Computational chemists have successfully simulated many systems by applying the principles of quantum mechanics, while approximate molecular mechanical models have seen great utility in problems of biochemical interest. In recent years, a number of methods have been developed to combine the advantages of both techniques. In this study the so-called QM/MM method is developed and applied to the determination of the free energy of a simple Menshutkin S_N2 chemical reaction. This is an extremely demanding process, well beyond the computational capacity of an average workstation, and thus a Beowulf-class Linux cluster is constructed to perform the calculations, and tested for a variety of computational chemistry applications. A number of methods for improving the QM/MM approach are considered in this work. The Fluctuating Charge, or FlucQ, polarizable molecular mechanics force field is implemented in a flexible manner within the CHARMM package and tested for a variety of systems, including the S_N2 test case. Several drawbacks of the original method are addressed and overcome. Both molecular dynamics and Monte Carlo techniques are used within the QM/MM framework to investigate the S_N2 reaction, and the two methods are compared. Techniques are developed and tested to increase the efficiency of QM/MC calculations to the point where they become competitive with QM/MD. Extremely expensive QM treatments are shown to be required to obtain accurate energies for the Menshutkin reaction. A method is developed and tested, and compared with the traditional ONIOM technique, for dramatically reducing the computational time required to use these treatments for QM/MC simulations, paving the way for fully ab initio high basis set QM/MM simulation.

Typical, potential-driven force fields have been usefully applied to small molecules for decades. However, complex effects such as polarisation, π systems and hydrogen bonding remain difficult to model while these effects become increasingly relevant. In fact, these complex electronic effects become crucial when considering larger biological molecules in solution. Instead, machine learning can be used to recognise patterns in chemical behaviour and predict them, sacrificing computational efficiency for accuracy and completeness of the force field. The kriging machine learning method is capable of taking the geometric features of a molecule and predicting its electrostatic properties after being trained using ab initio data of the same system. We present significant improvements in functionality, application and understanding of the kriging machine learning as part of an electrostatic force field. These improvements are presented alongside an up-scaling of the problems the force field is applied to. The force field predicts electrostatic energies for all common amino acids with a mean error of 4.2 kJmol-1 (1 kcal mol-1), cholesterol with a mean error of 3.9 kJmol-1 and a 10-alanine helix with a mean error of 6.4 kJmol-1. The kriging machine learning has been shown to work identically with charged systems, π systems and hydrogen bonded systems. This work details how different chemical environments and parameters affect the kriging model quality and assesses optimal methods for computationally-efficient kriging of multipole moments. In addition to this, the kriging models have been used to predict moments for atoms they have had no training data for with little loss in accuracy. Thus, the kriging machine learning has been shown to produce transferable models.

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Príons são proteínas que causam um grupo de doenças neurodegenerativas invariavelmente fatais, sendo uma das mais conhecidas a encefalopatia espongiforme bovina (ou doença da vaca louca). A proteína príon celular (PrPc), rica em estrutura α-helicoidal, sofre uma mudança na sua estrutura secundária produzindo a proteína patológica (PrPSc; o príon) na qual prevalecem folhas-β. Devido à falta de dados estruturais de alta resolução dos príons, simulações de dinâmica molecular (DM) podem ser particularmente úteis para estudar o redobramento de PrP. Estudos experimentais e computacionais, descritos na literatura, indicam que a utilização de pH ácido é capaz de criar certa instabilidade estrutural, produzindo um ganho de estrutura-β na região N-terminal antes desestruturada. Este trabalho se propõe a investigar computacionalmente as mudanças estruturais na proteína príon celular do hamster Sírio induzidas por alteração de pH. Para isso, foi avaliada a influência de diferentes campos de força (GROMOS96 53a6, GROMOS96 43a1, AMBER99SB, AMBER99SB-ILDN, CHARMM27 e OPLS) simulados para as condições de pH neutro e ácido. A partir das análises, observou-se uma forte dependência dos resultados com o campo de força empregado. Além disso, somente os campos de força GROMOS96 53a6 e AMBER99SB demonstraram tendência à expansão do núcleo de folhas-β na região N-terminal da proteína simulada sob pH ácido e conseguiram representar adequadamente a condição neutra. As estruturas correspondentes a esses campos de força em pH ácido, foram, então, utilizadas como ponto de partida para novas simulações de DM em pH neutro (pH 7,4). Essa situação de retorno ao pH neutro ocorre quando o príon sai do compartimento endossomal (submetido a pH ácido) e retorna à superfície externa celular (onde estaria submetida novamente a pH neutro). Os resultados desse estudo de retorno ao pH neutro apontaram para a não reversibilidade de PrPSc, com a manutenção da cauda N-terminal voltada para a extremidade N-terminal da α-hélice HB. / Prions are proteins that cause a group of invariably fatal neurodegenerative diseases, one of the most known being bovine spongiform encephalopathy (or mad cow disease). The cellular prion protein (PrPC), rich in α-helical structure, undergoes a change in its secondary structure producing the pathological protein (PrPSc; the prion) in which β-sheet prevails. Due to the lack of high resolution structural data of the prions, molecular dynamics simulations (MD) may be particularly useful to study the refolding of PrP. Experimental and computational studies, described in the literature, indicate that the use of acidic pH is capable to create some structural instability, producing a gain of β-structure in the previously unstructured N-terminal region. This work proposes to investigate computationally the structural changes in the cellular prion protein of the Syrian hamster induced by pH change. For this, the influence of different force fields (GROMOS96 53a6, GROMOS96 43a1, AMBER99SB, AMBER99SB-ILDN, CHARMM27 and OPLS) were evaluated for neutral and acid pH conditions. From the analysis, a strong dependence of the results with the force field was observed. In addition, only the GROMOS96 53a6 and AMBER99SB force fields showed a tendency to expand the β-sheet nucleus in the N-terminal region of the simulated protein under acid pH and were able to adequately represent the neutral condition. The structures corresponding to these force fields under acidic pH were then used as the starting point for new MD simulations under neutral pH. This situation of return to the neutral pH occurs when the prion leaves the endosomal compartment (submitted to acid pH) and returns to the external cellular surface (where it would be submitted again to neutral pH). The results of this neutral pH return study pointed to the non-reversibility of PrPSc, with the maintenance of the N-terminal tail facing the N-terminal end of the α-helix HB.

