Detailed Molecular Dynamics Simulations Research Articles

Origins of synergy in multilipid lubrication

Lipid bilayers, ubiquitous in living systems, form lubricious boundary layers in aqueous media, with broad relevance for biolubrication, especially in mechanically stressed environments such as articular cartilage in joints, as well as for modifying material interfacial properties. Model studies have revealed efficient lubricity by single-component lipid bilayers; synovial joints, however (e.g. hips and knees), comprise over a hundred different lipids, raising the question of whether this is natural redundancy or whether it confers any lubrication benefits. Here, we examine lubrication by progressively more complex mixtures of lipids representative of those in joints, using a surface forces balance at physiologically relevant salt concentrations and pressures. We find that different combinations of such lipids differ very significantly in the robustness of the bilayers to hemifusion under physiological loads (when lubrication breaks down), indicating a clear lubrication synergy afforded by multiple lipid types in the bilayers. Insight into the origins of this synergy is provided by detailed molecular dynamics simulations of potential profiles for the formation of stalks, the essential precursors of hemifusion, between bilayers of the different lipid mixtures used in the experiments. These reveal how bilayer hemifusion-and thus lubrication breakdown-depends on the detailed lipid bilayer composition, through the corresponding separation into domains that are better able to resist stalk formation. Our results shed light on the role of lipid-type proliferation in biolubrication synergy, point to improved treatment modalities for common joint diseases such as osteoarthritis, and indicate how lipid-based interfacial modification in a materials context may be optimized.

Deciphering the conformational changes induced by high-risk nsSNPs in β-lactoglobulin

Whey protein from bovine milk is highly valued in the food and pharmaceutical industries because of its high protein content and abundance of essential amino acids. The relationship between whey protein and the β-lactoglobulin (BLG) gene has been extensively discussed because BLG is the most abundant whey protein, making up approximately 50 % of the total whey protein in bovine milk. In recent years, researchers have been interested in this gene because of its critical role in healthy milk production, and any genetic polymorphism in this gene may deteriorate the milk quality. In the current study, we identified several deleterious and damaging non-synonymous single nucleotide polymorphisms (nsSNPs) in BLG and analyzed their destabilizing effects using different computational algorithms. Cumulative results from all tools and evolutionary conservation profiles of BLG suggested that four nsSNPs, G17A, W19C, F136S, and C119R, were the most deleterious and could affect the structural integrity of the protein. Detailed molecular dynamics simulation analysis revealed that all variants induced major structural alterations, that affected the ability of the protein to interact with natural and synthetic ligands. Particularly, the G17A, F136S, and C119R variants induced large conformational changes in the EF loop and main α-helix of BLG, which may affect the access of natural and synthetic ligands to the central calyx of BLG. We hope that the suggested nsSNPs will guide future studies and assist researchers in improving the quality of bovine milk.

Probing the Linear-to-Plastic Transition in Polymer Nanocomposites via Atomistic Simulations: The Role of Interphases.

Polymer nanocomposites have found ubiquitous use across diverse industries, attributable to their distinctive properties and enhanced mechanical performance compared to conventional materials. Elucidating the elastic-to-plastic transition in polymer nanocomposites under diverse mechanical loads is paramount for the bespoke design of materials with desired mechanical attributes. In the current work, the elastic-to-plastic transition is probed in model systems of polyethylene oxide (PEO) and silica, SiO2, nanoparticles, through detailed atomistic molecular dynamics simulations. This comprehensive, multi-scale analysis unveils pivotal markers of the elastic-to-plastic transition, highlighting the quintessential role of microstructural and regional heterogeneities in density, strain, and stress fields, featuring the polymer-nanoparticle interphase region. At the atomic level, the behavior of polymer chains interacting with nanoparticle surfaces is traced, differentiating between free and adsorbed chains, and identifying the microscopic origins of the linear-to-plastic transition. The mechanical behavior of subregions are characterized within the PEO/SiO2 nanocomposites, focusing on the interphase and bulk-like polymer areas, probing stress heterogeneities and their decomposition into various force contributions. At the inception of plasticity, a disruption is discerned in isotropy of the polymeric density field, the emergence of low-density regions, and microscopic voids/cavities within the polymer matrix concomitant with a transition of adsorbed chains to free. The yield strain also emerges as an inflection point in the local versus global strain diagram, demarcating the elastic limit, and the plastic regime shows pronounced strain heterogeneities. The decomposition of the atomic Virial stress into bonded and non-bonded interactions indicates that the rigidity of the material is primarily governed by non-bonded interactions, significantly influenced by the volume fraction of the nanoparticle. These findings emphasize the importance of the microstructural and micromechanical environment at the polymer-nanoparticle interface on the linear-to-plastic transition, which is of great importance in the design of nanocomposite materials with advanced mechanical properties.

