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© 2001 The American Physical Society. We wish to thank the financial support of the Spanish National Office for Science and Technology under grant TIC98/0740 and the technical assistance received from the ion implantation facility “CAI—Implantación Iónica” of the University of Madrid. Dr. E. Martínez from the Department of Chemistry of the University of Murcia is thanked for valuable comments and suggestions., Hydrogen and nitrogen release processes in amorphous silicon nitride dielectrics have been studied by MeV ion scattering spectrometry in combination with infrared spectroscopy. The outdiffusion of those light constituents was activated by the thermal energy supplied to the samples by rapid thermal annealing treatments. Molecular models of how these reactions proceed have been proposed based on the information obtained from the infrared spectra, and the validity of the models has been tested by an analysis of the activation energy of the desorption processes. For this purpose, the evolution of the hydrogen concentration versus the annealing temperature was fitted to an Arrhenius-type law obtained from a second-order kinetics formulation of the reactions that are described by the proposed structural models. It was found that the low values of the activation, energies can be consistently explained by the formation of hydrogen bonding interactions between Si-H or N-H groups and nearby doubly occupied nitrogen orbitals. This electrostatic interaction debilitates the Si-H or N-H bond and favors the release of hydrogen. The detailed mechanism of this process and the temperature range in which it takes place depend on the amount and the proportion of hydrogen in SI-H and N-H bonds. Samples with higher nitrogen content, in which all bonded hydrogen is in the form of N-H bonds, are more stable upon annealing than samples in which both Si-H and N-H bonds are detected. in those nitrogen-rich films only a loss of hydrogen is detected at the highest annealing temperatures., Spanish National Office for Science and Technology, Depto. de Estructura de la Materia, Física Térmica y Electrónica, Fac. de Ciencias Físicas, TRUE, pub

Background: Most experimentally determined structures of biomolecules lack annotated hydrogen positions due to their low electron density. However, thorough structure analysis and simulations require knowledge about the positions of hydrogen atoms. Existing methods for their prediction are either limited to a certain range of molecules or only work effectively on small compounds. Results: We present a novel algorithm that compiles fragments of molecules with known hydrogen atom positions into a library. Using this library the method is able to predict hydrogen positions for molecules with similar moieties. We show that the method is able to accurately assign hydrogen atoms to most organic compounds including biomacromolecules, if a sufficiently large library is used. Conclusions: We bundled the algorithm into the open-source Python package and command line program Hydride. Since usually no additional parametrization is necessary for the problem at hand, the software works out-of-box for a wide range of molecular systems usually within a few seconds of computation time. Hence, we believe that Hydride could be a valuable tool for structural biologists and biophysicists alike.

The transition metal iron is a cheap and abundant element. It has a versatile chemistry, which makes iron compounds ideal candidates for substituting critical elements such as platinum or palladium. These noble metals are frequently used as electrocatalysts, for which iron compounds have been investigated as possible alternatives. In particular, iron and nitrogen doped carbon (FeNC) materials have attracted significant interest due to their high electrocatalytic activity towards the oxygen reduction reaction (ORR). The ORR, the cathode reaction in fuel cells, is kinetically hindered in comparison to the oxidation of fuel on the anode side, and therefore requires high amounts of catalysts. By substituting expensive platinum cathode catalysts by FeNC catalysts, hydrogen fuel cell technology for green automotive applications can be made broadly accessible. Even though FeNC catalysts have been optimized over the last decades, they still lack long-term stability which hinders their utilization on an industrial scale. FeNC catalysts are synthesized by a mixture of iron, nitrogen and carbon precursors including at least one pyrolysis step, and are thus rendered as highly amorphous materials with functionalized carbon and several iron phases. The iron phase that dominates ORR activity contains single iron sites, so-called FeN4 centers, where iron is coordinated by nitrogen and embedded within a carbon matrix. However, their exact structure, their ORR mechanism and possible degradation mechanisms are not yet fully understood. It is still under debate whether the FeN4 sites are coordinated by pyrrolic (five-membered rings) or pyridinic nitrogen (six-membered rings), and furthermore whether axial ligands are present, and if so, which type. The exact structure determines the iron oxidation and spin states, crucial information for the catalytic mechanism that remains to be clarified. To unravel the exact nature of the FeNC active site(s), several spectroscopic techniques are use

