Computational methodologies were employed to study a supramolecular micellar structure and its application, nanoreactor. This task was done through rigorous scale-up procedure using both atomistic and mesoscopic simulations. Primarily, density functional theory (DFT) calculation was used to characterize the smallest unit of complex molecules in the multicomponent mixture system. The following step involved transferring the information achieved by DFT calculation to larger scale simulation, such as molecular dynamics (MD) simulation. Lastly, based on the atomistic simulation results, we performed a series of dissipative particle dynamics (DPD) simulations to study a full body of polymeric multicompartment micelle. In the course of research, we built a systematic procedure to minimize the complexity of computation and efficiently characterize macromolecular structures and its application.

Doctor of Philosophy / Department of Chemistry / Paul E. Smith / In the study of biological systems, molecular dynamics (MD) simulations have played an important role in providing atomic details for phenomena of interest. The force field used in MD simulations is a critical factor determining the quality of the simulations. Recently, Kirkwood-Buff (KB) theory has been applied to study preferential interactions and to develop a new force field. KB theory provides a path from quantities determined from simulation data to the corresponding thermodynamic data. Here we combine KB theory and molecular simulations to study a variety of intermolecular interactions in solution. First, recent results concerning the formulation and evaluation of preferential interactions in biological systems in terms of KB integrals are presented. In particular, experimental and simulated preferential interactions of a cosolvent with a biomolecule in the presence of water are described. Second, a force field for the computer simulation of aqueous solutions of amides is presented. The force field is designed to reproduce the experimentally observed density and KB integrals for N-methylacetamide (NMA), allowing for an accurate description of the NMA activity. Other properties such as the translational diffusion constant and heat of mixing are also well reproduced. The force field is then extended to include N,N'-dimethylacetamide and acetamide with good success. The models presented here provide a basis for an accurate force field for peptides and proteins. Comparison between the developed KB force fields (KBFF) and existing force fields is performed for amide and glycine and proves that the KBFF approach is competitive. Also, explicit expressions are developed for the chemical potential derivatives, partial molar volumes, and isothermal compressibility of solution mixtures involving four components at finite concentrations using the KB theory of solutions. A general recursion relationship is also provided which can be used to generate the chemical potential derivatives for higher component solutions. Finally, a pairwise preferential interaction model (PPIM), described by KB integrals is developed to quantify and characterize the interactions between functional groups observed in peptides.

No description available.

