No description available.

No / Chemical means of manipulating the distribution of lipids across biological membranes is of considerable interest for many biomedical applications as a characteristic lipid distribution is vital for numerous cellular functions. Here we employ atomic-scale molecular simulations to shed light on the ability of certain amphiphilic compounds to promote lipid translocation (flip-flops) across membranes. We show that chemically induced lipid flip-flops are most likely pore-mediated: the actual flip-flop event is a very fast process (time scales of tens of nanoseconds) once a transient water defect has been induced by the amphiphilic chemical (dimethylsulfoxide in this instance). Our findings are consistent with available experimental observations and further emphasize the importance of transient membrane defects for chemical control of lipid distribution across cell membranes

Yes / A major barrier to the systematic improvement of biomimetic peptide-mediated strategies for the controlled growth of inorganic nanomaterials in environmentally benign conditions lies in the lack of clear conceptual connections between the sequence of the peptide and its surface binding affinity, with binding being facilitated by noncovalent interactions. Peptide conformation, both in the adsorbed and in the nonadsorbed state, is the key relationship that connects peptide-materials binding with peptide sequence. Here, we combine experimental peptide–titania binding characterization with state-of-the-art conformational sampling via molecular simulations to elucidate these structure/binding relationships for two very different titania-binding peptide sequences. The two sequences (Ti-1, QPYLFATDSLIK; Ti-2, GHTHYHAVRTQT) differ in their overall hydropathy, yet via quartz-crystal microbalance measurements and predictions from molecular simulations, we show these sequences both support very similar, strong titania-binding affinities. Our molecular simulations reveal that the two sequences exhibit profoundly different modes of surface binding, with Ti-1 acting as an entropically driven binder while Ti-2 behaves as an enthalpically driven binder. The integrated approach presented here provides a rational basis for peptide sequence engineering to achieve the in situ growth and organization of titania nanostructures in aqueous media and for the design of sequences suitable for a range of technological applications that involve the interface between titania and biomolecules. / AFOSR grant FA9550-12-1-0226; AFOSR for funding via FA9550-13-1-0040

Molecular dynamics simulations were carried out on traditional polymers copolymerized with POSS (Polyhedral Oligomeric Silsesquioxanes) derivatives to identify the reason behind improved properties imparted to the conventional polymers with the chemical incorporation of POSS. Two classes of systems are used in the present study, namely the polystyrene and polymethyl methacrylate systems. Seven systems are studied in the polystyrene class. The effect of corner substituent groups of the POSS cage on the properties of the polymer nanocomposites was studied using the polystyrene. In addition, the effect of the type of cage structure on the properties was studied using T8, T10 and T12 POSS cage structures containing phenyl substituents on each POSS cage. Systems with polymethyl methacrylate were studied to analyze the effect of mole percent of POSS on the polymer properties, holding the corner substituents on the POSS unit constant. The corner function used was the isobutyl group. The properties analyzed using simulations include glass transition temperature, volumetric thermal expansion coefficient, X-ray scattering data, solubility parameter and mechanical properties. In both polystyrene and polymethyl methacrylate systems, simulations were also carried out on the pure parent polymers for the sake of comparison. The effect of forcefield on the predicted properties was studied using both COMPASS and PCFF forcefields. Performance analysis of the code used in the present simulation was done by analyzing the parallel run time of simulations involving pure atactic polystyrene.

No description available.

No description available.

