Atomic Detail Research Articles

Surface hydrophobization of hydrogels via interface dynamics-induced network reconfiguration

Effective and easy regulation of hydrogel surface properties without changing the overall chemical composition is important for their diverse applications but remains challenging to achieve. We report a generalizable strategy to reconfigure hydrogel surface networks based on hydrogel–substrate interface dynamics for manipulation of hydrogel surface wettability and bioadhesion. We show that the grafting of hydrophobic yet flexible polymeric chains on mold substrates can significantly elevate the content of hydrophobic polymer backbones and reduce the presence of polar groups in hydrogel surface networks, thereby transforming the otherwise hydrophilic hydrogel surface into a hydrophobic surface. Experimental results show that the grafted highly dynamic hydrophobic chains achieved with optimal grafting density, chain length, and chain structure are critical for such substantial hydrogel surface network reconfiguration. Molecular dynamics simulations further reveal the atomistic details of the hydrogel network reconfiguration induced by the dynamic interface interactions. The hydrogels prepared using our strategy show substantially enhanced bioadhesion and transdermal delivery compared with the hydrogels of the same chemical composition but fabricated via the conventional method. Our findings provide important insights into the dynamic hydrogel–substrate interactions and are instrumental to the preparation of hydrogels with custom surface properties.

Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks

This article introduces an innovative approach that utilizes machine learning (ML) to address the computational challenges of accurate atomistic simulations in materials science. Focusing on the field of molecular dynamics (MD), which offers insight into material behavior at the atomic level, the study demonstrates the potential of trained artificial neural networks (tANNs) as surrogate models. These tANNs capture complex patterns from built datasets, enabling fast and accurate predictions of material properties. The article highlights the application of 3D convolutional neural networks (CNNs) to incorporate atomistic details and defects in predictions, a significant advancement compared to current 2D image-based, or descriptor-based methods. Through a dataset of atomistic structures and MD simulations, the trained 3D CNN achieves impressive accuracy, predicting material properties with a root-mean-square error below 0.65 GPa for the prediction of elastic constants and a speed-up of approximately 185 to 2100 times compared to traditional MD simulations. This breakthrough promises to expedite materials design processes and facilitate scale-bridging in materials science, offering a new perspective on addressing computational demands in atomistic simulations.

Nanomatrix: Scalable Construction of Crowded Biological Environments.

We present a novel method for the interactive construction and rendering of extremely large molecular scenes, capable of representing multiple biological cells in atomistic detail. Our method is tailored for scenes, which are procedurally constructed, based on a given set of building rules. Rendering of large scenes normally requires the entire scene available in-core, or alternatively, it requires out-of-core management to load data into the memory hierarchy as a part of the rendering loop. Instead of out-of-core memory management, we propose to procedurally generate the scene on-demand on the fly. The key idea is a positional- and view-dependent procedural scene-construction strategy, where only a fraction of the atomistic scene around the camera is available in the GPU memory at any given time. The atomistic detail is populated into a uniform-space partitioning using a grid that covers the entire scene. Most of the grid cells are not filled with geometry, only those are populated that are potentially seen by the camera. The atomistic detail is populated in a compute shader and its representation is connected with acceleration data structures for hardware ray-tracing of modern GPUs. Objects which are far away, where atomistic detail is not perceivable from a given viewpoint, are represented by a triangle mesh mapped with a seamless texture, generated from the rendering of geometry from atomistic detail. The algorithm consists of two pipelines, the construction-compute pipeline, and the rendering pipeline, which work together to render molecular scenes at an atomistic resolution far beyond the limit of the GPU memory containing trillions of atoms. We demonstrate our technique on multiple models of SARS-CoV-2 and the red blood cell.

Development of a coarse-grained molecular dynamics model for poly(dimethyl-co-diphenyl)siloxane.