Construction of atomic-scale three-dimensional (3D) molecular models of unoxidized and oxidized activated carbon fiber (ACF) nanoregions is of substantial scientific and technological interest as it allows in-depth analysis of complex interfacial process. The influence of oxygen functionalities on the specific interactions with guest particles is an important factor that controls wettability, competitive adsorption of fluid mixtures, interfacial transport, reactions, or nucleation processes. We combine experimental techniques of wide-angle X-ray scattering and nitrogen adsorption porosimetry with advanced molecular modelling to construct reliable 3D molecular models of unoxidized and oxidized pitch-based ACF-5 nanoregions. ACF-5 is a ultramicroporous carbon molecular sieve with an average pore size of 0.46 ± 0.2 nm. Narrow hydrophobic nanospaces are formed between defective and slightly curved graphene-like sheets. By atomistic-scale modelling of formaldehyde adsorption at room temperature and different values of the relative humidities, we show that oxygen functionalities and water co-adsorption impact the mechanism and uptake of formaldehyde. Our simulation data indicate that hydrophobic nanopores are preferable to adsorb formaldehyde efficiently in the presence of humidity.

Chemistry as a discipline relies on cognitive interactions with models of atoms/molecules. Although molecular dynamism highlights the limits of molecular modelling, there is still consensus that modelling chemical structures is epistemologically pragmatic and realistic. While modelling can be performed through drawing, the representation of 3D structures in 2D sketches requires extensive visuospatial interpretation. Physical molecular modelling kits overcome these limitations. Given that first-year chemistry students struggle to simultaneously develop analytic strategies while grappling with 3D visualisation, molecular models offer a pedagogically pragmatic learning support tool for this cohort. Despite its pedagogical niche in chemistry, molecular models are physical resources and therefore have a limited mobility. It is no surprise that COVID-19 has diffused university campuses worldwide into predominately digital networks. The digital learning immaturity in the higher education sector together with the rapid and under-funded digital transformation required of universities in early-mid 2020 leaves little consideration for physical learning resources. There is currently no peer-reviewed literature of molecular model use in education since the arrival of COVID-19. I have therefore analysed YouTube uploads from chemistry instructors to determine how they have adapted traditional molecular modelling workshops for online learning. From this analysis, three fates for molecular models emerge: they are constructed with at-home items, manipulated and filmed by the instructor, or replaced with a virtual modelling tool. None of these approaches fully exploit the pedagogical value of molecular modelling kits. I will discuss the need to better utilise our molecular modelling kits for our chemistry students in a COVID world.

A conceptual link between Hume-Rothery inspired complexes/clusters and solid-state intermetallic catalysts with special emphasis on the heterogeneous semi hydrogenation of alkynes is presented. Quantum chemical calculations on the DFT level of theory reveal that [Ni(M'R)n(UHC)4-n] (M’ = Al, Ga, Zn, R = Cp*/Me, UHC = unsaturated hydrocarbons) complexes represent potential molecular models for substrate binding on related intermetallic surfaces. Furthermore, synthetic approaches towards such compounds are presented., In der vorliegenden Arbeit wird ein konzeptueller Vergleich zwischen molekularen Hume-Rothery inspirierten Komplexen/Clustern und intermetallischen Festphasen-Katalysatoren vorgestellt. Dabei wird das Hauptaugenmerk auf die katalytische Semihydrierung von Alkinen gelegt. Quantenchemische Rechnungen auf dem DFT-Level weisen darauf hin, dass Komplexe mit der allgemeinen Formel [Ni(M'R)n(UHC)4-n] (M’ = Al, Ga, Zn, R = Cp*/Me, UHC = ungesättigte Kohlenwasserstoffe) potentielle molekulare Modelle für Substrat-Katalysator Wechselwirkungen darstellen. Außerdem werden synthetische Ansätze zu solchen Komplexen präsentiert.