Islet amyloid polypeptide (IAPP) is a 37-residue amyloidogenic hormone implicated in the progression of Type II Diabetes (T2D). T2D affects an estimated 422 million people yearly and is a co-morbidity with numerous diseases. IAPP forms toxic oligomers and amyloid fibrils that reduce pancreatic β-cell mass and exacerbate the T2D disease state. Toxic oligomer formation is attributed, in part, to the formation of inter-peptide β-strands comprised of residues 23-27 (FGAIL). Flavonoids, a class of polyphenolic natural products, have been found experimentally to inhibit IAPP aggregate formation. Many of these known IAPP aggregation attenuating small flavonoids differ structurally only slightly; the influence of functional group placement on inhibiting the aggregation of the IAPP(20-29) has yet to be explored. To probe the role of small-molecule structural features that impede IAPP aggregation, molecular dynamics (MD) simulations were performed on a model fragment of IAPP(20-29) in the presence of morin, quercetin, dihydroquercetin, epicatechin, and myricetin. Contacts between Phe23 residues are critical to oligomer formation, and small-molecule contacts with Phe23 are a key predictor of β-strand reduction. Structural properties influencing the ability of compounds to disrupt Phe23-Phe23 contacts include carbonyl and hydroxyl group placement. These structural features influence aromaticity and hydrophobicity, principally affecting ability to disrupt IAPP(20-29) oligomer formation. This work provides key information on design considerations for T2D therapeutics. / Master of Science in Life Sciences / Type II Diabetes (T2D) affects an estimated 422 million people worldwide, with the World Health Organization (WHO) reporting that approximately 1.5 million deaths were directly caused by T2D in 2019. The progression of T2D has been attributed to a protein, called islet amyloid polypeptide (IAPP, or amylin) that is co-secreted with insulin after individuals eat or consumes calories. IAPP has been discovered to form toxic aggregates or clumps of protein material that worsen the disease state and cause a loss of mass of pancreatic cells. There is a large market for therapeutics of T2D and more small molecule drugs are needed to slow progression and severity of T2D. Flavonoids, a class of natural molecules, have been found to inhibit the processes by which IAPP promotes T2D disease progression by stopping the aggregation of IAPP. The structures of these flavonoid compounds differ slightly but show difference in ability to slow IAPP aggregation. By understanding how those differences confer more or less protection against T2D and inhibit IAPP aggregation, we can design more potent and specific drugs to target IAPP. To probe the role of molecular structure in preventing IAPP aggregation, molecular dynamics (MD) simulations — a powerful computational technique — were performed on a model fragment of IAPP in the presence of molecules morin, quercetin, dihydroquercetin, epicatechin, and myricetin. MD simulations provide extremely detailed information about potential drug interactions with a given target, serving as an important tool in the development of new drugs. This work has identified key features and predictors of effective IAPP drugs, providing a framework for the further development of therapeutics against T2D and similar diseases.

Coarse-grained (CG) molecular dynamics (MD) simulations have seen a wide range of applications from biomolecules, polymers to graphene and metals. In CG MD simulations, atomistic groups are represented by beads, which reduces the degrees of freedom in the systems and allows larger timesteps. Thus, large time and length scales could be achieved in CG MD simulations with inexpensive computational cost. The representative example of large time- and length-scale phenomena is the conformation transitions of single polymer chains as well as polymer chains in their architectures, self-assembly of biomaterials, etc. Polymers exist in many aspects of our life, for example, plastic packages, automobile parts, and even medical devices. However, the large chemical and structural diversity of polymers poses a challenge to the existing CG MD models due to their limited accuracy and transferabilities. In this regard, this dissertation has developed CG models of polymers on the basis of accurate and transferable hydrocarbon models, which are important components of the polymer backbone. CG hydrocarbon models were created with 2:1 and 3:1 mapping schemes and their force-field (FF) parameters were optimized by using particle swarm optimization (PSO). The newly developed CG hydrocarbon models could reproduce their experimental properties including density, enthalpy of vaporization, surface tension and self-diffusion coefficients very well. The cross interaction parameters between CG hydrocarbon and water models were also optimized by the PSO to repeat the experimental properties of Gibbs free energies and interfacial tensions. With the hydrocarbon models as the backbone, poly(acrylic acid) (PAA) and polystyrene (PS) models were constructed. Their side chains were represented by one COOH (carboxylic acid) and three BZ beads, respectively. Before testing the PAA and PS models, their monomer models, propionic acid and ethylbenzene, were created and validated, to confirm that the cross interactions between hydrocarbon and COOH beads, and between hydrocarbon and BZ beads could be accurately predicted by the Lorentz-Berthelot (LB) combining rules. Then the experimental properties, density of polymers at 300 K and glass transition temperatures, and the conformations of their all-atom models in solvent mixtures of water and dimethylformamide (DMF) were reproduced by the CG models. The CG PAA and PS models were further used to build the bottlebrush copolymers of PAA-PS and to predict the structures of PAA-PA in different compositions of binary solvents water/DMF. Although CG models are useful in understanding the phenomena at large time- or length- scales, atomistic information is lost. Backmapping is usually involved in reconstructing atomistic models from their CG models. Here, four machine learning (ML) algorithms, artificial neural networks (ANN), k-nearest neighbor (kNN), gaussian process regression (GPR), and random forest (RF) were developed to improve the accuracy of the backmapped all-atom structures. These optimized four ML models showed R2 scores of more than 0.99 when testing the backmapping against four representative molecules: furan, benzene, naphthalene, graphene. / Doctor of Philosophy / Polymers have a wide range of applications from packaging, foams, coating to pipes, tanks and even medical devices and biosensors. To improve the properties of these materials it is important to understand their structure and features responsible for controlling their properties at the molecular-level. Molecular dynamic (MD) simulations are a powerful tool to study their structures and properties at microscopic level. However, studying the molecular-level conformations of polymers and their architectures usually requires large time- or length-scales, which is challenging for the all-atom MD simulations because of the high computational cost. Coarse-grained (CG) MD simulations can be used to study these soft-materials as they represent atomistic groups with beads, enabling the reduction of the system sizes drastically, and allowing the use of large timesteps in MD simulations. In MD simulations, force-fields (FF) that describe the intramolecular and intermolecular interactions determine the performance of simulations. Here, we firstly optimized the FF parameters for hydrocarbons. With the optimized CG hydrocarbon models, two representative CG polymer models, poly(acrylic acid) (PAA) and polystyrene (PS) were built by using hydrocarbons as the backbones of polymers. Furthermore, the PAA and PS chains were grafted on a linear hydrocarbon backbone to form a bottlebrush copolymer. Although CG MD models are useful in studying the complex process of polymers, the atomic detailed information is lost. To reconstruct accurate atomistic structures, backmapping by using machine learning (ML) algorithms was performed. The performance of the ML models was better than that of the existing backmapping packages built in Visual Molecular Dynamics (VMD).