Polydimethylsiloxane is an important polymeric material with a wide range of applications. However, environmental effects like low temperature can induce crystallization in this material with resulting changes in its structural and dynamic properties. The incorporation of phenyl-siloxane components, e.g., as in a poly(dimethyl-co-diphenyl)siloxane random copolymer, is known to suppress such crystallization. Molecular dynamics (MD) simulations can be a powerful tool to understand such effects in atomistic detail. Unfortunately, all-atomistic molecular dynamics (AAMD) is limited in both spatial dimensions and simulation times it can probe. To overcome such constraints and to extend to more useful length- and time-scales, we systematically develop a coarse-grained molecular dynamics (CGMD) model for the poly(dimethyl-co-diphenyl)siloxane system with bonded and non-bonded interactions determined from all-atomistic simulations by the iterative Boltzmann inversion (IBI) method. Additionally, we propose a lever rule that can be useful to generate non-bonded potentials for such systems without reference to the all-atomistic ground truth. Our model captures the structural and dynamic properties of the copolymer material with quantitative accuracy and is useful to study long-time dynamics of highly-entangled systems, sequence-dependent properties, phase behaviour, etc.

Step-induced double-row pattern of interfacial water on rutile TiO2(110) under electrochemical conditions.

Metal oxides are promising (photo)electrocatalysts for sustainable energy technologies due to their good activity and abundant resources. Their applications such as photocatalytic water splitting predominantly involve aqueous interfaces under electrochemical conditions, but in situ probing oxide-water interfaces is proven to be extremely challenging. Here, we present an electrochemical scanning tunneling microscopy (EC-STM) study on the rutile TiO2(110)-water interface, and by tuning surface redox chemistry with careful potential control we are able to obtain high quality images of interfacial structures with atomic details. It is interesting to find that the interfacial water exhibits an unexpected double-row pattern that has never been observed. This finding is confirmed by performing a large scale simulation of a stepped interface model enabled by machine learning accelerated molecular dynamics (MLMD) with ab initio accuracy. Furthermore, we show that this pattern is induced by the steps present on the surface, which can propagate across the terraces through interfacial hydrogen bonds. Our work demonstrates that by combining EC-STM and MLMD we can obtain new atomic details of interfacial structures that are valuable to understand the activity of oxides under realistic conditions.

Hydrogen atom scattering at the Al2O3(0001) surface: a combined experimental and theoretical study.

Investigating atom-surface interactions is the key to an in-depth understanding of chemical processes at interfaces, which are of central importance in many fields - from heterogeneous catalysis to corrosion. In this work, we present a joint experimental and theoretical effort to gain insights into the atomistic details of hydrogen atom scattering at the α-Al2O3(0001) surface. Surprisingly, this system has been hardly studied to date, although hydrogen atoms as well as α-Al2O3 are omnipresent in catalysis as reactive species and support oxide, respectively. We address this system by performing hydrogen atom beam scattering experiments and molecular dynamics (MD) simulations based on a high-dimensional machine learning potential trained to density functional theory data. Using this combination of methods we are able to probe the properties of the multidimensional potential energy surface governing the scattering process. Specifically, we compare the angular distribution and the kinetic energy loss of the scattered atoms obtained in experiment with a large number of MD trajectories, which, moreover, allow to identify the underlying impact sites at the surface.

Conformational preferences of heparan sulfate to recognize the CXCL8 dimer in aqueous medium: degree of sulfation and hydrogen bonds.