A conceptual link between Hume-Rothery inspired complexes/clusters and solid-state intermetallic catalysts with special emphasis on the heterogeneous semi hydrogenation of alkynes is presented. Quantum chemical calculations on the DFT level of theory reveal that [Ni(M'R)n(UHC)4-n] (M’ = Al, Ga, Zn, R = Cp*/Me, UHC = unsaturated hydrocarbons) complexes represent potential molecular models for substrate binding on related intermetallic surfaces. Furthermore, synthetic approaches towards such compounds are presented., In der vorliegenden Arbeit wird ein konzeptueller Vergleich zwischen molekularen Hume-Rothery inspirierten Komplexen/Clustern und intermetallischen Festphasen-Katalysatoren vorgestellt. Dabei wird das Hauptaugenmerk auf die katalytische Semihydrierung von Alkinen gelegt. Quantenchemische Rechnungen auf dem DFT-Level weisen darauf hin, dass Komplexe mit der allgemeinen Formel [Ni(M'R)n(UHC)4-n] (M’ = Al, Ga, Zn, R = Cp*/Me, UHC = ungesättigte Kohlenwasserstoffe) potentielle molekulare Modelle für Substrat-Katalysator Wechselwirkungen darstellen. Außerdem werden synthetische Ansätze zu solchen Komplexen präsentiert.

We develop fully glycosylated computational models of ACE2-Fc fusion proteins which are promising targets for a COVID-19 therapeutic. These models are tested in their interaction with a fragment of the receptor-binding domain (RBD) of the Spike Protein S of the SARS-CoV-2 virus, via atomistic molecular dynamics simulations. We see that some ACE2 glycans interact with the S fragments, and glycans are influencing the conformation of the ACE2 receptor. Additionally, we optimize algorithms for protein glycosylation modelling in order to expedite future model development. All models and algorithms are openly available.

Cooperation between two different metals plays a crucial role in many synergistic catalytic reactions, such as the Sonogashira C-C cross-coupling reaction, where an interaction between the Pd and Cu centers is proposed in the transmetalation step. Although several heterobimetallic Pd/Cu complexes were proposed as structural models of the active species in Sonogashira coupling, the detailed understanding of the metal-metal cooperation in transmetalation is still lacking in current systems. In this work, we report a stepwise and systematic approach to building heteromultimetallic Pd/Cu assemblies as a tool to study metal-metal cooperativity. We obtained fully characterized Pd/Cu multimetallic assemblies that show reactivity in alkyne activation, formation of catalytically relevant aryl/acetylide species, and C-C elimination, serving as functional models for Sonogashira reaction intermediates. The combined experimental and DFT studies highlight the importance of ligand-controlled coordination geometry, metal-metal distances and dynamics of the multimetallic assembly for transmetalation step., source:https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/chem.202002013

Molybdenum disulfide (MoS2) is a layered material with outstanding electrical and optical properties. Numerous studies evaluate the performance in sensors, catalysts, batteries, and composites that can benefit from guidance by simulations in all-atom resolution. However, molecular simulations remain difficult due to lack of reliable models. We introduce an interpretable force field for MoS2 with record performance that reproduces structural, interfacial, and mechanical properties in 0.1% to 5% agreement with experiments. The model overcomes structural instability, deviations in interfacial and mechanical properties by several 100%, and empirical fitting protocols in earlier models. It is compatible with several force fields for molecular dynamics simulation, including the interface force field (IFF), CVFF, DREIDING, PCFF, COMPASS, CHARMM, AMBER, and OPLS-AA. The parameters capture polar covalent bonding, X-ray structure, cleavage energy, infrared spectra, bending stability, bulk modulus, Youngs modulus, and contact angles with polar and nonpolar solvents. We utilized the models to uncover the binding mechanism of peptides to the MoS2 basal plane. The binding strength of several 7mer and 8mer peptides scales linearly with surface contact and replacement of surface-bound water molecules, and is tunable in a wide range from -86 to -6 kcal mol-1. The binding selectivity is multifactorial, including major contributions by van-der-Waals coordination and charge matching of certain side groups, orientation of hydrophilic side chains towards water, and conformation flexibility. We explain the relative attraction and role of the 20 amino acids using computational and experimental data. The force field can be used to screen and interpret the assembly of MoS2-based nanomaterials and electrolyte interfaces up to a billion atoms with high accuracy, including multiscale simulations from the quantum scale to the microscale.

1:1 metal complexes of small crown ethers are structurally similar to extraframework sites in metal-exchanged zeolites. Using ab initio calculations, we show that adsorbed molecular hydrogen follows the same trends in adsorption energies and vibrational frequencies at both types of metal sites. Unlike zeolites, crown ethers can be characterized in the gas phase, which opens new possibilities for understanding the bonding of dihydrogen at undercoordinated metal sites to help guide the rational design of porous materials for hydrogen isotope separation. Because more strongly binding adsorbates affect the geometry of the hosts, the similarity of crown ethers and zeolites with regard to the vibrational spectra of the adsorbed molecule seems to be limited to H₂.