Ag thin and thick films have been experimentally deposited using a technique called Laser Ablation of a Microparticle Aerosol (LAMA). This technique is based on a supersonic jet accelerating NPs of a few nm diameter up to 1000 m/s and operating at room temperature. The deposited films have experimentally demonstrated interesting properties such as dense growth with good adherence on the substrate. Aerosol feed rates have been fixed to 10 mg/h which corresponds to rate depositions of 10¹⁰ to 10¹¹ NPs/s/cm². In order to model this deposition technique and possibly be able to predict the morphology and structure of deposited films using computational methods, we have designed MD programs simulating the depositions of several Ag nanoclusters onto a substrate at a fixed temperature (300 K). The variation of parameters such as cluster size, cluster impact energy, and deposition rate has influenced the morphology and structure of the deposited films. Cluster diameters have been set to 3 nm or 5 nm, cluster velocities set to 200 m/s (0.022 eV/atom), 400 m/s (0.069 eV/ atom), or 800 m/s (0.358 eV/atom), and the deposition rate adjusted to ensure relaxation times between impactions of 5 ps to 20 ps. The evolution of deposited film density, adherence, and crystal arrangement has been analyzed with the variation of the aforementioned parameters. The highest cluster velocities have enabled the deposition of smoother, denser, and more adherent films. NCs with an initial velocity of 200 m/s have shown ratios of flattening equal to 50 % as opposed to 85% flattening for NCs deposited at 800 m/s. These observations have enabled us to draw qualitative conclusions on the film density The deposited films are less porous when the cluster impaction velocity increases. Atomic mixing between substrate and impacted NC atoms increased with increasing deposition velocity, which can perhaps be correlated to an increase of adherence, assuming that more mixing will create stronger molecular binding in the cluster-substrate interaction. Finally, complete epitaxial growth was observed for the highest impaction velocities only, which indicates that recrystalization can occur for this range of impact energies (0.3 eV/atom - 0.5 eV/atom). Although experimental results have given more quantitative data on film density and sticking ratios, they agree with our modeling, and this comparison allows us to validate our MD simulations. However, some limitations have been faced, mainly because of long computing time requirements that a single laptop computer has not been able to support. / text

Red blood cell hemolysis in sickle cell disease (SCD) releases free hemoglobin. Extracellular hemoglobin and its degradation products, free heme and iron, are highly toxic due to oxidative stress induction and decrease in nitric oxide availability. We propose an approach that helps to eliminate extracellular hemoglobin toxicity in SCD by employing a bacterial protein system that evolved to extract heme from extracellular hemoglobin. NEAr heme Transporter (NEAT) domains from iron-regulated surface determinant proteins from Staphylococcus aureus specifically bind free heme as well as facilitate its extraction from hemoglobin. We demonstrate that a purified NEAT domain fused with human haptoglobin beta-chain is able to remove heme from hemoglobin and reduce heme content and peroxidase activity of hemoglobin. We further use molecular dynamics (MD) simulations to resolve molecular pathway of heme transfer from hemoglobin to NEAT, and to elucidate molecular mechanism of such heme transferring process. Our study is the first of its kind, in which simulations are employed to characterize the process of heme leaving hemoglobin and subsequent rebinding with a NEAT domain. Our MD results highlight important amino acid residues that facilitate heme transfer and will guide further studies for the selection of best NEAT candidate to attenuate free hemoglobin toxicity.