The sulfation pattern and epimerization of the long-chain sulfated polysaccharide heparan sulfate (HS) cause structural diversity and regulate various physiological and pathological processes when binding with proteins. In this work, we performed a series of molecular dynamics simulations of three variants of the octadecasaccharide HS with varying sulfation positions in aqueous medium in their free forms and in the presence of the chemokine CXCL8 dimer. The free energy of binding depicts the sulfation at the 6-O position of GlcNAc (HS6S), and both 3-O and 6-O positions of GlcNAc (HS3S6S) of HS variants are more likely to bind with the CXCL8 dimer than the triply sulfated HS2S3S6S, which is sulfated at the 2-O position of GlcUA additionally along with 3-O and 6-O positions of GlcNAc. Binding between HS and CXCL8 was driven by electrostatic and van der Waals interactions predominantly regardless of the sulfation pattern; however, unfavorable entropic contribution suppressed the interaction between HS and CXCL8. The contribution of different amino acid residues to the binding energetics suggested that basic amino acids line up the binding site of CXCL8. This study further acknowledges the role of interfacial water that is structured and bound with HS through hydrogen bonds, exhibiting differential hydrogen bond relaxation dynamics compared to when the HS molecules are free. Moreover, this study identifies that with the increase in sulfation, the HS-water hydrogen bond relaxation occurs faster with the complexation, while the reverse trend is followed in their free forms. Significant structural adaptation of the different sulfated HS molecules, as verified from the free energy landscapes generated from various reaction coordinates, root-mean-square-deviations, end-to-end distances, including ring pucker angles, dihedral flexibility, and the high conformational entropy cost arising from the glycosidic bonds, suggests that the different sulfated variants of HS undergo significant structural transformation to bind with CXCL8. The presence of a CXCL8 dimer imposes the bound forms of HS to adopt non-linear structures with skew-boat conformations. The atomistic details of the study would help in understanding the selectivity and conformational diversity, as well as the role of solvents in the recognition of CXCL8 by different sulfated variants of HS molecules.

Supporting the CIF file format of proteins in molecular dynamics simulations

Molecular dynamics (MD) simulations can capture the dynamic behavior of proteins in full atomic detail and at very fine temporal resolution, so they have become an important tool in the study of protein dynamics. To date, several MD packages are widely used. An MD simulation starts from an initial structure that is generally taken from the Protein Data Bank (PDB). Until 2014, the PDB format was the standard file format for protein structures. However, there are certain intrinsic limitations in the PDB format, such as the storage of structural information in a fixed-width format, which is an issue for very large protein complexes. Therefore, the CIF (crystallographic information framework) format has been proposed, which is characterized by its superior expansibility. To our knowledge, the current mainstream MD packages support only the PDB format but do not support the CIF format directly. In this study, we modified the source code of one of the MD packages, GROMACS, which enables it to support CIF-formatted structure files as input and subsequently generate molecular topology files. This work simplifies the preprocessing of large protein complexes for MD simulations.

Metal ions in biomedically relevant macromolecular structures.

Understanding the functions of metal ions in biological systems is crucial for many aspects of research, including deciphering their roles in diseases and potential therapeutic use. Structural information about the molecular or atomic details of these interactions, generated by methods like X-ray crystallography, cryo-electron microscopy, or nucleic magnetic resonance, frequently provides details that no other method can. As with any experimental method, they have inherent limitations that sometimes lead to an erroneous interpretation. This manuscript highlights different aspects of structural data available for metal-protein complexes. We examine the quality of modeling metal ion binding sites across different structure determination methods, where different kinds of errors stem from, and how they can impact correct interpretations and conclusions.

Recent advances in computational drug discovery for therapy against coronavirus SARS-CoV-2

Despite essential experimental efforts focused on studying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), computational chemistry methods are promising complementary tools in combating coronavirus disease 2019 (COVID-19). The present review aims to provide readers with the recent progress and advances in computational approaches currently used to streamline drug discovery and development in the context of COVID-19. Our review is dual purpose. It is intended (a) to familiarize the readership with the general concept of QSAR, in silico screening, molecular docking and molecular dynamics (MD) simulations and (b) to provide key examples of the recent applications of these computational tools in discovering novel therapeutic agents against COVID-19. We outline how structure- and ligand-based drug design can accelerate the structural elucidation of pharmacological drug targeting and the discovery of preclinical drug candidate molecules. Several examples of MD computational studies demonstrate how atomistic MD simulations can facilitate our understanding of the molecular basis of drug actions and biological mechanisms of virus inhibition in atomic detail. Finally, the short- and long-term perspectives in computational drug discovery are discussed. The aim of this study is to summarize the last three years' progress and advances in computational approaches currently used to streamline the drug discovery and development process in the context of COVID-19. Materials and methods. The literature overview of QSAR, in silico screening, machine learning, molecular docking and molecular dynamics (MD) simulations is given in the context of COVID-19. The literature search was performed using online databases, such as Scopus, Web of Science, PDB-protein databank, and PubMed, focusing on the following keywords - human coronavirus, QSAR, molecular docking, virtual screening, machine learning, molecular dynamics, Mpro and PLpro proteases, SARS-CоV-2, respectively. Results. The review familiarizes the readership with the general concept of QSAR, in silico screening, machine learning, molecular docking and MD simulations and provides key examples of the recent applications of these computational tools in discovering novel therapeutic agents against COVID-19. Conclusions. New insight into the recent progress and achievements in computer-guided drug discovery for therapeutic agents against SARS-CoV-2 is provided

