Detailed Molecular Dynamics Simulations Research Articles

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.

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.

Partial Denaturation of Double-Stranded DNA on Pristine Graphene under Physiological-like Conditions

Interactions between DNA and graphene are paramount for a wide range of applications, such as biosensing and nanoelectronics; nonetheless, the molecular details of such interactions remain largely unexplored. We employ atomically detailed molecular dynamics simulations with an enhanced sampling technique to investigate the adsorption and mobility of double-stranded DNA along the basal plane of graphene, in an electrolytic aqueous medium. The study focuses on physiologically relevant conditions, using a buffer of [NaCl] = 134 mM. DNA physisorption is shown to be fast and irreversible, leading to deformation and partial melting of the double helix as a result of π–π stacking between the terminal nucleobases and graphene. Denaturation occurs primarily at the termini, with ensemble averaged H-bond ratios of 47.8–62%; these can, however, reach a minimum of 15%. Transition between free-energy minima occurs via a thermodynamical pathway driving the nucleic acid from a radius of gyration of 1.5 nm to 1.35 nm. Mobility along the basal plane of graphene is dominant, accounting for ~90% of all centre-of-mass translation and revealing that the DNA’s apparent diffusivity is similar to diffusion along the endohedral volume of carbon nanotubes, but one order of magnitude faster than in other 2D materials, such as BC3 and C3N.

Implementation of probe rheology simulation technique in atomistically detailed molecular dynamics simulations

The probe rheology simulation technique is a technique for measuring the viscosity of a fluid by measuring the motion of an inserted probe particle. This approach has the benefit of greater potential accuracy at a lower computational cost than other conventional simulation techniques used for the calculation of mechanical properties, such as the Green-Kubo approach and nonequilibrium molecular dynamics simulations, and the potential to allow for sampling local variations of properties. This approach is implemented and demonstrated for atomistically detailed models. The viscosity of four different simple Newtonian liquids is calculated from both the Brownian motion (passive mode) and the forced motion (active mode) of an embedded probe particle. The probe particle is loosely modeled as a nano-sized diamond particle: a rough sphere cut out of an FCC lattice made of carbon atoms. The viscosities obtained from the motion of the probe particle are compared with those obtained from the periodic perturbation method, and good agreement between the two sets of values is observed once the probe-fluid interaction strength (i.e., in the pair-wise Lennard-Jones interaction) is two times higher than their original values, and the artificial hydrodynamic interactions between the probe particle and its periodic images are accounted for. The success of the proposed model opens new opportunities for applying such a technique in the rheological characterization of local mechanical properties in atomistically detailed molecular dynamics simulations, which can be directly compared with or help guide experiments of similar nature.

Linagliptin and Empagliflozin Inhibit Microtubule Affinity Regulatory Kinase 4: Repurposing Anti-Diabetic Drugs in Neurodegenerative Disorders Using In Silico and In Vitro Approaches.

Type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) are significant public health burdens. Many studies have revealed the possibility of common pathophysiology between T2DM and AD. Thus, in recent years, studies deciphering the action mechanism of anti-diabetic drugs with their future use in AD and related pathologies are on high demand. Drug repurposing is a safe and effective approach owing to its low cost and time-saving attributes. Microtubule affinity regulating kinase 4 (MARK4) is a druggable target for various diseases and is found to be linked with AD and diabetes mellitus. MARK4 plays a vital role in energy metabolism and regulation and thus serves as an irrefutable target to treat T2DM. The present study was intended to identify the potent MARK4 inhibitors among FDA-approved anti-diabetic drugs. We performed structure-based virtual screening of FDA-approved drugs to identify the top hits against MARK4. We identified five FDA-approved drugs having an appreciable affinity and specificity toward the binding pocket of MARK4. Among these identified hits, two drugs, linagliptin, and empagliflozin, favorably bind to the MARK4 binding pocket, interacting with its critical residues and thus subjected to detailed analysis. All-atom detailed molecular dynamics (MD) simulations revealed the dynamics of binding of linagliptin and empagliflozin with MARK4. Kinase assay showed significant inhibition of MARK4 kinase activity in the presence of these drugs, implying them as potent MARK4 inhibitors. In conclusion, linagliptin and empagliflozin may be promising MARK4 inhibitors, which can further be exploited as potential lead molecules against MARK4-directed neurodegenerative diseases.

Evolution of energy landscapes in designer enzymes.