One of the central tasks for biomolecular modeling is to develop accurate and computationally cheap methods. In this dissertation, we present the development of a brand new polarizable force field—Polarizable Simulations with Second order Interaction Model (POSSIM) involving electrostatic polarization. The POSSIM framework combines accuracy of a polarizable force field and computational efficiency of the second-order approximation of the full-scale induced point dipole polarization formalism. POSSIM force field has been extended to include parameters for small molecules serving as models for peptide and protein side-chains. Parameters have been fitted to permit reproducing many-body energies, gas-phase dimerization energies and geometries and liquid-phase heats of vaporization and densities. Quantum mechanical and experimental data have been used as the target for the fitting. The resulting parameters can be used for simulations of the parameterized molecules themselves or their analogues. In addition to this, these force field parameters have been employed in further development of the POSSIM fast polarizable force field for proteins. The POSSIM framework has been expanded to include a complete polarizable force field for proteins. Most of the parameter fitting was done to high-level quantum mechanical data. Conformational geometries and energies for dipeptides have been reproduced within average errors of ca. 0.5 kcal/mol for energies of the conformers (for the electrostatically neutral residues) and 9.7º for key dihedral angles. We have also validated this force field by simulating an elastin-like polypeptide GVG(VPGVG)3 in aqueous solution. Elastin-like peptides with the (VPGVG)n motif are known to exhibit anomalous behavior of their radius of gyration that increases when temperature is lowered (the so called inverse temperature transition). We have simulated the system with the OPLS-AA and POSSIM force fields and demonstrated that our newly developed polarizable POSSIM parameters permit to capture the experimentally observed decrease of the radius of gyration with increasing temperature, while the fixed-charges OPLS-AA ones do not. Furthermore, our fitting of the force field parameters for the peptides and proteins has been streamlined compared with the previous generation of the complete polarizable force field and relied more on transferability of parameters for non-bonded interactions (including the electrostatic component). The resulting deviations from the quantum mechanical data are similar to those achieved with the previous generation, thus the technique is robust and the parameters are transferable. At the same time, the number of parameters used in this work was noticeably smaller than that of the previous generation of our complete polarizable force field for proteins, thus the transferability of this set can be expected to be greater and the danger of force field fitting artifacts is lower. Therefore, we believe that this force field can be successfully applied in a wide variety of applications to proteins and protein-ligand complexes.

The objects with which the hand interacts with may significantly change the dynamics of the arm. How does the brain adapt control of arm movements to this new dynamic? We show that adaptation is via composition of a model of the task's dynamics. By exploring generalization capabilities of this adaptation we infer some of the properties of the computational elements with which the brain formed this model: the elements have broad receptive fields and encode the learned dynamics as a map structured in an intrinsic coordinate system closely related to the geometry of the skeletomusculature. The low--level nature of these elements suggests that they may represent asset of primitives with which a movement is represented in the CNS.

About 90% of all chemicals are produced with the help of catalysts, substances with the ability to accelerate reactions without being consumed. Metal oxides play a prominent role in catalysis, since they are able to act reversibly in many chemical processes. Zink oxide (ZnO) is used to catalyse a number of industrially important reactions. For many of these reactions water is present as a reactant, product, or byproduct. The surface structure has a significant impact on the catalytic activity. However, currently, no experimental method simultaneously offers the spatial and temporal resolution to directly follow a catalytic process. This thesis explores surface structure dependent dynamical behavior for ZnO surfaces, nanoparticles, and water interfaces, using the computational chemistry method Molecular Dynamics, which enables detailed studies of structural and dynamical processes. Quantum mechanical (QM) calculations have been performed to obtain the energetics of the materials as a function of structure. This data has been used to parametrize reactive force-fields (ReaxFF), since the catalytic processes require both far larger and longer simulations than the capabilities of QM calculations on current computers. The simulations show that when steps are present on the surface, during crystal growth of ZnO, the creation of energetically favorable structures is accelerated. At the ZnO - water interface, structures that favor hydrogen bonding is promoted. At low, monolayer, coverage water adsorbs both molecularly and dissociatively, whereas at high coverage dissociated adsorption is favored. During evaporation from the monolayers, the ratio of dissociated and molecular water is preserved. Surface steps stabilizes the dissociated state as well as increases the rate of dissociation. The dynamical properties of ZnO nanoparticles were explored using Raman measurements and simulation. In both simulation and experiment certain vibrations were suppressed in the nanoparticles, compared to bulk. The simulations show that a narrow surface region lack the bulk-specific vibrations.

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No description available.

Accurate knowledge of the physical properties of complex chemical systems is of extreme importance for the design and optimization of industrial processes. The unprecedented increase of computing power in the last couple of decades, the development of efficient algorithms and methods, and advances in molecular force fields have made molecular simulation a powerful tool in predicting such properties very accurately, and often with very limited experimental information involved. Related to this, molecular simulation can be used for the design of new materials with improved, often tailor-made, properties compared to existing materials. In this lecture, a few representative examples from recent work related to the oil & gas industry will be discussed.