Proteins in silico-modeling and sampling

Proteins are linear chain molecules made out of amino acids. Only when they fold to their native states, they become functional. This dissertation aims to model the solvent (environment) effect and to develop & implement enhanced sampling methods that enable a reliable study of the protein folding problem in silico. We have developed an enhanced solvation model based on the solution to the Poisson-Boltzmann equation in order to describe the solvent effect. Following the quantum mechanical Polarizable Continuum Model (PCM), we decomposed net solvation free energy into three physical terms– Polarization, Dispersion and Cavitation. All the terms were implemented, analyzed and parametrized individually to obtain a high level of accuracy. In order to describe the thermodynamics of proteins, their conformational space needs to be sampled thoroughly. Simulations of proteins are hampered by slow relaxation due to their rugged free-energy landscape, with the barriers between minima being higher than the thermal energy at physiological temperatures. In order to overcome this problem a number of approaches have been proposed of which replica exchange method (REM) is the most popular. In this dissertation we describe a new variant of canonical replica exchange method in the context of molecular dynamic simulation. The advantage of this new method is the easily tunable high acceptance rate for the replica exchange. We call our method Microcanonical Replica Exchange Molecular Dynamic (MREMD). We have described the theoretical frame work, comment on its actual implementation, and its application to Trp-cage mini-protein in implicit solvent. We have been able to correctly predict the folding thermodynamics of this protein using our approach.

Quantum modeling of bioconjugated nanomaterials

In recent years, the bio-conjugated nanostructured materials have emerged as a new class of materials for the bio-sensing and medical diagnostics applications. In spite of their multi-directional applications, interfacing nanomaterials with bio-molecules has been a challenge due to somewhat limited knowledge about the underlying physics and chemistry behind these interactions and also for the complexity of biomolecules. The main objective of this dissertation is to provide such a detailed knowledge on bioconjugated nanomaterials toward their applications in designing the next generation of sensing devices. Specifically, we investigate the changes in the electronic properties of a boron nitride nanotube (BNNT) due to the adsorption of different bio-molecules, ranging from neutral (DNA/RNA nucleobases) to polar (amino acid molecules). BNNT is a typical member of III-V compounds semiconductors with morphology similar to that of carbon nanotubes (CNTs) but with its own distinct properties. More specifically, the natural affinity of BNNTs toward living cells with no apparent toxicity instigates the applications of BNNTs in drug delivery and cell therapy. Our results predict that the adsorption of DNA/RNA nucleobases on BNNTs amounts to different degrees of modulation in the band gap of BNNTs, which can be exploited for distinguishing these nucleobases from each other. Interestingly, for the polar amino acid molecules, the nature of interaction appeared to vary ranging from Coulombic, van der Waals and covalent depending on the polarity of the individual molecules, each with a different binding strength and amount of charge transfer involved in the interaction. The strong binding of amino acid molecules on the BNNTs explains the observed protein wrapping onto BNNTs without any linkers, unlike carbon nanotubes (CNTs). Additionally, the widely varying binding energies corresponding to different amino acid molecules toward BNNTs indicate to the suitability of BNNTs for the biosensing applications, as compared to the metallic CNTs. The calculated I-V characteristics in these bioconjugated nanotubes predict notable changes in the conductivity of BNNTs due to the physisorption of DNA/RNA nucleobases. This is not the case with metallic CNTs whose transport properties remained unaltered in their conjugated systems with the nucleobases. Collectively, the bioconjugated BNNTs are found to be an excellent system for the next generation sensing devices.

MULTISCALE MODELING OF LIQUID CRYSTALLINE/NANOTUBE COMPOSITES

The objective of this research is to synthesize structural composites designed with particular areas defined with custom modulus, strength and toughness values in order to improve the overall mechanical behavior of the composite. Such composites are defined and referred to as 3D-designer composites. These composites will be formed from liquid crystalline polymers and carbon nanotubes. The fabrication process is a variation of rapid prototyping process, which is a layered, additive-manufacturing approach. Composites formed using this process can be custom designed by apt modeling methods for superior performance in advanced applications. The focus of this research is on enhancement of Young's modulus in order to make the final composite stiffer. Strength and toughness of the final composite with respect to various applications is also discussed. We have taken into consideration the mechanical properties of final composite at different fiber volume content as well as at different orientations and lengths of the fibers. The orientation of the LC monomers is supposed to be carried out using electric or magnetic fields. A computer program is modeled incorporating the Mori-Tanaka modeling scheme to generate the stiffness matrix of the final composite. The final properties are then deduced from the stiffness matrix using composite micromechanics. Eshelby's tensor, required to calculate the stiffness tensor using Mori-Tanaka method, is calculated using a numerical scheme that determines the components of the Eshelby's tensor (Gavazzi and Lagoudas 1990). The numerical integration is solved using Gaussian Quadrature scheme and is worked out using MATLAB as well. . MATLAB provides a good deal of commands and algorithms that can be used efficiently to elaborate the continuum of the formula to its extents. Graphs are plotted using different combinations of results and parameters involved in finding these results