The accurate analyses of massive amounts of data obtained through next¿generation sequencing depend on the selection of appropriate evolutionary models. Many plastid phylogenomic studies typically analyze plastome data as a single partition, or divided by a region, using a concatenate ¿supergene¿ approach. The effects of molecular evolutionary models and character partition strategies on plastome¿based phylogenies have generally been evaluated at higher taxonomic levels in green plants. Using plastome data from 32 species of Amphilophium, a genus of Neotropical lianas, we explored potential sources of topological incongruence with different plastid genome datasets and approaches. Specifically, we evaluated the effects of compositional heterogeneity, codon usage bias, positive selection, and incomplete lineage sorting as sources of systematic error (i.e., the recovery of well¿supported conflicting topologies). We compared different datasets (e.g., non¿coding regions, exons, and codon¿aligned and translated amino acids) using concatenated approaches under site¿heterogeneous and site¿homogeneous models, as well as multispecies coalescent (MSC) methods. We found incongruences in recovered phylogenetic relationships, which were mainly located in short internodes. The MSC and concatenated approaches recovered similar topologies. The analysis of GC content and codon usage bias indicated higher substitution rates and AT excess at the third codon positions, and we found evidence of positive selection in 3% of amino acid sites. There were no significant differences among species in site biochemical profiles. We argue that the selection of appropriate partition strategies and evolutionary models is important to increase accuracy in phylogenetic relationships, even when using plastome datasets, which is still the primarily used genome in plant phylogenetics.

Large format picture books form a major part of school and public library reference collections and are often a child’s first introduction to science (Rawson & McCool, 2014). These books are designed to be visually stimulating as well as informative and brightly coloured images of molecular models are an important device in illustrating subjects such as genetics and medicine for young children. Both textual and visual discourse articulate and embed social and cultural practices and this study explores the messages about science that are conveyed in children’s science books when using images of molecular models. A number of themes occur in the way these images are utilised to illustrate science. Firstly, pictures of molecular models are used to illustrate the discovery narrative of science (see Latour & Woolgar, 1986), exemplified by the frequent use of the photograph of Watson and Crick next to their model of the double helix. Secondly, the ‘constructivist’ narrative of science, where mankind can build solutions to problems (see Meinel, 2004), is often illustrated by the use of pictures of brightly coloured plastic models of small ‘useful’ molecules such as paracetamol. Interestingly, images of molecular models, often computer generated, are also used to illustrate challenges to science, such as unethical practices or commodification. In science picture books for young children, images of molecular models are rarely used to explain scientific concepts but are more often utilised to illustrate social and cultural messages about science.

Molecular visualization software is an upcoming and growing sector than it's tracing its path thanks to the incoming technologies. Not only allows to compute time-consuming CPU and GPU programs and renders in lesser time, but also introduces more complex and complete visualization methods that allow

Describes two activities to analyze unit-cell structures from a geometric viewpoint and invites students to apply their mathematical understanding to scientific phenomena. Students form models of the simple cube, a building block of crystalline structures, and a methane molecule. (MKR)

The work contained within this dissertation describes the synthesis and study of a series of molecular complexes that mimic the connectivity and geometry of active sites in heterogeneous catalysis. The molecular complexes serve two purposes; they serve as models of surface sites for spectroscopic comparison to metals sites on silica described in the literature and as thermolytic molecular precursors to new materials and isolated metal centers on silica supports by the thermolytic molecular precursor (TMP) method. This dissertation focuses on the development of molecular precursors and model complexes based on group 13 and 14 elements which can be used to model or generate metal sites with relevance to catalysis. In chapter one, methods are explored and developed to control the nuclearity of gallium metal centers in silica materials formed by direct decomposition of molecular precursors which influences their acidity and coordination environments. In chapter two, novel molecular models of catalytic sites reported in the literature are synthesized, characterized and used to investigate proposed initiation steps in heterogeneous catalysis. Finally, chapter three explores new molecular precursors for doping metal centers on silica with germanium, which has previously been shown to influence catalysis.Chapter one describes the use of a new molecular precursor, Ga[OSi(OtBu)3]3•THF, to generate gallium-containing silica materials by direct thermolysis of the molecular precursor dissolved in a hydrocarbon solvent. The use of a block co-polymer, P-123 Pluronic, as templating agent during thermolysis was investigated in conjunction with the inclusion of tris(tert-butoxy)silanol as a co-thermolysis partner. Co-thermolysis was pursued to tune the Ga:Si ratio of the resulting materials, denoted UCB1-GaSiX. When P-123 pluronic was added as a template in the direct thermolysis of Ga[OSi(OtBu)3]3•THF, the resulting material displayed uniform vermicular pores. However, a decrease in