Computational identification and analysis of CNP0269688 as a natural product inhibitor disrupting the interaction between the HIV matrix domain and tRNA.

Our research is dedicated to combating HIV by targeting its Matrix (MA) domain, which is crucial for viral assembly and replication. This strategy specifically aims to interrupt early-stage infection and deter drug resistance by focusing on this essential domain. Due to the MA domain's conservation across different HIV strains, our approach promises broad-spectrum efficacy, which is particularly crucial in regions marked by significant genetic diversity and resistance issues. In our study, we introduce CNP0269688, a natural product that exhibits high affinity for the HIV-1 Matrix. Through detailed molecular dynamics simulations, we have assessed the compound's structural stability and interaction dynamics, particularly its potential to hinder Protein-tRNA interactions. This analysis lays the groundwork for future experimental investigations. Our efforts are steps toward enhancing HIV treatment, reducing viral transmission, and curbing drug resistance, with the ultimate aim of controlling and eradicating the pandemic, thereby contributing significantly to public health and scientific advancement.

Multiscale Modeling of Vinyl-Addition Polynorbornenes: The Effect of Stereochemistry.

Vinyl-addition polynorbornenes are candidates for designing high-performance polymers due to unique characteristics, which include a high glass transition temperature associated with a rigid backbone. Recent studies have established that the processability and properties of these polymers can be fine-tuned by using targeted substitutions. However, synthesis with different catalysts results in materials with distinct properties, potentially due to the presence of various stereoisomers that are difficult to quantify experimentally. Herein, we develop all-atom models of polynorbornene oligomers based on classical force fields and density functional theory. To establish the relationship between chemical architecture, chain conformations, and melt structure, we perform detailed molecular dynamics simulations with the fine-tuned atomistic force field and propose simpler coarse-grained descriptions to address the high molecular weight limit. All-atom simulations of oligomers suggest high glass transition temperatures in the range of 550-600 K. In the melt state (800 K), meso chains form highly rigid extended coils (C∞≈11) with amorphous structural characteristics similar to the X-ray diffraction data observed in the literature. In contrast, simulations with racemo chains predict highly helical tubular chain conformations that could promote assembly into crystalline structures.

Predicting mechanical heterogeneity in glassy polymer nanocomposites via an inverse computational approach based on atomistic molecular simulations and homogenization methods

Probing the mechanical behavior of the region formed between a nanoparticle reinforcement and a polymer matrix in a polymer nanocomposite structure, denoted as the “interphase”, is a main challenge as such regions are difficult to investigate by experimental methods. Here, we accurately characterize the heterogeneous mechanical behavior of polymer nanocomposites, focusing on polymer/nanofiller interphases via a combination of nanomechanical simulations and numerical homogenization techniques. Initially, the global mechanical performance of a glassy poly(ethylene oxide) polymer nanocomposite reinforced with silica nanoparticles is studied using detailed atomistic molecular dynamics simulations for 1.9% and 12.7% silica volume fractions. Next, the polymer/silica interphase thickness is identified by probing the polymer atom-based density distribution profile in the vicinity of the nanofiller at equilibrium. On the basis of this thickness, the interphase is subdivided to check the position-dependent change in mechanical properties. Then, using continuum mechanics and atomistic simulations, we proceed to compute the effective Young’s modulus and Poisson’s ratio of the polymer/nanoparticle interphase as function of the distance from the nanoparticle. In the last step, an inverse numerical homogenization model is proposed to predict the mechanical properties of the interphase on the basis of a comparison criteria with the data from MD. The results were found to be acceptable, raising the possibility of accurately and efficiently predicting interfacial properties in nanostructured materials.

