computational methodology for the accurate numerical calculation of the Cauchy stress tensor, stress invariants, principal stress components, von Mises and Tresca tensors is developed. The methodology is based on the atomic stress approach which permits the calculation of stress tensors, widely used in continuum mechanics modeling of materials properties, using the output from the MD simulations of discrete atomic and [Formula: see text]-based coarse-grained structural models of biological particles. The methodology mapped into the software package TensorCalculator was successfully applied to the empty cowpea chlorotic mottle virus (CCMV) shell to explore the evolution of mechanical stress in this mechanically-tested specific example of a soft virus capsid. We found an inhomogeneous stress distribution in various portions of the CCMV structure and stress transfer from one portion of the virus structure to another, which also points to the importance of entropic effects, often ignored in finite element analysis and elastic network modeling. We formulate a criterion for elastic deformation using the first principal stress components. Furthermore, we show that von Mises and Tresca stress tensors can be used to predict the onset of a viral capsid's mechanical failure, which leads to total structural collapse. TensorCalculator can be used to study stress evolution and dynamics of defects in viral capsids and other large-size protein assemblies.

Mechanistic Basis for the Binding of RGD- and AGDV-Peptides to the Platelet Integrin αIIbß3.

Binding of soluble fibrinogen to the activated conformation of the integrin αIIbß3 is required for platelet aggregation and is mediated exclusively by the C-terminal AGDV-containing dodecapeptide (γC-12) sequence of the fibrinogen γ chain. However, peptides containing the Arg-Gly-Asp (RGD) sequences located in two places in the fibrinogen Aα chain inhibit soluble fibrinogen binding to αIIbß3 and make substantial contributions to αIIbß3 binding when fibrinogen is immobilized and when it is converted to fibrin. Here, we employed optical trap-based nanomechanical measurements and computational molecular modeling to determine the kinetics, energetics, and structural details of cyclic RGDFK (cRGDFK) and γC-12 binding to αIIbß3. Docking analysis revealed that NMR-determined solution structures of cRGDFK and γC-12 bind to both the open and closed αIIbß3 conformers at the interface between the αIIb ß-propeller domain and the ß3 ßI domain. The nanomechanical measurements revealed that cRGDFK binds to αIIbß3 at least as tightly as γC-12. A subsequent analysis of molecular force profiles and the number of peptide-αIIbß3 binding contacts revealed that both peptides form stable bimolecular complexes with αIIbß3 that dissociate in the 60-120 pN range. The Gibbs free energy profiles of the αIIbß3-peptide complexes revealed that the overall stability of the αIIbß3-cRGDFK complex was comparable with that of the αIIbß3-γC-12 complex. Thus, these results provide a mechanistic explanation for previous observations that RGD- and AGDV-containing peptides are both potent inhibitors of the αIIbß3-fibrinogen interactions and are consistent with the observation that RGD motifs, in addition to AGDV, support interaction of αIIbß3 with immobilized fibrinogen and fibrin.

Assembly and Mechanical Properties of the Cargo-Free and Cargo-Loaded Bacterial Nanocompartment Encapsulin.

Prokaryotes mostly lack membranous compartments that are typical of eukaryotic cells, but instead, they have various protein-based organelles. These include bacterial microcompartments like the carboxysome and the virus-like nanocompartment encapsulin. Encapsulins have an adaptable mechanism for enzyme packaging, which makes it an attractive platform to carry a foreign protein cargo. Here we investigate the assembly pathways and mechanical properties of the cargo-free and cargo-loaded nanocompartments, using a combination of native mass spectrometry, atomic force microscopy and multiscale computational molecular modeling. We show that encapsulin dimers assemble into rigid single-enzyme bacterial containers. Moreover, we demonstrate that cargo encapsulation has a mechanical impact on the shell. The structural similarity of encapsulins to virus capsids is reflected in their mechanical properties. With these robust mechanical properties encapsulins provide a suitable platform for the development of nanotechnological applications.

SOP-GPU: influence of solvent-induced hydrodynamic interactions on dynamic structural transitions in protein assemblies.

Hydrodynamic interactions (HI) are incorporated into Langevin dynamics of the Cα -based protein model using the Truncated Expansion approximation (TEA) to the Rotne-Prager-Yamakawa diffusion tensor. Computational performance of the obtained GPU realization demonstrates the model's capability for describing protein systems of varying complexity (10(2) -10(5) residues), including biological particles (filaments, virus shells). Comparison of numerical accuracy of the TEA versus exact description of HI reveals similar results for the kinetics and thermodynamics of protein unfolding. The HI speed up and couple biomolecular transitions through cross-communication among protein domains, which result in more collective displacements of structure elements governed by more deterministic (less variable) dynamics. The force-extension/deformation spectra from nanomanipulations in silico exhibit sharper force signals that match well the experimental profiles. Hence, biomolecular simulations without HI overestimate the role of tension/stress fluctuations. Our findings establish the importance of incorporating implicit water-mediated many-body effects into theoretical modeling of dynamic processes involving biomolecules. © 2016 Wiley Periodicals, Inc.