Electrospraying of nanodroplets modifies the target surface by sputtering its atoms and amorphizing the outer layers. Collisions of droplets of diameters between 1 nm and 1 m are not yet totally understood. Previous experimental studies have been performed and compared with Molecular Dynamics simulations using the pseudo-atom model (PA) for the droplets. This PA model considers each liquid molecule as a single particle with the same molecular mass. In this study a new atomic/molecule model (AM) which contemplates all atoms and bonds of the droplet is presented. Simulations of the collision of a 5 nm diameter droplet for formamide and EMIMIm are performed using both models. Droplet initial kinetic energies between 3 KeV and 15 KeV are tested. Results show that PA model collisions transfer almost all of their initial kinetic energy to the slab. Temperature fields after the collision are much higher for this model. Furthermore, highly energetic EMIMIm droplets pseudo-atoms penetrate further into the target whereas atoms in the AM model do not. The ratio of Silicon atoms sputtered and the amorphized region depth for the PA models are larger than for the atomic/molecule, which compare well to experimental results. The AM model is more accurate, especially when simulating droplets of high atomic mass for which the PA model particles are very energetic. Energy consumed to break bonds in the AM model is negligible in comparison with the droplet initial kinetic energy. Simulations show that unbreakable bonds can be used in the AM models obtaining almost identical results.

The Triple Negative Breast Cancer (TNBC) is considered as the most difficult Breast Cancer type to be medicated. So far, the acceptable medication and diagnostics measure are based upon the proteomics-world view, especially with utilizing the natural products and their derivatives as drug candidates. However, as the transcriptomics-studies are getting more advanced, the research on non-coding (nc)RNA is being considered as a more feasible approach to deal with the TNBC. The miR-31, as the ncRNA that play part in the molecular mechanism of the TNBC, is extensively studied mainly for providing the biomarker and drug candidate. However, the molecular interaction and structures are mainly unknown and it is subject of investigation of this research. This research is utilizing the interplay of pipelines that consist of the Vienna RNA package for 2D RNA structure prediction and the simRNA/modeRNA packages for 3D RNA structure prediction. The result is a blueprint that satifactionary illustrates the RNA structure in a fine-grained manner. The miR-31 and its respective silencing (si)RNA are indeed could be useful to be biomarkers and blue-print for the drug design of the TNBC.

CarbBuilder is a portable software tool for producing three-dimensional molecular models of carbohydrates from the simple text specification of a primary structure. CarbBuilder can generate a wide variety of carbohydrate structures, ranging from monosaccharides to large, branched polysaccharides. Version 2.0 of the software, described in this article, supports monosaccharides of both mammalian and bacterial origin and a range of substituents for derivatization of individual sugar residues. This improved version has a sophisticated building algorithm to explore the range of possible conformations for a specified carbohydrate molecule. Illustrative examples of models of complex polysaccharides produced by CarbBuilder demonstrate the capabilities of the software. CarbBuilder is freely available under the Artistic License 2.0.

Molekulski modeli neizostavni su dio nastave kemije. Komercijalno dostupni modeli, osim što su skupi, uglavnom su premaleni za upotrebu u demonstracijske svrhe. U radu je dan prijedlog izrade kalotnih molekulskih modela od stiropora s magnetnim kuglicama u ulozi spojnica te pločastim magnetima za prikazivanje polarnosti molekula., Molecular models are indispensable tools in teaching chemistry. Beside their high price, commercially available models are generally too small for classroom demonstration. This paper suggests how to make space-filling (callote) models from Styrofoam with magnetic balls as connectors and disc magnets for showing molecular polarity.