Sodium Triflate Water-in-Salt Electrolytes in Advanced Battery Applications: A First-Principles-Based Molecular Dynamics Study.

Offering a compelling combination of safety and cost-effectiveness, water-in-salt (WiS) electrolytes have emerged as promising frontiers in energy storage technology. Still, there is a strong demand for research and development efforts to make these electrolytes ripe for commercialization. Here, we present a first-principles-based molecular dynamics (MD) study addressing in detail the properties of a sodium triflate WiS electrolyte for Na-ion batteries. We have developed a workflow based on a machine learning (ML) potential derived from ab initio MD simulations. As ML potentials are typically restricted to the interpolation of the data points of the training set and have hardly any predictive properties, we subsequently optimize a classical force field based on physics principles to ensure broad applicability and high performance. Performing and analyzing detailed MD simulations, we identify several very promising properties of the sodium triflate as a WiS electrolyte but also indicate some potential stability challenges associated with its use as a battery electrolyte.

Interactions of NaCl with cellulose I$$\beta$$ crystal surfaces and the effect on cellulose hydration: a molecular dynamics study

AbstractCrystalline nanocellulose is widely used for example, in the paper-making and food industries, as support matrix material or reinforcement of polymer materials, but also in drug carrier and nanomedicine applications. Interestingly, aqueous solutions of cellulose are extremely sensitive to small amounts of added salt yet mere considerations of charge screening leave open questions regarding the mechanisms, especially for unmodified cellulose in aqueous solutions. Here, we map NaCl ion distributions and the effect of added NaCl salt on the hydration of I$$\beta$$ β cellulose nanocrystal (CNC) surfaces by atomistic detail molecular dynamics simulations with explicit water solvent. The simulations reveal the dependency of the hydration layers of the six surfaces of CNCs on the ions, as well as NaCl ion binding sites, and preferences in terms of binding free energy for the ions near CNC surfaces at different NaCl concentrations. We discuss the modelling results against our prior rheology characterization of cellulose solutions. Together, the results indicate that the high sensitivity of cellulose aqueous solutions to added salt rises from the ions near the surface changing locally the ordering and structure of the hydration layers of the CNC surfaces. The revealed mechanism of salt-induced viscosity changes in cellulose aqueous solutions allows advanced design of gelling CNC systems for various end uses and may also guide tuning cellulose interactions by different solvent environments.

Molecular dynamics simulation as a promising approach for computational study of liquid crystal-based aptasensors

As a potent computational methodology, molecular dynamics (MD) simulation provides advantageous knowledge about biological compounds from the molecular viewpoint. In particular, MD simulation gives exact information about aptamer strands, such as the short synthetic oligomers, their orientation, binding sites, folding-unfolding state, and conformational re-arrangement. Also, the effect of the different chemicals and biochemicals as the components of aptamer-based sensors (aptasensors) on the aptamer-target interaction can be investigated by MD simulation. Liquid crystals (LCs) as soft substances with characteristics of both solid anisotropy and liquid fluidity are new candidates for designing label-free aptasensors. To now, diverse aptasensors have been developed experimentally based on the optical anisotropy, fluidity, and long-range orientational order of LCs. Here, we represent a computational model of an LC-based aptasensor through a detailed MD simulation study. The different parameters are defined and studied to achieve a comprehensive understanding of the computational design of the LC-based aptasensor, including the density of LCs, their orientation angle, and lognormal distribution in the absence and presence of aptamer strands, both aptamer and target molecules with various concentrations, and interfering substance. As a case study, the tobramycin antibiotic is considered the target molecule for the computational model of the LC-based aptasensor. Communicated by Ramaswamy H. Sarma

Kinetics of phase separation and aging dynamics of segregating fluid mixtures in the presence of quenched disorder.