Os nitretos do grupo III (BN, AIN,Gan e InN) e suas ligas ternárias Al-GaN e InGaN proporcionam, recentemente, um extraordinário avanço na fabricação de dispositivos opto-eletrônicos operando na região do espectro correspondente ao verde-azul-UV e na produção de dispositivos eletrônicos de alta frequência, alta temperatura e alta potência. Estes materiais semicondutores de gap largo atraíram enorme atenção dos pesquisadores nos últimos anos. O objetivo desta tese é o estudo das propriedades vibracionais dos nitretos do grupo III referente tanto ao cristal perfeito, quanto ao cristal com defeito. Utilizamos como base a Teoria Clássica do Crital Harmônico e o Método das Funções de Green. Com a Teoria Clássica do Cristal Harmônico, juntamente com o Método do Valence Force Filed e o Método da Soma de Ewald, que permitem gerar a matriz dinâmica do sistema, determinamos o comportamento vibracional dos nitretos binários e das ligas ternárias. A utilização destes métodos permitiu a obtenção do espectro de fônons dos nitretos binários, e o estudo do comportamento dos modos ópticos em para as ligas ternárias. A partir da Função de Green do cristal perfeito e da Função de Green do cristal com defeito, obtivemos as frequências e os modos vibracionais localizados e ressonantes introduzidos pela impureza de C e As em GaN. A partir das densidades de estados do cristal perfeito e do cristal com defeito, calculamos a entropia de formação da vacância de N em GaN. Os resultados obtidos foram usados na interpretação de dados experimentais disponíveis na literatura, relativos às propriedades vibracionais dos nitretos na estrutura wurtzita, e na predição e análise de dados experimentais obtidos pelo grupo do Laboratório de Novos Materiais Semicondutores do Instituto de Física da USP para os nitretos zincblende. / The group-III nitrides (BN, AIN, GaN and InN) and their ternary alloys AlGaN and InGaN generated recently an extraordirlary progress in the production of optoelectronic devices operating in the green-blue-UV region of the spectrum, and in the production of electronic devices of high frequency, high temperature and high power. These wide gap semiconductor materials attracted enormous attention in the last years. The objective of this Thesis was to study the vibrational properties of the bulk III nitrides, without and with defects. To accomplish this study we used the Classic Theory of the Harmonic Crystal and the Method of the Green\'s Functions. With the Classic Theory of the Harmonic Crystal, together with the Valence Force Field Method and the Method of the Ewald\'s Sum, that allow to generate the dynamic matrix of the system, we determined the vibrational behavior of the binary nitrides and of the ternary alloys. The use of these methods allowed us to obtain the phonon spectra of the binary nitrides and to study the behavior of the optical modes at of the ternary alloys. Starting from the Green\'s Function of the perfect crystal and the Green\'s Function of the crystal with defect, we obtained the frequencies and the localized and resonant vibrational modes introduced by the C and As impurities in GaN. Starting from the densities of states of the perfect crystal and of the crystal with defect, we calculated the formation entropy of the N vacancy in GaN. The obtained results were used in the interpretation of experimental data related to the vibrational properties of the wurtzite nitrides available in the literature, and in the prediction and analysis of experimental results obtained for zincblende nitrides by the group of the New Semiconductors Materials Laboratory of t11c Physics Institute at USP.

Force fields have been an integral part of computational chemistry for decades, providing invaluable insight and facilitating the better understanding of biomolecular system behaviour. Despite the many benefits of a force field, there continue to be deficiencies as a result of the classical architecture they are based upon. Some deficiencies, such as a point charge electrostatic description instead of a multipole moment description, have been addressed over time, permitted by the ever-increasing computational power available. However, whilst incorporating such significant improvements has improved force field accuracy, many still fail to describe several chemical effects including polarisation, non-covalent interactions and secondary/tertiary structural effects. Furthermore, force fields often fail to provide consistency when compared with other force fields. In other words, no force field is reliably performing more accurately than others, when applied to a variety of related problems. The work presented herein develops a next-generation force field entitled FFLUX, which features a novel architecture very different to any other force field. FFLUX is designed to capture the relationship between geometry and energy through a machine learning method known as kriging. Instead of a series of parameterised potentials, FFLUX uses a collection of atomic energy kriging models to make energy predictions. The energies describing atoms within FFLUX are obtained from the Interacting Quantum Atoms (IQA) energy partitioning approach, which in turn derives the energies from the electron density and nuclear charges of topological atoms described by Quantum Chemical Topology (QCT). IQA energies are shown to provide a unique insight into the relationship between geometry and energy, allowing the identification of explicit atoms and energies contributing towards torsional barriers within various systems. The IQA energies can be modelled to within 2.6% accuracy, as shown for a series of small systems including weakly bound complexes. The energies also allow an interpretation of how an atom feels its surrounding environment through intra-atomic, covalent and electrostatic energetic descriptions, which typically are seen to converge within a ~7 - 8 A horizon radius around an atom or small system. These energy convergence results are particularly relevant to tackling the transferability theme within force field development. Where energies are seen to converge, a proximity limit on the geometrical description needed for a transferable energy model is defined. Finally, the FFLUX force field is validated through successfully optimising distorted geometries of a series of small molecules, to near-ab initio accuracy.

