Aspects of biomacromolecular dynamics at different scales

Biological functions of biomacromolecules are often indispensably linked to their internal dynamics. To investigate the dynamic nature of biomolecules, molecular dynamics (MD) simulation offers unique advantages by providing high spatial and temporal resolution over orders of magnitude in time- and length scales. Here, simulations at two different scales are used to investigate different aspects of biomolecular dynamics. At the atomistic scale, the first study investigates the relationship between the axial methyl group order parameter and the corresponding entropy in protein side chains. Three classes of methyl group are characterized based on the methyl group’s “topological distance” from the backbone (that is the number of bonds between the methyl group axis and the closest backbone atom) even when direct effects of the topological distance are removed. This distinction implies that methyl groups at the same topological position share similar nonbonded environments. Furthermore, consideration of these classes of methyl group improves the accuracy of entropy-estimates based upon changes in order parameter. The second study investigates the deconstruction of crystalline cellulose, a problem relevant to bioenergy research. The large size of crystalline cellulose together with the associated long-time dynamics exceeds the capabilities of atomistic simulation. Thus, a residue-scale, coarse-grained model of cellulose is calculated using the REACH (Realistic Extension Algorithm via Covariance Hessian) method. The model is successfully validated against experiment using Young’s moduli and the velocity of sound. The coarse-grained analysis of the cellulose fibril suggests that the intrinsic dynamics facilitates deconstruction of the crystalline cellulose fibril from the hydrophobic surface. Both applications share the same concept of approach (that is, computational modeling and simulation at an appropriate scale), which reveals key insights into biomolecules by investigating their dynamic behavior

Monte-Carlo Simulations of Soft Matter Using SIMONA: A Review of Recent Applications

Molecular simulations such as Molecular Dynamics (MD) and Monte Carlo (MC) have gained increasing importance in the explanation of various physicochemical and biochemical phenomena in soft matter and help elucidate processes that often cannot be understood by experimental techniques alone. While there is a large number of computational studies and developments in MD, MC simulations are less widely used, but they offer a powerful alternative approach to explore the potential energy surface of complex systems in a way that is not feasible for atomistic MD, which still remains fundamentally constrained by the femtosecond timestep, limiting investigations of many essential processes. This paper provides a review of the current developments of a MC based code, SIMONA, which is an efficient and versatile tool to perform large-scale conformational sampling of different kinds of (macro)molecules. We provide an overview of the approach, and an application to soft-matter problems, such as protocols for protein and polymer folding, physical vapor deposition of functional organic molecules and complex oligomer modeling. SIMONA offers solutions to different levels of programming expertise (basic, expert and developer level) through the usage of a designed Graphical Interface pre-processor, a convenient coding environment using XML and the development of new algorithms using Python/C++. We believe that the development of versatile codes which can be used in different fields, along with related protocols and data analysis, paves the way for wider use of MC methods

Evaluation of pesticide toxicity: a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides

The thesis aimed to extract information relevant to the hazard and risk assessment of pesticides. In particular, quantitative structure-activity relationship (QSAR) approaches have been used to build up a mathematical model able to predict the aquatic acute toxicity, LC50, and the avian oral toxicity, LD50, for pesticides. Ecotoxicological values were collected from several databases, and screened according to quality criteria. A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Different linear and non-linear regression techniques were used to obtain reliable and thoroughly validated QSARs. The final model was developed by a counter-propagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors. The model was assessed thoroughly in test (R2 = 0.8) and validation sets (R2 = 0.72), the y-scrambling test and a sensitivity/stability test. The second endpoint considered in this thesis was avian oral toxicity. As previously, the chemical description of chemicals was generated automatically by the OpenMolGRID system. The best classification model was chosen on the basis of the performances on a validation set of 19 data points, and was obtained from a support vector machine using 94 data points and nine variables selected by genetic algorithms (Error Ratetraining = 0.021, Error Ratevalidation = 0.158). The model allowed for a mechanistic estimation of the toxicological action. In fact, several descriptors selected for the final classification model encode for the interaction of the pesticides with other molecules. The presence of hetero-atoms, e.g. sulphur atoms, is correlated with the toxicity, and the pool of descriptor selected is generally dependent from the 3D conformation of the structures. These suggest that, in the case of avian oral toxicity, pesticides probably exert their toxic action through the interaction with some macromolecule and/or protein of the biological system