Fluctuating Nonlinear Spring Model of Mechanical Deformation of Biological Particles.

The mechanical properties of virus capsids correlate with local conformational dynamics in the capsid structure. They also reflect the required stability needed to withstand high internal pressures generated upon genome loading and contribute to the success of important events in viral infectivity, such as capsid maturation, genome uncoating and receptor binding. The mechanical properties of biological nanoparticles are often determined from monitoring their dynamic deformations in Atomic Force Microscopy nanoindentation experiments; but a comprehensive theory describing the full range of observed deformation behaviors has not previously been described. We present a new theory for modeling dynamic deformations of biological nanoparticles, which considers the non-linear Hertzian deformation, resulting from an indenter-particle physical contact, and the bending of curved elements (beams) modeling the particle structure. The beams' deformation beyond the critical point triggers a dynamic transition of the particle to the collapsed state. This extreme event is accompanied by a catastrophic force drop as observed in the experimental or simulated force (F)-deformation (X) spectra. The theory interprets fine features of the spectra, including the nonlinear components of the FX-curves, in terms of the Young's moduli for Hertzian and bending deformations, and the structural damage dependent beams' survival probability, in terms of the maximum strength and the cooperativity parameter. The theory is exemplified by successfully describing the deformation dynamics of natural nanoparticles through comparing theoretical curves with experimental force-deformation spectra for several virus particles. This approach provides a comprehensive description of the dynamic structural transitions in biological and artificial nanoparticles, which is essential for their optimal use in nanotechnology and nanomedicine applications.

Tubulin bond energies and microtubule biomechanics determined from nanoindentation in silico.

Microtubules, the primary components of the chromosome segregation machinery, are stabilized by longitudinal and lateral noncovalent bonds between the tubulin subunits. However, the thermodynamics of these bonds and the microtubule physicochemical properties are poorly understood. Here, we explore the biomechanics of microtubule polymers using multiscale computational modeling and nanoindentations in silico of a contiguous microtubule fragment. A close match between the simulated and experimental force-deformation spectra enabled us to correlate the microtubule biomechanics with dynamic structural transitions at the nanoscale. Our mechanical testing revealed that the compressed MT behaves as a system of rigid elements interconnected through a network of lateral and longitudinal elastic bonds. The initial regime of continuous elastic deformation of the microtubule is followed by the transition regime, during which the microtubule lattice undergoes discrete structural changes, which include first the reversible dissociation of lateral bonds followed by irreversible dissociation of the longitudinal bonds. We have determined the free energies of dissociation of the lateral (6.9 ± 0.4 kcal/mol) and longitudinal (14.9 ± 1.5 kcal/mol) tubulin-tubulin bonds. These values in conjunction with the large flexural rigidity of tubulin protofilaments obtained (18,000-26,000 pN·nm(2)) support the idea that the disassembling microtubule is capable of generating a large mechanical force to move chromosomes during cell division. Our computational modeling offers a comprehensive quantitative platform to link molecular tubulin characteristics with the physiological behavior of microtubules. The developed in silico nanoindentation method provides a powerful tool for the exploration of biomechanical properties of other cytoskeletal and multiprotein assemblies.

Botulinum neurotoxin: unique folding of enzyme domain of the most-poisonous poison.

Botulinum neurotoxin (BoNT), the most toxic substance known to mankind, is the first example of the fully active molten globule state. To understand its folding mechanism, we performed urea denaturation experiments and theoretical modeling using BoNT serotype A (BoNT/A). We found that the extent of BoNT/A denaturation from the native state (N) shows a nonmonotonic dependence on urea concentration indicating a unique multistep denaturation process, N → I1 [Formula: see text] I2 [Formula: see text] U, with two intermediate states I1 and I2. BoNT/A loses almost all its secondary structure in 3.75 M urea (I1), yet it displays a native-like secondary structure in 5 M urea (I2). This agrees with the results of theoretical modeling, which helped to determine the molecular basis of unique behavior of BoNT/A in solution. Except for I2, all the states revert back to full enzymatic activity for SNAP-25 including the unfolded state U stable in 7 M urea. Our results stress the importance of structural flexibility in the toxin's mechanism of survival and action, an unmatched evolutionary trait from billion-year-old bacteria, which also correlates with the long-lasting enzymatic activity of BoNT inside neuronal cells. BoNT/A provides a rich model to explore protein folding in relation to functional activity.