The molecular models developed recently for use in the Monte Carlo simulation of rarefied real gases are briefly discussed., 希薄実在気体のモンテカルロシミュレーションに用いるため最近開発した分子モデルの概略を述べた。

Computational molecular modeling and visualization has seen significant progress in recent years with sev- eral molecular modeling and visualization software systems in use today. Nevertheless the molecular biology community lacks techniques and tools for the rigorous analysis, quantification and visualization of the associated errors in molecular structure and its associated properties. This paper attempts at filling this vacuum with the introduction of a systematic statistical framework where each source of structural uncertainty is modeled as a ran- dom variable (RV) with a known distribution, and properties of the molecules are defined as dependent RVs. The framework consists of a theoretical basis, and an empirical implementation where the uncertainty quantification (UQ) analysis is achieved by using Chernoff-like bounds. The framework enables additionally the propagation of input structural data uncertainties, which in the molecular protein world are described as B-factors, saved with almost all X-ray models deposited in the Protein Data Bank (PDB). Our statistical framework is also able and has been applied to quantify and visualize the uncertainties in molecular properties, namely solvation interfaces and solvation free energy estimates. For each of these quantities of interest (QOI) of the molecular models we provide several novel and intuitive visualizations of the input, intermediate, and final propagated uncertainties. These methods should enable the end user achieve a more quantitative and visual evaluation of various molecular PDB models for structural and property correctness, or the lack thereof., Comment: 19 figures, 30 pages

The effect of molecular models on viscosity and thermal conductivity calculations is investigated. The Direct Simulation Monte Carlo (DSMC) method for rarefied gas flows is used to simulateCouette and Fourier flows as a means of obtaining the transport coefficients. Experimentalmeasurements for argon (Ar) provide a baseline for comparison over a wide temperature range of 100–1,500 K. The variable hard sphere (VHS), variable soft sphere (VSS), and Lennard-Jones (L-J) molecular models have been implemented into a parallel version of Bird’s one-dimensional DSMC code, DSMC1, and the model parameters have been recalibrated to the current experimental data set. While the VHS and VSS models only consider the short-range, repulsive forces, the L-J model also includes constributions from the long-range, dispersion forces. Theoretical results for viscosity and thermal conductivity indicate the L-J model is more accurate than the VSS model; with maximum errors of 1.4% and 3.0% in the range 300–1,500 K for L-J and VSS models, respectively. The range of validity of the VSS model is extended to 1,650 K through appropriate choices for the model parameters.

Although nonflexible, scaled molecular models like Pauling-Corey's and its descendants have made significant contributions in structural biology research and pedagogy, recent technical advances in 3D printing and electronics make it possible to go one step further in designing physical models of biomacromolecules: to make them conformationally dynamic. We report the design, construction, and validation of a flexible, scaled, physical model of the polypeptide chain, which accurately reproduces the bond rotational degrees-of-freedom in the peptide backbone. The coarse-grained backbone model consists of repeating amide and alpha-carbon units, connected by mechanical bonds (corresponding to phi and psi angles) that include realistic barriers to rotation that closely approximate those found at the molecular scale. Longer-range hydrogen-bonding interactions are also incorporated, allowing the chain to easily fold into stable secondary structures. This physical model can serve as the basis for linking tangible bio-macromolecular models directly to the vast array of existing computational tools to provide an enhanced and interactive human-computer interface. We have explored the boundaries of this direction at the interface of computational tools and physical models of biological macromolecules at the nano-scale. Using a CAD-biocomputational framework, we have provided a methodology to design and build physical protein models focusing on shape and dynamics. We have also developed a workflow and an interface implemented for such bio-modeling tools. This physical-digital interface paradigm, at the intersection of native state proteins (P), computational models (C) and physical models (P), provides new opportunities for building an interactive computational modeling tool for protein folding and drug design. Furthermore, this model is easily constructed with readily obtainable parts and promises to be a tremendous educational aid to the intuitive understanding of chain folding

Molecular models for capturing the behavior of various entangled polymer systems were developed based on the physical framework of the tube model by Doi and Edwards and stochastic slip-link simulations built on similar ingredients. Using such models, we confronted three problems involving entangled polymers in this work. First, we investigated the effects of entanglements on the elastic behavior of polymer networks using slip-link simulations. In particular, we simulated randomly-crosslinked networks using the Primitive Chain Network model of Masubuchi and co-workers. We observed that the obtained stress-strain behavior for these networks from simulations compared reasonably with the replica theory of Edwards and Vilgis, which is consistent with experiments. This contrasts with previous findings in end-linked networks where applica- tion of the model was less successful. We explored possible mechanisms to eliminate the discrepancies on predictions for the latter. However, none of these mechanisms seem physically reasonable in the context of the present model. We also confronted the issue of thermodynamic inconsistency of the model by considering an alternative sliding equation based on the chemical potential. This new sliding equation gave a slightly different stress-strain response for randomly-crosslinked networks but the difference was minimal in contrast with the huge discrepancy observed in end-linked systems. Second, we modeled data on parallel superposition flows of monodisperse and nearly monodisperse solutions from the experiments of Wang and co-workers using a simple tube-based constitutive equation with convective constraint release (CCR). By doing a linear expansion on this equation, we obtained analytic expressions for superposition spectra as a function of shear rate and the CCR parameter β. We then compared predictions based on these expressions with the experimental data. Model agreement was quite satisfactory and was independent of the choice of β. Predi