Quenched or frozen-in structural disorder is ubiquitous in real experimental systems. Much of the progress is achieved in understanding the phase separation of such systems using the diffusion-driven coarsening in an Ising model with quenched disorder. But there is a paucity of research on the phase-separation kinetics in fluids with quenched disorder. In this paper, we present results from a detailed molecular dynamics simulation, showing the effects of randomly placed localized impurities on the phase separation kinetics of binary fluid mixtures. Two different models are offered for representing the impurities. We observe a dramatic slowing down in the pattern formation with increasing impurity concentration. This sluggish domain growth kinetics follows a power-law with a disorder-dependent exponent. The correlation function and structure factor show a non-Porod behavior, indicating the roughening of the domain interfaces. We have also studied the effect of quenched disorder on the aging dynamics by calculating the two-time order parameter auto correlation function and find that the Fisher and Huse scaling law holds good in the presence of quenched disorder.

Iridium(III)-based minor groove binding complexes as DNA photocleavage agents.

Transition metal complexes containing the qtpy ligand (2':4,4'':4',4'''-quaterpyridyl) are known to be DNA intercalators or minor groove binders. In this study, new tricationic iridium(III) complexes of qtpy are reported. Both [Ir(bpy)2(qtpy)]3+1 and [Ir(phen)2(qtpy)]3+2 display good water solubility as chloride salts. The complexes possess high-energy excited states, which are quenched in the presence of duplex DNA and even by the mononucleotides guanosine monophosphate and adenosine monophosphate. Further studies reveal that although the complexes bind to quadruplex DNA, they display a preference for duplex structures, which are bound with an order of magnitude higher affinities than their isostructural dicationic RuII-analogues. Detailed molecular dynamics simulations confirm that the complexes are groove binders through the insertion of, predominantly, the qtpy ligand into the minor groove. Photoirradiation of 1 in the presence of plasmid DNA confirms that this class of complexes can function as synthetic photonucleases by cleaving DNA.

A detailed molecular dynamics simulation and experimental investigation of CO2 adsorption on graphene oxide-polyaniline nanocomposite

The study investigated the sorption and diffusion of carbon dioxide on Graphene oxide-polyaniline (GO-PANI) using volumetric method and molecular dynamics simulations. The experimental results showed that GO-PANI was more effective at adsorbing CO2 than either of the individual materials alone. Simulations supported this trend in CO2 adsorption and provided additional insights into the structural information of the adsorbents and the dynamics of CO2 molecules. The Langmuir adsorption model was employed to fit sorption isotherms computed using Grand Canonical Monte Carlo (GCMC) method. The GO-PANI nanocomposite compound exhibited a superior adsorption rate compared to pure structures. The isosteric heat for CO2 adsorption was 27.59 kJ/mol. The interaction energy of single molecules of CO2 on GO-PANI surface was −38.075 kcal/mol. Diffusion coefficient value for CO2 was 41.2 × 10 −9 m2/s, lower than that of the pure polymer. FT-IR, FE-SEM, and BET techniques were used to analyze synthesized GO-PANI structure. The highest CO2 adsorption was observed at a temperature of 283 K. Isosteric heat adsorption decreased with uniform surface charge. This research provides critical microscopic information on gas adsorption in polymer nanocomposites and demonstrates the effectiveness of GO-PANI as an efficient adsorbent for carbon dioxide, which could be useful in various industrial applications.

Analysis of bioactive compounds of Olea europaea as potential inhibitors of SARS-CoV-2 main protease: a pharmacokinetics, molecular docking and molecular dynamics simulation studies