Doctor of Philosophy / Department of Chemistry / Paul Edward Smith / The activity and function of many macromolecules in cellular environments are coupled with the binding of ions such as alkaline earth metal ions and poly oxo anions. These ions are involved in the regulation of important processes such as protein crystallization, nucleic acid and protein stability, enzyme activity, and many others. The exact mechanism of ion specificity is still elusive. In principle, computer simulations can be used to help provide a molecular level understanding of the dynamics of hydrated ions and their interactions with the biomolecules. However, most of the force fields available today often fail to accurately reproduce the properties of ions in aqueous environments. Here we develop a classical non polarizable force field for aqueous alkaline earth metal halides (MX₂) where M = Mg²⁺, Ca²⁺, Sr²⁺, Ba²⁺ and X = Cl⁻, Br⁻, I⁻, and for some biologically important oxo anions which are NO₃⁻, ClO₄⁻, H₂PO₄⁻ and SO₄²⁻, for use in biomolecular simulations. The new force field parameters are developed to reproduce the experimental Kirkwood-Buff integrals. The Kirkwood-Buff integrals can be used to quantify the affinity between molecular species in solution. This helps to capture the fine balance between the interactions of ions and water. Since this new force field can reproduce the experimental Kirkwood-Buff integrals for most concentrations of the respective salts, they are capable of reproduce the experimental activity derivatives, partial molar volumes, and excess coordination numbers. Use of these new models in MD simulations also leads to reasonable diffusion constants and dielectric decrements. Attempts to develop force field parameters for CO₃²⁻, HPO₄²⁻ and PO₄³⁻ ions were unsuccessful due to an excessive aggregation behavior in the simulations. Therefore, in an effort to overcome this aggregation behavior in the simulations, we have investigated scaling the anion to water interaction strength, and also the possibility of using a high frequency permittivity in the simulations. The strategy of increasing relative permittivity of the system to mimic electronic screening effects are particularly promising for decreasing the excessive ion clustering observed in the MD simulations.

With recent advances and cost reductions in next generation sequencing (NGS), the amount of genetic sequence data is increasing rapidly. However, before patient specific genetic information reaches its full potential to advance clinical diagnostics, the immense degree of genetic heterogeneity that contributes to human disease must be more fully understood. For example, although large numbers of genetic variations are discovered during clinical use of NGS, annotating and understanding the impact of such coding variations on protein phenotype remains a bottleneck (i.e. what is the molecular mechanism behind deafness phenotypes). Fortunately, computational methods are emerging that can be used to efficiently study protein coding variants, and thereby overcome the bottleneck brought on by rapid adoption of clinical sequencing. To study proteins via physics-based computational algorithms, high-quality 3D structural models are essential. These protein models can be obtained using a variety of numerical optimization methods that operate on physics-based potential energy functions. Accurate protein structures serve as input to downstream variation analysis algorithms. In this work, we applied a novel amino acid side-chain optimization algorithm, which operated on an advanced model of atomic interactions (i.e. the AMOEBA polarizable force field), to a set of 164 protein structural models implicated in deafness. The resulting models were evaluated with the MolProbity structure validation tool. MolProbity “scores” were originally calibrated to predict the quality of X-ray diffraction data used to generate a given protein model (i.e. a 1.0 Å or lower MolProbity score indicates a protein model from high quality data, while a score of 4.0 Å or higher reflects relatively poor data). In this work, the side-chain optimization algorithm improved mean MolProbity score from 2.65 Å (42nd percentile) to nearly atomic resolution at 1.41 Å (95th percentile). However, side-chain optimization with the AMOEBA many-body potential function is computationally expensive. Thus, a second contribution of this work is a parallelization scheme that utilizes nVidia graphical processing units (GPUs) to accelerate the side-chain repacking algorithm. With the use of one GPU, our side-chain optimization algorithm achieved a 25 times speed-up compared to using two Intel Xeon E5-2680v4 central processing units (CPUs). We expect the GPU acceleration scheme to lessen demand on computing resources dedicated to protein structure optimization efforts and thereby dramatically expand the number of protein structures available to aid in interpretation of missense variations associated with deafness.