Self-Assembling Peptide Nanomaterials: Molecular Dynamics Studies, Computational Designs And Crystal Structure Characterizations

Peptides present complicated three-dimensional folds encoded in primary amino acid sequences of no more than 50 residues, providing cost-effective routes to the development of self-assembling nanomaterials.� The complexity and subtlety of the molecular interactions in such systems make it interesting to study and to understand the fundamental principles that determine the self-assembly of nanostructures and morphologies in solution. Such principles can then be applied to design novel self-assembling nanomaterials of precisely defined local structures and to controllably engineer new advanced functions into the materials. We first report the rational engineering of complementary hydrophobic interactions to control β-fibril type peptide self-assemblies that form hydrogel networks. Complementary to the experimental observations of the two distinct branching morphologies present in the two β-fibril systems that share a similar sequence pattern, we investigated on network branching, hydrogel properties by molecular dynamics simulations to provide a molecular picture of the assemblies. Next, we present the theory-guided computational design of novel peptides that adopt predetermined local nanostructures and symmetries upon solution assembly. Using such an approach, we discovered a non-natural, single peptide tetra-helical motif that can be used as a common building block for distinct predefined material nanostructures. The crystal structure of one designed peptide assembly demonstrates the atomistic match of the motif structure to the prediction, as well as provides fundamental feedback to the methods used to design and evaluate the computationally designed peptide candidates. This study could potentially improve the success rate of future designs of peptide-based self-assembling nanomaterials

Fabrication and characterization of shape memory polymers at small scales

The objective of this research is to thoroughly investigate the shape memory effect in polymers, characterize, and optimize these polymers for applications in information storage systems. Previous research effort in this field concentrated on shape memory metals for biomedical applications such as stents. Minimal work has been done on shape memory poly- mers; and the available work on shape memory polymers has not characterized the behaviors of this category of polymers fully. Copolymer shape memory materials based on diethylene glycol dimethacrylate (DEGDMA) crosslinker, and tert butyl acrylate (tBA) monomer are designed. The design encompasses a careful control of the backbone chemistry of the materials. Characterization methods such as dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC); and novel nanoscale techniques such as atomic force microscopy (AFM), and nanoindentation are applied to this system of materials. Designed experiments are conducted on the materials to optimize spin coating conditions for thin films. Furthermore, the recovery, a key for the use of these polymeric materials for information storage, is examined in detail with respect to temperature. In sum, the overarching objectives of the proposed research are to: (i) design shape memory polymers based on polyethylene glycol dimethacrylate (PEGDMA) and diethylene glycol dimethacrylate (DEGDMA) crosslinkers, 2-hydroxyethyl methacrylate (HEMA) and tert-butyl acrylate monomer (tBA). (ii) utilize dynamic mechanical analysis (DMA) to comprehend the thermomechanical properties of shape memory polymers based on DEGDMA and tBA. (iii) utilize nanoindentation and atomic force microscopy (AFM) to understand the nanoscale behavior of these SMPs, and explore the strain storage and recovery of the polymers from a deformed state. (iv) study spin coating conditions on thin film quality with designed experiments. (iv) apply neural networks and genetic algorithms to optimize these systems.Ph.D.Committee Chair: Gall, Ken; Committee Chair: May, Gary S; Committee Member: Brand, Oliver; Committee Member: Degertekin, F Levent; Committee Member: Milor, Linda

Towards a Unification of Supercomputing, Molecular Dynamics Simulation and Experimental Neutron and X-ray Scattering Techniques

Molecular dynamics simulation has become an essential tool for scientific discovery and investigation. The ability to evaluate every atomic coordinate for each time instant sets it apart from other methodologies, which can only access experimental observables as an outcome of the atomic coordinates. Here, the utility of molecular dynamics is illustrated by investigating the structure and dynamics of fundamental models of cellulose fibers. For that, a highly parallel code has been developed to compute static and dynamical scattering functions efficiently on modern supercomputing architectures. Using state of the art supercomputing facilities, molecular dynamics code and parallelization strategies, this work also provides insight into the relationship between cellulose crystallinity and cellulose-lignin aggregation by performing multi-million atom simulations. Finally, this work introduces concepts to augment the ability of molecular dynamics to interpret experimental observables with the help of Markov modeling, which allows for a convenient description of complex molecule dynamics as transitions between well defined conformations. The work presented here suggests that molecular dynamics will continue to evolve and integrate with experimental techniques, like neutron and X-ray scattering, and stochastic models, like Markov modeling, to yield unmatched descriptions of molecule dynamics and interpretations of experimental data, facilitated by the growing computational power available to scientists