Systematic construction of coarse-grained molecular models from detailed atomistic simulations, and even from ab initio simulations is discussed. Atomistic simulations are first performed to extract structural information about the system, which is then used to determine effective potentials for a coarse-grained model of the same system. The statistical-mechanical equations expressing the canonical properties in terms of potential parameters can be inverted and solved numerically according to the iterative Newton scheme. In our previous applications, known as the Inverse Monte Carlo, radial distribution functions were inverted to reconstruct pair potential, while in a more general approach the targets can be other canonical averages. We have considered several examples of coarse-graining; for the united atom water model we suggest an easy way to overcome the known problem of high pressure. Further, we have developed coarse-grained models for L- and D-prolines, dissolved here in an organic solvent (dimethylsulfoxide), keeping their enantiomeric properties from the corresponding all-atom proline model. Finally, we have revisited the previously developed coarse-grained lipid model based on an updated all-atomic force field. We use this model in large-scale meso-scale simulations demonstrating spontaneous formation of different structures, such as vesicles, micelles, and multi-lamellar structures, depending on thermodynamical conditions.

Hepatitis B virus (HBV) infects more than 2 billion people alive today and is responsible for over 1 million deaths caused by acute and chronic hepatitis and hepatocellular carcinoma every year. Although remarkable progress has been made in recent years, effective treatment and eradication of chronic HBV infection remain as a tremendous therapeutic challenge. Experimental investigations over the past few decades have resulted in vast information on the characteristic features of viral replication and molecular structures of the HBV genome as well as the viral proteins. However, it has long been recognized that the viral replication process is regulated by a balanced interaction of the geometric material and its viral proteins in a crowded cellular milieu that is hardly detectable with direct experimental means. Molecular modeling that provides a quantitative analysis of important intermolecular interactions will be invaluable for unraveling the microscopic details of physicochemical processes underlying viral formation and replication cycle and for designing new experiments to develop novel anti-viral strategies. This Ph.D. research focuses on molecular modeling of capsid formation, genome packaging and maturation of HBV particles under various mutagenesis and physiological conditions. A suite of theoretical models has been developed to facilitate better understanding of the fundamentals of viral replication cycles and enabling new therapeutic breakthrough for future treatment of HBV infection. Specifically, I applied an effective coarse-grained model for the key viral components and quantified the stability of nucleocapsids based on statistical mechanics. The theoretical model yields faithful results for describing the effects of temperature and ion concentration on viral particle formation, supporting an experimentally derived hypothesis that suggests the balanced electrostatic interaction for the capsid stability and genome encapsidation. In addition, I analyzed

Molecular Dynamics simulations are of key importance in the drug design field. Among all possible representations commonly used to inspect these simulations, Ribbons has the advantage of giving the expert a good overview of the conformation of the molecule. Although several techniques have been previously proposed to render ribbons, all of them have limitations in terms of space or calculation time, making them not suitable for real-time interaction with simulation software. In this paper we present a novel adaptive method that generates ribbons in real-time, taking advantage of the tessellation shader. The result is a fast method that requires no precomputation, and that generates high quality shapes and shading., This work has been supported by the projects TIN2013-47137-C2-1-P and TIN2014-52211-C2-1-R of the Spanish Ministerio de Economía y Competitividad, and the project 2014-SGR 146 from the Catalan Government., Postprint (author's final draft)

Formal verification of molecular programs is a first step towards their automatic processing by means of reasoning systems (ACL2, PVS, etc). In this paper a systematic method to establish verifications of these programs within molecular models with memory, that is, molecular computing models where some operations modifying the inner structure of molecules exist, is proposed. The method presented in this work is applied to relevant problems for the design of molecular programs solving some well-known numerical NP-complete problems: the Generating Cover Families problem, the Set Covering problem and the Minimal Set Cover Selection Problem.