First Principles Modelling of Growth of Hybrid Organic-Inorganic Films

Hybrid organic-inorganic materials fabricated with molecular layer deposition (MLD) are a special class of materials that are attracting great interest in many economically and socially relevant technological applications due to their diphasic and tunable organic-inorganic structure. Despite the growing interest in these hybrid materials, knowledge around relevant precursor chemistries and reaction mechanisms, as well as details of many determined experimental results are unknown. Density functional theory (DFT) has proven to be a powerful tool to investigate and understand the atomic level details of the precursor chemistry and address aspects of the MLD experiments.In our work, we use first principles DFT calculations to examine key steps in the mechanism of hybrid film deposition through MLD by modelling precursor-surface and precursor-precursor reactions. We explore the growth mechanism of aluminium and titanium containing hybrid films, namely alucones and titanicones. Both materials are of high interest for passivation layers in batteries. For alucones we investigate in detail the chemistry of the MLD process between the post-trimethylaluminium (TMA) pulse methyl-terminated Al2O3 surface with aromatic compounds with hydroxy and/or amino groups as reactive groups: hydroquinone (HQ), 4-aminophenol (AP) and p-phenylenediamine (PD). Double reactions of HQ, AP and PDA with the Al2O3 surface are explored in detail to assist the interpretation of the experimental findings regarding the differences found for hybrid films grown with aromatic and aliphatic organic precursors. DFT calculations show that in contrast to aliphatic organics, an upright configuration will be present for HQ, AP and PD so that the molecules are self-assembled in an upright configuration, which leads to thicker hybrid films. We predict that aromatic molecules are the best choice for stable hybrid films and their chemistry can be manipulated without degrading stability. [1]We also investigate in detail the chemistry between the most common titanium precursors, namely titanium tetrachloride (TiCl4) and tetrakis(dimethylamido)titanium (Ti(DMA)4), and ethylene glycol (EG) and glycerol (GL) as the organic precursors for the fabrication of titanicone films. We analyse the impact of the substrate on the initial MLD reactions in titanicone film growth using three different surface models: anatase TiO2, rutile TiO2 and Al2O3. This work highlights the role of the phase of TiO2 and Ti-containing precursors in production of titanicone films and provides motivation to develop a new rutile TiO2 based hybrid film suggesting that the desired film growth, flexibility and stability can be achieved. [2][1] A. Muriqi, M. Karppinen and M. Nolan, Role of terminal groups in aromatic molecules on the growth of Al2O3-based hybrid materials, Dalton Transactions, 2021, 50, 17583-17593. doi.org/10.1039/D1DT03195C.[2] Arbresha Muriqi and Michael Nolan, “Role of Titanium and Organic Precursors in Molecular Layer Deposition of “Titanicone” Hybrid Materials”, Beilstein Journal of Nanotechnology, 2022, 13, 1240–1255. doi.org/10.3762/bjnano.13.103.

