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Description
Spider dragline silk is well known for its outstanding mechanical properties - a combination of strength and extensibility that makes it one of the toughest materials known. Two proteins, major ampullate spidroin 1 (MaSp1) and 2 (MaSp2), comprise dragline silk fibers. There has been considerable focus placed on understanding the source of spider silk's unique mechanical properties by investigating the protein composition, molecular structure and dynamics. Chemical compositional heterogeneity of spider silk fiber is critical to understand as it provides important information for the interactions between MaSp1 and MaSp2. Here, the amino acid composition of dragline silk protein was precisely determined using a solution-state nuclear magnetic resonance (NMR) approach on hydrolyzed silk fibers. In a similar fashion, solution-state NMR was applied to probe the "13"C/"15"N incorporation in silk, which is essential to understand for designing particular solid-state NMR methods for silk structural characterization. Solid-state NMR was used to elucidate silk protein molecular dynamics and the supercontraction mechanism. A "2"H-"13"C heteronuclear correlation (HETCOR) solid-state NMR technique was developed to extract site-specific "2"H quadrupole patterns and spin-lattice relaxation rates for understanding backbone and side-chain dynamics. Using this technique, molecular dynamics were determined for a number of repetitive motifs in silk proteins - Ala residing nanocrystalline &beta-sheet; domains, 3"1"-helical regions, and, Gly-Pro-Gly-XX &beta-turn; motifs. The protein backbone and side-chain dynamics of silk fibers in both dry and wet states reveal the impact of water on motifs with different secondary structures. Spider venom is comprised of a diverse range of molecules including salts, small organics, acylpolyamines, peptides and proteins. Neurotoxins are an important family of peptides in spider venom and have been shown to target and modulate various ion channels. The neurotoxins are Cys-rich and share an inhibitor Cys knot (ICK) fold. Here, the molecular structure of one G. rosea tarantula neurotoxin, GsAF2, was determined by solution-state NMR. In addition, the interaction between neurotoxins and model lipid bilayers was probed with solid-state NMR and negative-staining (NS) transmission electron microscopy (TEM). It is shown that the neurotoxins influence lipid bilayer assembly and morphology with the formation of nanodiscs, worm-like micelles and small vesicles.
ContributorsShi, Xiangyan (Author) / Yarger, Jeffery L (Thesis advisor) / Holland, Gregory P (Thesis advisor) / Levitus, Marcia (Committee member) / Marzke, Robert F (Committee member) / Arizona State University (Publisher)
Created2014
Description
Nucleic acid nanotechnology is a field of nanoscale engineering where the sequences of deoxyribonucleicacid (DNA) and ribonucleic acid (RNA) molecules are carefully designed to create self–assembled nanostructures with higher spatial resolution than is available to top–down fabrication methods. In the 40 year history
of the field, the structures created have scaled from small tile–like structures constructed from a few hundred
individual nucleotides to micron–scale structures assembled from millions of nucleotides using the technique
of “DNA origami”. One of the key drivers of advancement in any modern engineering field is the parallel
development of software which facilitates the design of components and performs in silico simulation of the
target structure to determine its structural properties, dynamic behavior, and identify defects. For nucleic acid
nanotechnology, the design software CaDNAno and simulation software oxDNA are the most popular choices
for design and simulation, respectively. In this dissertation I will present my work on the oxDNA software
ecosystem, including an analysis toolkit, a web–based graphical interface, and a new molecular visualization
tool which doubles as a free–form design editor that covers some of the weaknesses of CaDNAno’s lattice–based design paradigm. Finally, as a demonstration of the utility of these new tools I show oxDNA simulation
and subsequent analysis of a nanoscale leaf–spring engine capable of converting chemical energy into dynamic motion. OxDNA simulations were used to investigate the effects of design choices on the behavior of
the system and rationalize experimental results.
ContributorsPoppleton, Erik (Author) / Sulc, Petr (Thesis advisor) / Yan, Hao (Committee member) / Forrest, Stephanie (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2022
Description
Biopolymers perform the majority of essential functions necessary for life. From a small amount of components emerges considerable complexity in both structure and function. The separated timescales of dynamic processes and intricate intra- and inter-molecular interactions of these molecules necessitate the development and utilization of computational approaches for biopolymer study and nanotechnology applications. Biopolymer nanotechnology exploits the natural chemistry of biopolymers to perform novel functions at the nanoscale. Molecular dynamics is the numerical simulation of chemical entities according to the physical laws of motion and statistical mechanics. The number of atoms in biopolymers require coarse-grained methods to fully sample the dynamics of the system with reasonable resources. Accordingly, a coarse-grained molecular dynamics model for the characterization of hybrid nucleic acid-protein nanotechnology was developed. Proteins are represented as an anisotropic network model (ANM) which show good agreement with experimentally derived protein dynamics for a small computational cost. The model was subsequently applied to hybrid DNA-protein cages systems and exhibited excellent agreement with experimental results. Ongoing development efforts look to apply network models to oxDNA origami to create multiscale models for DNA origami. The network approximation will allow for detailed simulation of DNA origami association, of concern to DNA crystal and lattice formation. Identification and design of target-specific binders (aptamers) has received considerable attention on account of their diagnostic and therapeutic potential. Generated in selection cycles from extensive random libraries, biopolymer aptamers are of particular interest due to their potential non-immunogenic properties. Machine learning leverages the use of powerful statistical principles to train a model to transform an input into a desired output. Parameters of the model are iteratively adjusted according to the gradient of the cost function. An unsupervised and generative machine learning model was applied to Thrombin aptamer sequence data. From the model, sequence characteristics necessary for binding were identified and new aptamers capable of binding Thrombin were sampled and verified experimentally. Future work on the development and utilization of an unsupervised and interpretable machine learning model for unaligned sequence data is also discussed.
ContributorsProcyk, Jonah (Author) / Sulc, Petr (Thesis advisor) / Stephanopoulos, Nicholas (Thesis advisor) / Hariadi, Rizal (Committee member) / Heyden, Matthias (Committee member) / Arizona State University (Publisher)
Created2022