CARBON-BASED COMPLEX STRUCTURE AND MODEL DEVELOPMENT

Growing concerns about the environment and energy crisis prompt a search for effective carbon-based materials due to their low cost, renewability, sustainability, easy accessibility and excellent properties. We study the model development, structure and properties of graphene oxide, cellulose and their nanocomposites in order to obtain a better fundamental understanding of carbon complex materials and construct a structure-property relationship via reactive molecular dynamics simulations. In chapter 3, the model development of GO is studied. Theoretical GO models developed so far present a good description of its chemical structure. However, when it comes to the structural properties, such as the size and distribution of vacancy defects, the curvature (or roughness), there exist significant gaps between computational models and experimentally synthesized GO materials. We carry out reactive molecular dynamics simulations and use experimental characteristics to fine tune theoretical GO models. Attentions have been paid to the vacancy defects, the distribution and hybridization of carbon atoms, and the overall C/O ratio of GO. The GO models proposed in this work have been significantly improved to represent quantitative structural details of GO materials synthesized via the modified Hummers method. The temperature-programmed protocol and the computational post analyses of Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, vacancy size and curvature distribution, are of general interest to a broad audience working on GO structures from other synthesis methods and other two-dimensional materials and their composites. In Chapter 4, we outline the state-of-the-art understanding of cellulose structures, and discuss in details cellulose interactions, dissolutions and decompositions via computational methods of molecular dynamics (MD) and reactive molecular dynamics (RxMD) simulations. In addition, cellulose characterizations, beneficial to validate and support computational results, are also briefly summarized. Such a state-of-the-art account of atomistic computational studies could inspire interdisciplinary collaborations, optimize process design, promote cellulose-based materials for emerging important applications and shed a light on fundamental understandings of similar systems of biomolecules, polymers and surfactants. In Chapter 5, we investigate the fundamental mechanism of how cellulose structure transforms under pyrolysis conditions and the practical guideline of how cellulose properties are fined tuned accordingly. A series of reactive molecular dynamics calculations has been designed to reveal the structural evolution of crystalline cellulose under pyrolysis treatments. Through the detailed analysis of cellulose configuration change, hydrogen bonding network variation, reaction and redistribution of carbon, oxygen and hydrogen elements, and Young’s modulus, a molecule level insight of crystalline cellulose and its structural evolution under pyrolysis conditions has been constructed via reactive molecular dynamics simulations. We anticipate those theoretical results could effectively promote the design, the manufacture, and the optimization of cellulose based materials for relevant emerging applications. In Chapter 6, we combined the results from previous chapters and explore a new composite material that incorporating amorphous cellulose chains on GO surface, which is barely reported by recent publications. A series of RxMD simulations have been carried out to reveal the mechanical properties of pure GO and cellulose-GO nanocomposites. Two different cellulose-GO composites are constructed, namely, cellulose (monolayer)-GO model and cellulose (multilayer)-GO model. The tensile deformation, Young’s modulus and mechanical strength of GO and cellulose-GO composites have been recorded and calculated under the temperature of 300, 500 800 K, with two strain rates of 10-4/fs and 10-5/fs. We hope the GO model with the simultaneously description to both structural and chemical properties can provide a new fundamental understanding of the mechanical performance of GO and cellulose-GO composites, and could add some advancement to existing knowledge of carbon-based materials

Computational Modeling in Glycoscience

The ubiquitous occurrences of glycans (from oligo to polysaccharides) as cell components to significant constituents of the terrestrial biomass provide the glycans with a panel of biological functions and physicochemical properties. The progress made in algorithms and computational power allows for the simulation of glycans in their natural environment, and new dimensions, both spatial and temporal, can be assessed. The review will illustrate advancements in high-performance computing have allowed molecular simulation methods not only to play a more substantial role in supporting experiments but to transcend such mandate to guide experimental design and to lead autonomously scientific discovery

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used