COVID-19 is a highly infectious disease caused by a new type of extremely contagious coronavirus called SARS-CoV-2. The virus’s main protease enzyme, SARS-CoV-2 Mpro, is essential for its replication and transcription processes. Targeting this enzyme presents a promising avenue for antiviral drug development. Researchers have explored the intricate three-dimensional configurations of the enzyme, analyzing its interactions with various inhibitors. These findings provide a foundation for designing specific and powerful inhibitors targeting SARS-CoV-2 Mpro. Certain plants possess medicinal attributes due to the presence of bioactive compounds that inhibit pathogens. The olive tree (Olea europaea) has served as a source of food and medicine, containing bioactive compounds in its leaves that hinder the proliferation of various pathogens including viruses. This study explores the potential of bioactive compounds from olive leaf extract (OLE) to inhibit SARS-CoV-2 Mpro. In-silico study was conducted to predict the pharmacokinetic and toxicity profiles of these compounds. Molecular docking was utilized to assess their binding affinity to SARS-CoV-2 Mpro and their potential interference with its function. The top three compounds, apigenin (Api), luteolin-7-O-glucoside (Lut) and rutin (Rut), were chosen based on their favorable drug-like properties and strong binding affinities to Mpro. Detailed molecular dynamics simulations demonstrated the stability of SARS-CoV-2 Mpro in conjunction with these compounds, showing minimal structural alterations over the simulation period. Particularly, Lut and Rut formed bonds with critical amino acid residues His41 and Cys145 of Mpro, suggesting their potential inhibitory effect. These findings suggest that these compounds hold promise as natural drug candidates for combating COVID-19.Communicated by Ramaswamy H. Sarma

Spidroins under the Influence of Alcohol: Effect of Ethanol on Secondary Structure and Molecular Level Solvation of Silk-Like Proteins.

Future sustainable materials based on designer biomolecules require control of the solution assembly, but also interfacial interactions. Alcohol treatments of protein materials are an accessible means to this, making understanding of the process at the molecular level of seminal importance. We focus here on the influence of ethanol on spidroins, the main proteins of silk. By large-scale atomistically detailed molecular dynamics (MD) simulations and interconnected experiments, we characterize the protein aggregation, secondary structure changes, molecular level origins of them, and solvation environment changes for the proteins, as induced by ethanol as a solvation additive. The MD and circular dichoroism (CD) findings jointly show that ethanol promotes ordered structure in the protein molecules, leading to an increase of helix content and turns but also increased aggregation, as revealed by dynamic light scattering (DLS) and light microscopy. The structural changes correlate at the molecular level with increased intramolecular hydrogen bonding. The simulations reveal that polar amino acids, such as glutamine and serine, are most influenced by ethanol, whereas glycine residues are most prone to be involved in the ethanol-induced secondary structure changes. Furthermore, ethanol engages in interactions with the hydrophobic alanine-rich regions of the spidroin, significantly decreasing the hydrophobic interactions of the protein with itself and its surroundings. The protein solutes also change the microstructure of water/ethanol mixtures, essentially decreasing the level of larger local clustering. Overall, the work presents a systematic characterization of ethanol effects on a widely used, common protein type, spidroins, and generalizes the findings to other intrinsically disordered proteins by pinpointing the general features of the response. The results can aid in designing effective alcohol treatments for proteins, but also enable design and tuning of protein material properties by a relatively controllable solvation handle, the addition of ethanol.

Solvation of Nanoions in Aqueous Solutions.

In recent years it has been increasingly recognized that different classes of large ions with multiple valency have effects conceptually similar to weakly solvated ions in the Hofmeister series, also labeled by the term chaotropic. The term "superchaotropic effect" has been coined because these effects are much more strongly pronounced for nanometer-sized ions, whose adsorption properties often resemble typical surfactants. Despite this growing interest in these nanometer-sized ions, a simple conceptual extension of the Hofmeister series toward nanoions has not been achieved because an extrapolation of the one-dimensional surface charge density scale does not lead to the superchaotropic regime. In this work, we discuss a generic model that is broadly applicable to ions of nearly spherical shape and thus includes polyoxometalates and boron clusters. We present a qualitative classification scheme in which the ion size appears as a second dimension. Ions of different sizes but the same charge density differ in their bulk solvation free energy. As the ions grow bigger at constant surface charge density, they become more stable in solution, but the adsorption behavior is still governed by the surface charge density. A detailed molecular dynamics simulation study of large ions that is based on a shifted Lennard-Jones potential is presented that supports the presented classification scheme.

Masking the transmembrane region of the amyloid β precursor protein as a safe means to lower amyloid β production.