(Invited) Atomic Scale Details in Electrochemistry & Electrodeposition: The Good, the Bad, and the Ugly

Since the invention of the STM more than 40 years ago, which enabled the direct observation of surfaces on the atomic scale, it took us around 25 years full of surprises, before we gathered enough knowledge to formulate a decent background including almost all surface science phenomena [1].At this time, it was often believed that fundamental surface science was dead. However,progress in nanotechnology and the semiconducting industry, kept this discipline alive, although the number of researchers steadily decreased. Being at the border between physics and chemistry, electrochemistry and electrodeposition research was executed on the atomic, fundamental level by only a few “Mohicans”.The Good is:- that the required energy transition makes our discipline increasingly relevant.- that this leads to a revival of surface science.- that more and more researchers from different fields step into our business.- that we developed in operando methods, like ECSTM, SXRD, and XPS, that deliver atomic insights.- that inventive new methods become available, like the reaction volume or entropy on the atomic scale.- that our complex machines get easier over time.- that we constantly gain new fundamental, atomic scale insights.The Bad, however, is:- that the combination of electrochemistry with surface science is a real complex matter.- that the parameter space in electrochemistry is orders of magnitudes larger than surface science.- that there are too less groups and researchers, in general, to tackle all emerging problems.- that the time is ticking: global climate change is not waiting to figure out all details.- that we will need at least one generation to educate new students.The Ugly is severe, interesting and challenging, as it becomes clear:- that, instead of directly gaining more insights, often a multitude of additional questions emerge.- that there is a complex interplay of fundamental atomic and molecular processes.- that “known” textbook knowledge should be taken with a grain of salt.- that some fundamentals are even missing completely: our research is still in its infancy.- that there exists an enormous amount of literature describing a magnitude of different effects.- that one needs expertise of several different fields to really understand one detail.In my talk, I will present several recent examples, in which we thought that we had understood it. Often one last, small experiment was required to deliver the final confirmation. Oh boy, and how often have we been wrong. Nature confronts us with what we do not know! It will be a long and arduous journey, but similar to the early days in surface science, full with exciting and surprising puzzles from nature.I hope that I can trigger your enthusiasm to join our important journey![1] H. Ibach, “Physics of Surfaces and Interfaces”, Springer (2006)

Atomistic-Scale Simulations Toward the Understanding of the Conversion–Alloying Mechanism in Li-Ion Battery Anodes

The increasing demands for energy storage call for improved capacity and cycling stability of modern Li-ion batteries (LIBs). However, the application of the state-of-the-art LIB anode material – graphite is limited as graphite underperforms as a high-rate/high-storage-capacity anode. Alternatively, materials capable of alloying with Li-ions have attracted attention as a promising pathway to solve high-rate and high-capacity challenges. Furthermore, when alloying reaction in a material is coupled with irreversible conversion reaction, the materials operating under such mechanism exhibit a high Li-ion storage capacity and long-term stability during electrochemical cycling. However, despite the optimistic performance of these materials, a number of parameters need to be optimized and fundamentally understood.Among the unknowns for the conversion/alloying materials are the atomistic details of the reaction mechanism - understanding the atomistic structure during transformations is a pre-requisite for the future rational optimization of the materials. Furthermore, amorphization, which is prevalent in these materials, severely limits the conventional characterization methods essentially impeding further development and understanding the degradation mechanisms. Hence, to investigate the atomistic structure changes, we proposed a computational methodology for these conversion-alloying materials and their reaction mechanism by taking amorphous substoichiometric silicon nitride as a showcase system [1]. The molecular dynamics (MD) simulations utilized the ReaxFF parameter set which was specifically developed for the investigation. Subsequently, the experimental pair distribution functions (PDF) were used to verify the modeling results.While the reactive molecular dynamics was coupled with the PDF simulation to complement the experimental measurements, the effective and practical approach guided us to the atomistic structure evolution understanding for LIBs in a conversion–alloying process, revealing the bond coordinations around the Si atoms in the active material. Consequently, the analysis opened an in-depth understanding of the cycling stability of substoichiometric a-SiNx anode materials by revealing the atomistic features relevant to the changes this material undergoes during lithiation and delithiation processes in a LIB. Furthermore, the proposed approach allows modeling of the changes at the atomistic scale for the amorphous materials and predicting the experimental PDF at different cycling stages, verifying the modeling outcome. The topic also addresses the applications of the approach for other anode materials.