Bottom-up design of biocatalysts provides a unique opportunity to study how the dynamic motions of enzymes change as catalytic efficiency evolves. Using detailed biophysical and molecular dynamics simulations of enzymes designed to promote a simple proton transfer reaction, we have shown that a chemical reaction can be speeded up by modulating protein conformation landscapes to cooperatively preorganize catalytic residues to enhance transition-state stabilization while minimizing competing, unproductive conformations. Moreover, the general tightening of correlated dynamical networks that include the transition state and large parts of the protein was found to induce an activation heat capacity, leading to a non-linear temperature‑activity dependence. These findings suggest that explicit consideration of both static and dynamic computational approaches could aid the design of many de novo enzymes.

Csk αC Helix: A Computational Analysis of an Essential Region for Conformational Transitions.

Conformational changes are an essential feature for the function of some dynamic proteins. Understanding the mechanism of such motions may allow us to identify important properties, which may be directly related to the regulatory function of a protein. Also, this knowledge may be employed for a rational design of drugs that can shift the balance between active and inactive conformations, as well as affect the kinetics of the activation process. Here, the conformational changes in carboxyl-terminal Src kinase, the major catalytic repressor to the Src family of kinases, was investigated, and it was proposed as a functionally related hypothesis. A Cα Structure-Based Model (Cα-SBM) was applied to provide a description of the overall conformational landscape and further analysis complemented by detailed molecular dynamics simulations. As a first approach to Cα-SBM simulations, reversible transitions between active (closed) and inactive (open) forms were modeled as fluctuations between these two energetic basins. It was found that, in addition to the interdomain Carboxyl-terminal SRC Kinase (Csk) correlated motions, a conformational change in the αC helix is required for a complete conformational transition. The result reveals this as an important region of transition control and domain coordination. Restrictions in the αC helix region of the Csk protein were performed, and the analyses showed a direct correlation with the global conformational changes, with this location being propitious for future studies of ligands. Also, the Src Homology 3 (SH3) and SH3 plus Src Homology 2 (SH2) domains were excluded for a direct comparison with experimental results previously published. Simulations where the SH3 was deleted presented a reduction of the transitions during the simulations, while the SH3-SH2 deletion vanishes the Csk transitions, corroborating the experimental results mentioned and linking the conformational changes with the catalytic functionality of Csk. The study was complemented by the introduction of a known kinase inhibitor close to the Csk αC helix region where its consequences for the kinetic behavior and domain displacement of Csk were verified through detailed molecular dynamics. The findings describe the mechanisms involving the Csk αC helix for the transitions and also support the dynamic correlation between SH3 and SH2 domains against the Csk lobes and how local energetic restrictions or interactions in the Csk αC helix can play an important role for long-range motions. The results also allow speculation if the Csk activity is restricted to one specific conformation or a consequence of a state transition, this point being a target for future studies. However, the αC helix is revealed as a potential region for rational drug design.

The viscosity of liquid ethene: Measurement and molecular dynamic simulation

This work reports new experimental data and detailed equilibrium molecular dynamics (EMD) simulations of viscosity for liquid ethene. The measurements were carried out by using an improved vibrating-wire viscometer for temperatures from (173.150 to 233.150) K and pressures up to 5.5 MPa, with a combined expanded relative uncertainty of 5.3 % (k = 2). The experimental data were compared with the values calculated by the correlation of Holland et al. (with an uncertainty of 10 % for liquid), the deviations vary from − 7.2 % to 0.8 % and the AARD is 3.6 %. Furthermore, the Assael and Dymond scheme based on the hard-sphere model was used to correlate the experimental results with an AARD of 0.1 %. For understanding the structure and properties of liquid ethene at the molecular level, a comprehensive evaluation of ten different ethene force fields with four types of molecular configurations (two-site, three-site, four-site, and six-site models) was then performed to predict density and viscosity at a wide temperature range from (133.150 to 233.150) K and pressure of 5 MPa. The selected force fields were generally developed for the vapor–liquid equilibrium properties, without explicit consideration of viscosity and other transport properties. Overall, three rigid two-site force fields (SET, TraPPE-UA, and AUA4) gave the best predictions with the AARD within 0.32 % for density and 8.5 % for viscosity comparing with the correlations of Smukala et al. and Holland et al., separately. Mie force field with 16–6 potential and TraPPE-UA2 force field with two negative partial charges can accurately predict density but severely overestimate viscosity greater than 20 %. The larger intermolecular repulsion and electrostatic interaction may have contributed to the overestimation of viscosity especially at low temperatures.