Reducing brain levels of both soluble and insoluble forms of amyloid beta (Aβ) remains the primary goal of most therapies that target Alzheimer's disease (AD). However, no treatment has so far resulted in patient benefit, and clinical trials of the most promising drug candidates have generally failed due to significant adverse effects. This highlights the need for safer and more selective ways to target and modulate Aβ biogenesis. Peptide technology has advanced to allow reliable synthesis, purification, and delivery of once-challenging hydrophobic sequences. This is opening up new routes to target membrane processes associated with disease. Here we deploy a combination of atomic detail molecular dynamics (MD) simulations, living-cell Förster resonance energy transfer (FRET), and in vitro assays to elucidate the atomic-detail dynamics, molecular mechanisms, and cellular activity and selectivity of a membrane-active peptide that targets the Aβ precursor protein (APP). We demonstrate that Aβ biogenesis can be downregulated selectively using an APP occlusion peptide (APPOP). APPOP inhibits Aβ production in a dose-dependent manner, with a mean inhibitory concentration (IC50) of 450nM toward exogenous APP and 50nM toward endogenous APP in primary rat cortical neuronal cultures. APPOP does not impact the γ-secretase cleavage of Notch-1, or exhibit toxicity toward cultured primary rat neurons, suggesting that it selectively shields APP from proteolysis. Drugs targeting AD need to be given early and for very long periods to prevent the onset of clinical symptoms. This necessitates being able to target Aβ production precisely and without affecting the activity of key cellular enzymes such as γ-secretase for other substrates. Peptides offer a powerful way for targeting key pathways precisely, thereby reducing the risk of adverse effects. Here we show that protecting APP from proteolytic processing offers a promising route to safely and specifically lower Aβ burden. In particular, we show that the amyloid pathway can be targeted directly and specificically. This reduces the risk of off-target effects and paves the way for a safe prophylactic treatment.

Mechanistic insights into the allosteric inactivation mechanism of ZAP-70 induced by the hot spot W165C mutation

Zeta chain-associated protein kinase 70 (ZAP-70) is a non-receptor tyrosine kinase that interacts with the activated T-cell receptor to transduce downstream signals, and thus plays an important role in the adaptive immune system. The biphosphorylated immunotyrosine-based activation motifs (ITAM-Y2P) binds to the N-SH2 and C-SH2 domains of ZAP-70 to promote the activation of ZAP-70. The present study explores molecular mechanisms of allosteric inactivation of ZAP-70 induced by the hot spot W165C mutation through atomically detailed molecular dynamics simulation approaches. We report microsecond-length simulations of two states of the tandem SH2 domains of ZAP-70 in complex with the ITAM-Y2P motif, including the wild-type and W165C mutant. Extensive analysis of local flexibility and dynamical correlated motions show that W165C mutation changes coupled motions of protein domains and community networks. The binding affinities of the ITAM-Y2P motif to the wild-type and W165C mutant of ZAP-70 are predicted using binding free energy calculations. The results suggest that the driving force to decrease the binding affinity in the W165C mutant derives from the difference in the protein-protein electrostatic interactions. Moreover, the per-residue free energy decomposition unravels that the contributions from residues in the phosphorylated Tyr315 (pY315) binding site, in particular pY315 of ITAM-Y2P, and Arg43, Tyr240 of ZAP-70, are the key determinants for the loss of binding affinity. This study may insights into our understanding of the pathological mechanism of ZAP-70. Communicated by Ramaswamy H. Sarma

Deep neural network-based reduced-order modeling of ion–surface interactions combined with molecular dynamics simulation