Natural aging and vacancy trapping in Al-6xxx

Undesirable natural aging (NA) in Al-6xxx delays subsequent artificial aging (AA) but the size, composition, and evolution of clustering are challenging to measure. Here, atomistic details of early-stage clustering in Al–1%Mg–0.6%Si during NA are studied computationally using a chemically-accurate neural-network potential. Feasible growth paths for the preferred beta '' precipitates identify: dominant clusters differing from beta '' motifs; spontaneous vacancy-interstitial formation creating 14–18 solute atom beta ''-like motifs; and lower-energy clusters requiring chemical re-arrangement to form beta '' nuclei. Quasi-on-lattice kinetic Monte Carlo simulations reveal that 8–14 solute atom clusters form within 1000 s but that growth slows considerably due to vacancy trapping inside clusters, with trapping energies of 0.3-0.5 eV. These findings rationalize why cluster growth and alloy hardness saturate during NA, confirm the concept of “vacancy prisons”, and suggest why clusters must be dissolved during AA before formation of beta ''. This atomistic understanding of NA may enable design of strategies to mitigate negative effects of NA.Graphical abstractEnergy and RMS vacancy displacement vs simulation time for three different kMC simulations of Al matrix containing Mg (green) and Si atoms(orange), and 1 vacancy (pink). Geometries extracted from the third trajectory at four different times show solute clustering and vacancy trapping.

Antigen-induced chimeric antigen receptor multimerization amplifies on-tumor cytotoxicity

Ligand-induced receptor dimerization or oligomerization is a widespread mechanism for ensuring communication specificity, safeguarding receptor activation, and facilitating amplification of signal transduction across the cellular membrane. However, cell-surface antigen-induced multimerization (dubbed AIM herein) has not yet been consciously leveraged in chimeric antigen receptor (CAR) engineering for enriching T cell-based therapies. We co-developed ciltacabtagene autoleucel (cilta-cel), whose CAR incorporates two B-cell maturation antigen (BCMA)-targeted nanobodies in tandem, for treating multiple myeloma. Here we elucidated a structural and functional model in which BCMA-induced cilta-cel CAR multimerization amplifies myeloma-targeted T cell-mediated cytotoxicity. Crystallographic analysis of BCMA–nanobody complexes revealed atomic details of antigen–antibody hetero-multimerization whilst analytical ultracentrifugation and small-angle X-ray scattering characterized interdependent BCMA apposition and CAR juxtaposition in solution. BCMA-induced nanobody CAR multimerization enhanced cytotoxicity, alongside elevated immune synapse formation and cytotoxicity-mediating cytokine release, towards myeloma-derived cells. Our results provide a framework for contemplating the AIM approach in designing next-generation CARs.

Catalytic Effects of Active Site Conformational Change in the Allosteric Activation of Imidazole Glycerol Phosphate Synthase.

Imidazole glycerol phosphate synthase (IGPS) is a class-I glutamine amidotransferase (GAT) that hydrolyzes glutamine. Ammonia is produced and transferred to a second active site, where it reacts with N1-(5'-phosphoribosyl)-formimino-5-aminoimidazole-4-carboxamide ribonucleotide (PrFAR) to form precursors to purine and histidine biosynthesis. Binding of PrFAR over 25 Å away from the active site increases glutaminase efficiency by ∼4500-fold, primarily altering the glutamine turnover number. IGPS has been the focus of many studies on allosteric communication; however, atomic details for how the glutamine hydrolysis rate increases in the presence of PrFAR are lacking. We present a density functional theory study on 237-atom active site cluster models of IGPS based on crystallized structures representing the inactive and allosterically active conformations and investigate the multistep reaction leading to thioester formation and ammonia production. The proposed mechanism is supported by similar, well-studied enzyme mechanisms, and the corresponding energy profile is consistent with steady-state kinetic studies of PrFAR + IGPS. Additional active site models are constructed to examine the relationship between active site structural change and transition-state stabilization via energy decomposition schemes. The results reveal that the inactive IGPS conformation does not provide an adequately formed oxyanion hole structure and that repositioning of the oxyanion strand relative to the substrate is vital for a catalysis-competent oxyanion hole, with or without the hVal51 dihedral flip. These findings are valuable for future endeavors in modeling the IGPS allosteric mechanism by providing insight into the atomistic changes required for rate enhancement that can inform suitable reaction coordinates for subsequent investigations.