Biological molecules are essential parts of organisms and participate in a variety of biological processes within cells. Understanding the relationship between sequence, structure, and function of biological molecules are of fundamental importance in life science and the health care industry. In this dissertation, a multi-scale approach was utilized to develop coarse-grained molecular models for protein and RNA simulations. By simplifying the atomistic representation of a biomolecular system, the coarse-grained approach enables the molecular dynamics simulations to reveal the biological processes, which occur on the time and length scales that are inaccessible to the all-atom models. For RNA, an "intermediate" coarse-grained model was proposed to provide both accuracy and efficiency for RNA 3D structure modeling and prediction. The overall potential parameters were derived based on structural statistics sampled from experimental structures. For protein, a general, transferable coarse-grain framework based on the Gay-Berne potential and electrostatic point multipole expansion was developed for polypeptide simulations. Next, an advanced atomistic model was developed to model electrostatic interaction with high resolution and incorporates electronic polarization effect that is ignored in conventional atomistic models. The last part of my thesis work involves applying all-atom molecular simulations to address important questions and problems in biophysics and structural biology. For example, the interaction between protein and miRNA, the recognition mechanism of antigen and antibody, and the structure dynamics of protein in mixed denaturants. / text

A molecule can crystallise in more than one crystal structure, a common phenomenon in organic compounds known as polymorphism. Different polymorphic forms may have significantly different physical properties, and a reliable prediction would be beneficial to the pharmaceutical industry. However, crystal structure prediction (CSP) based on the knowledge of the chemical structure had long been considered impossible. Previous failures of some CSP attempts led to speculation that the thermodynamic calculations in CSP methodologies failed to predict the kinetically favoured structures. Similarly, regarding the stabilities of co-crystals relative to their pure components, the results from lattice energy calculations and full CSP studies were inconclusive. In this thesis, these problems are addressed using the state-of-the-art CSP methodology implemented in the GRACE software. Firstly, it is shown that the low-energy predicted structures of four organic molecules, which have previously been considered difficult for CSP, correspond to their experimental structures. The possible outcomes of crystallisation can be reliably predicted by sufficiently accurate thermodynamic calculations. Then, the polymorphism of 5- chloroaspirin is investigated theoretically. The order of polymorph stability is predicted correctly and the isostructural relationships between a number of predicted structures and the experimental structures of other aspirin derivatives are established. Regarding the stabilities of co-crystals, 99 out of 102 co-crystals and salts of nicotinamide, isonicotinamide and picolinamide reported in the Cambridge Structural Database (CSD) are found to be more stable than their corresponding co-formers. Finally, full CSP studies of two co-crystal systems are conducted to explain why the co-crystals are not easily obtained experimentally.

Understanding the neural processes underlying motor learning in humans is important to facilitate the acquisition of new motor skills and to aid the relearning of skills lost after neurologic injury. Although it is known that the learning of a new movement is guided by the error feedback received after each repeated attempt to produce the movement, how the central nervous system (CNS) processes individual errors and how it modulates its learning rate in response to the history of errors experienced are issues that remain to be elucidated. To address these issues we studied the generalization of learning and learning decay – the transfer of what has been learned, or unlearned, in a particular movement condition to new movement conditions. Generalization offers a window into the process of error credit assignment during motor learning, since it allows us to measure which actions benefit the most in terms of learning after experiencing an error. We found that the distributions that describe generalization after learning are unimodal and biased towards the motion directions experienced during training, a finding that suggests that the credit for the learning experienced after a particular trial is assigned to the actual motion (motion-referenced learning) and not to the planned motion (plan-referenced learning) as it had previously been assumed in the motor learning literature. In addition, after training the same action along multiple directions, we found that the pattern of learning decay has two distinct components: one that is time-dependent and affects all trained directions, and one that is trial-dependent and affects mostly the direction where decay was induced, generalizing narrowly with a unimodal pattern similar to the one observed for learning generalization. We finally studied the effect that the consistency of the error perturbations in the training environment has on the learning rate adopted by the CNS. We found that learning rate increases when the perturbations experienced in training are consistent, and decreases when these perturbations are inconsistent. Besides increasing our understanding of the mechanisms underlying motor learning, the findings described in the present dissertation will enable the principled design of skill training and rehabilitation protocols that accelerate learning. / Engineering and Applied Sciences