This paper describes an efficient approach to (self) collision detection and distance computations for complex articulated mechanisms such as molecular chains. The proposed algorithm called BioCD is particularly designed for sampling-based motion planning on molecular models described by long kinematic chains possibly including cycles. The algorithm con s that the kinematic chain is structured into a number of rigid groups articulated by preselected degrees of freedom. This structuration is exploited by a two-level spatially-adapted hierarchy. The proposed algorithm is not limited to particular kinematic topologies and allows good collision detection times. BioCD is also tailored to deal with the particularities imposed by the molecular context on collision detection. Experimental results show the effectiveness of the proposed approach which is able to process thousands of (self) collision tests per second on flexible protein models with up to hundreds of degrees of freedom.

info:eu-repo/semantics/published

In order to increase the efficiency of the computer simulation of biological molecules, it is very common to impose holonomic constraints on the fastest degrees of freedom; normally bond lengths, but also possibly bond angles. Since the maximum time step required for the stability of the dynamics is proportional to the shortest period associated with the motions of the system, constraining the fastest vibrations allows to increase it and, assuming that the added numerical cost is not too high, also increase the overall efficiency of the simulation. However, as any other element that affects the physical model, the imposition of constraints must be assessed from the point of view of accuracy: both the dynamics and the equilibrium statistical mechanics are model-dependent, and they will be changed if constraints are used. In this review, we investigate the accuracy of constrained models at the level of the equilibrium statistical mechanics distributions produced by the different dynamics. We carefully derive the canonical equilibrium distributions of both the constrained and unconstrained dynamics, comparing the two of them by means of a “stiff” approximation to the latter. We do so both in the case of flexible and hard constraints, i.e., when the value of the constrained coordinates depends on the conformation and when it is a constant number. We obtain the different correcting terms associated with the kinetic energy mass-metric tensor determinants, but also with the details of the potential energy in the vicinity of the constrained subspace (encoded in its first and second derivatives). This allows us to directly compare, at the conformational level, how the imposition of constraints changes the thermal equilibrium of molecular systems with respect to the unconstrained case. We also provide an extensive review of the relevant literature, and we show that all models previously reported can be considered special cases of the most general treatments presented in this

The direct simulation Monte Carlo (DSMC) method is applied here to model the electron‐beam (e‐beam) physical vapor deposition of copper thin films. A suitable molecular model for copper‐copper interactions have been determined based on comparisons with experiments for a 2D slit source. The model for atomic copper vapor is then used in axi‐symmetric DSMC simulations for analysis of a typical e‐beam metal deposition system with a cup crucible. The dimensional and non‐dimensional mass fluxes obtained are compared for two different deposition configurations with non‐uniformity as high as 40% predicted from the simulations.

BACKGROUND: P-glycoprotein belongs to the family of ATP-binding cassette proteins which hydrolyze ATP to catalyse the translocation of their substrates through membranes. This protein extrudes a large range of components out of cells, especially therapeutic agents causing a phenomenon known as multidrug resistance. Because of its clinical interest, its activity and transport function have been largely characterized by various biochemical studies. In the absence of a high-resolution structure of P-glycoprotein, homology modeling is a useful tool to help interpretation of experimental data and potentially guide experimental studies. RESULTS: We present here three-dimensional models of two different catalytic states of P-glycoprotein that were developed based on the crystal structures of two bacterial multidrug transporters. Our models are supported by a large body of biochemical data. Measured inter-residue distances correlate well with distances derived from cross-linking data. The nucleotide-free model features a large cavity detected in the protein core into which ligands of different size were successfully docked. The locations of docked ligands compare favorably with those suggested by drug binding site mutants. CONCLUSION: Our models can interpret the effects of several mutants in the nucleotide-binding domains (NBDs), within the transmembrane domains (TMDs) or at the NBD:TMD interface. The docking results suggest that the protein has multiple binding sites in agreement with experimental evidence. The nucleotide-bound models are exploited to propose different pathways of signal transmission upon ATP binding/hydrolysis which could lead to the elaboration of conformational changes needed for substrate translocation. We identified a cluster of aromatic residues located at the interface between the NBD and the TMD in opposite halves of the molecule which may contribute to this signal transmission. Our models may characterize different steps in the catalytic cycle an