Structural Insights and Mechanistic Understanding of Iron-Molybdenum Cofactor Biosynthesis by NifB in Nitrogenase Assembly Process.

NifB, a radical S-adenosylmethionine (SAM) enzyme, is pivotal in the biosynthesis of the iron-molybdenum cofactor (FeMo-co), commonly referred to as the M-cluster. This cofactor, located within the active site of nitrogenase, is essential for the conversion of dinitrogen (N2) to NH3. Recognized as the most intricate metallocluster in nature, FeMo-co biosynthesis involves multiple proteins and a sequence of steps. Of particular significance, NifB directs the fusion of two [Fe4S4] clusters to assemble the 8Fe core, while also incorporating an interstitial carbide. Although NifB has been extensively studied, its molecular mechanisms remain elusive. In this review, we explore recent structural analyses of NifB and provide a comprehensive overview of the established catalytic mechanisms. We propose prospective directions for future research, emphasizing the relevance to biochemistry, agriculture, and environmental science. The goal of this review is to lay a solid foundation for future endeavors aimed at elucidating the atomic details of FeMo-co biosynthesis.

One bead per residue can describe all-atom protein structures

Atomistic resolution is the standard for high-resolution biomolecular structures, but experimental structural data are often at lower resolution. Coarse-grained models are also used extensively in computational studies to reach biologically relevant spatial and temporal scales. This study explores the use of advanced machine learning networks for reconstructing atomistic models from reduced representations. The main finding is that a single bead per amino acid residue allows construction of accurate and stereochemically realistic all-atom structures with minimal loss of information. This suggests that lower resolution representations of proteins may be sufficient for many applications when combined with a machine learning framework that encodes knowledge from known structures. Practical applications include the rapid addition of atomistic detail to low-resolution structures from experiment or computational coarse-grained models. The application of rapid, deterministic all-atom reconstruction within multi-scale frameworks is further demonstrated with a rapid protocol for the generation of accurate models from cryo-EM densities close to experimental structures.

Molecular Organization of the Skin Barrier.

Cryo-electron microscopy of vitreous sections allows for investigation directly in situ of the molecular architecture of skin. Recently, this technique has contributed to the elucidation of the molecular organization of the skin's permeability barrier and its stepwise formation process. The aim of this review is to provide an overview of the procedure for cryo-electron microscopy of vitreous sections, its analysis using atomic detail molecular dynamics modelling and electron microscopy simulation, and its application in the investigation of the barrier structure and formation process of the skin.

Mechanisms of dissolution from gibbsite step edges elucidated by ab initio molecular dynamics with enhanced sampling

Surface reactions underpin our collective understanding of mineral dissolution. This impacts a range of predictive geochemical models (for weathering, the fate and transport of metals) as well as industrial utilization of minerals. Yet atomistic details are rarely known due to the complex mineral/fluid interfacial environment. There is significant need to understand the mechanistic details of dissolution reactions and how they depend on surface morphology and solution conditions. This work utilizes surface pit models in conjunction with changes to solution composition that mimic pH to explore surface detachment of aluminate from gibbsite, which is a primary source of Al in soils and within the industrial processing of aluminum. Ab initio molecular dynamics simulations with enhanced sampling has been used to explore the detailed process of the detachment, the results of which indicate two potential pathways that are differentiated based upon the extent of water hydration. The heights of the energy barriers depend upon the local morphology which influence the number of bridges (quasi-)simultaneously broken (1 or 2) or the Al-O coordination of the neighboring aluminum atoms (5 or 6) at the armchair edge. pH effects are significant, with a nearly 50% reduction in barrier height under alkaline conditions that are relevant to geothermal fluids and Al extraction from minerals.