With the advent of complex and sophisticated architectures in semiconductor device manufacturing, atomic-resolution accuracy and precision are commonly required for industrial plasma processing. This demands a comprehensive understanding of the plasma–material interactions—particularly for forming fine high-aspect ratio (HAR) feature patterns with sufficiently high yield in wafer-level processes. In particular, because the shape distortion in HAR pattern etching is attributed to the deviation of the energetic ion trajectory, the detailed ion–surface interactions need to be thoroughly investigated. In this study, molecular dynamics (MD) simulations were utilized to obtain a fundamental understanding of the collisional nature of accelerated Ar ions on the fluorinated Si surface that may appear on the sidewall of the HAR etched hole. High-fidelity data for ion–surface interaction features representing the energy and angle distributions (EADs) of sputtered atoms for varying degrees of surface F coverage and ion incident angles were obtained via extensive MD simulations. A deep learning-based reduced-order modeling (DL-ROM) framework was developed for efficiently predicting the characteristics of the ion–surface interactions. In the ROM framework, a conditional variational autoencoder (AE) was implemented to obtain regularized latent representations of the distributional data with the condition of the governing factors of the physical system. The proposed ROM framework accurately reproduced the MD simulation results and significantly outperformed various DL-ROMs, such as AE, sparse AE, contractive AE, denoising AE, and variational AE. From the inferred features of the sputtering yield and EADs of sputtered/scattered species, significant insights can be obtained regarding the ion interactions with the fluorinated surface. As the ion incident angle deviated from the glancing-angle range (incident angle >80°), diffuse reflection behavior was observed, which can substantially affect the ion transport in the HAR patterns. Moreover, it was hypothesized that a shift in sputtering characteristics occurs as the surface F coverage varies, based on the inferred EADs. This conjecture was confirmed through detailed MD simulations that demonstrated the fundamental relationship between surface atomic conformations and their sputtering behavior. Combined with additional atomistic-scale investigations, this framework can provide an efficient way to reveal various fundamental plasma–material interactions which are highly demanded for the future development of semiconductor device manufacturing.

Self-Assembly of Phenylalanine-Leucine, Leucine-Phenylalanine, and Cyclo(-leucine-phenylalanine) Dipeptides through Simulations and Experiments.

For over two decades, peptide self-assembly has been the focus of attention and a great source of inspiration for biomedical and nanotechnological applications. The resulting peptide nanostructures and their properties are closely related to the information encoded within each peptide building block, their sequence, and their modes of self-organization. In this work. we assess the behavior and differences between the self-association of the aromatic-aliphatic Phe-Leu dipeptide compared to its retro-sequence Leu-Phe and cyclic Cyclo(-Leu-Phe) counterparts, using a combination of simulation and experimental methods. Detailed all-atom molecular dynamics (MD) simulations offer a quantitative prediction at the molecular level of the conformational, dynamical and structural properties of the peptides' self-assembly, while field emission scanning electron microscopy (FESEM) experiments allow microscopic observation of the self-assembled end-structures. The complementarity and qualitative agreement between the two methods not only highlights the differences between the self-assembly propensity of cyclic and linear retro-sequence peptides but also sheds light on underlying mechanisms of self-organization. The self-assembling propensity was found to follow the order: Cyclo(-Leu-Phe) > Leu-Phe > Phe-Leu.

Identifying novel risk conferring genes involved in glycosylation processes with familial schizophrenia in an Indian cohort: Prediction of ADAMTS9 gene variant for structural stability

Schizophrenia is a complex neuropsychiatric disorder and heritability is as high as 80 % making it the most heritable mental disorder. Although GWAS has identified numerous variants, the pathophysiology is still elusive. Here, an attempt was made to identify genetic risk factors in familial cases of schizophrenia that are associated with a common causative pathway. To achieve this objective, exome sequencing was done in 4 familial cases and identified six unique coding variants in five genes. Among these genes, PIGQ gene has two pathogenic variants, one nonsense and in-frame deletion. One missense variant in GALNT16 and one in GALNT5 have variable damaging score, however, the other variants, in ADAMTS9 and in LTBP4 have the highest damaging score. Further analysis showed that the variant of LTBP4 was not present in the functional domain. The other missense variant in the ADAMTS9 gene was found to be significant and was present in the thrombospondin repeat motif, one of the important motifs. Detailed molecular dynamics simulation study on this variant showed a damaging effect on structural stability. Since, all these genes culminated into the glycosylation process, it was evident that an aberrant glycosylation process may be one of the risk factors. Although, extracellular matrix formation through glycosylation have been shown to be associated, the involvement of ADAMTS9 and PIGQ gene mediated glycosylation has not been reported. In this paper, a novel link between ADAMTS9 and PIGQ gene with schizophrenia have been reported. Therefore, this novel observation has contributed immensely to the existing knowledge on risk factor of Schizophrenia.