Since many years, carbon nanotubes (CNTs) have been considered for a wide range of applications due to their outstanding mechanical properties. CNTs are tubular structures, showing a graphene like hexagonal lattice. Our interest in the calculation of the mechanical properties is motivated by several applications which demand the knowledge of the material behavior. One application in which the knowledge of the material behavior is vital is the CNT based fiber. Due to the excellent stiffness and strength of the individual CNTs, these fibers are expected to be a promising successor for state of the art carbon fibers. However, the mechanical properties of the fibers fall back behind the properties of individual CNTs. It is assumed that this gap in the properties is a result of the van-der-Waals interactions of the individual CNTs within the fiber. In order to understand the mechanical behavior of the fibers we apply a molecular mechanics approach. The mechanical properties of the individual CNTs are investigated by using a modified structural molecular mechanics approach. This is done by calculating the properties of a truss-beam element framework representing the CNT with the help of a chemical force field. Furthermore, we also investigate the interactions of CNTs arranged in basic CNT assemblies, mimicking the ones in a simple CNT fiber. We consider the van-der-Waals interactions in the structure and calculate the potential surface of the CNT assemblies.

This dissertation details the use of computational methods to understand the effect that cancer-related mutations have on proteins that complex with nucleic acids. Firstly, we perform molecular dynamics (MD) simulations of various mutations in DNA polymerase κ (pol κ). Through an experimental collaboration, we classify the mutations as more or less active than the wild type complex, depending upon the incoming nucleotide triphosphate. From these classifications we use quantum mechanics/molecular mechanics (QM/MM) to explore the reaction mechanism. Preliminary analysis points to a novel method for nucleotide addition in pol κ. Secondly, we study the ten-eleven translocation 2 (TET2) enzyme in various contexts. We find that the identities of both the substrate and complementary strands (or lack thereof) are crucial for maintaining the complex structure. Separately, we find that point mutations within the protein can affect structural features throughout the complex, only at distal sites, or only within the active site. The mutation's position within the complex alone is not indicative of its impact. Thirdly, we share a new method that combines direct coupling analysis and MD to predict potential rescue mutations using poly(ADP-ribose) polymerase 1 as a model enzyme. Fourthly, we perform MD simulations of mutations in the protection of telomeres 1 (POT1) enzyme. The investigated variants modify the POT1-ssDNA complex dynamics and protein—DNA interactions. Fifthly, we investigate the incorporation of remdesivir and other nucleotide analogue prodrugs into the protein-RNA complex of severe acute respiratory syndrome-coronavirus 2 RNA-dependent RNA polymerase. We find evidence for destabilization throughout the complex and differences in inter-subunit communication for most of the incorporation patterns studied. Finally, we share a method for determining a minimum active region for QM/MM simulations. The method is validated using 4-oxalocrotonate, TET2, and DNA polymerase λ as test cases.

Le but de cette thèse était de modéliser la réponse mécanique de nanotubes de carbone à des champs électriques. Nous avons commencé par utiliser le potentiel AIREBO dans des simulations de dynamique moléculaire afin d'étudier l'élasticité non-linéaire et la limite de déformation en torsion de divers nanotubes, en fonction de leur longueur, rayon et chiralité. Nous trouvons notamment que le module d'Young effectif des tubes décroît d'autant plus vite que la chiralité est faible. D'autre part, nous montrons que la limite de l'énergie stockable par atome lors de la torsion d'un tube est d'autant plus grande que le diamètre est petit.<br><br>Nous modélisons ensuite, de façon atomistique, la distribution surfacique de charge électrique sur des nanotubes de carbone possédant une charge nette. Nous retrouvons notamment l'effet de pointe classique avec un très bon accord quantitatif avec des résultats expérimentaux obtenus par microscopie à force électrostatique.<br><br>Par combinaison des méthodes utilisées dans les études précédentes, nous simulons la déflection de nanotubes semi-conducteurs et métalliques par un champ électrique extérieur, dans une configuration de type interrupteur moléculaire. L'effet des caractéristiques géométriques des tubes et du champ sur cette déflection ont été systématiquement étudiés.<br><br>En outre, nous avons vu que des simulations de dynamique moléculaire avec le potentiel AIREBO permettent de retrouver quantitativement les énergies expérimentales d'adsorption du benzène, du naphtalène et d'anthracène sur le graphite. Ce type de simulation nous permet d'avancer sur la voie de la compréhension de la sélectivité de l'adsorption de certaines molécules surfactantes à plusieurs cycles benzéniques sur des nanotubes de chiralité donnée.

This work concerns the interrelationship between thermodynamic, kinetic and structural aspects of crystal polymorphism. It is both experimental and theoretical, and limited with respect to compounds to substituted monocyclic aromatics. Two polymorphs of the compound m-aminobenzoic acid have been experimentally isolated and characterized by ATR-FTIR spectroscopy, X-ray powder diffraction and optical microscopy. In addition, two polymorphs of the compound m-hydroxybenzoic acid have been isolated and characterized by ATR-FTIR spectroscopy, high-temperature XRPD, confocal Raman, hot-stage and scanning electron microscopy. For all polymorphs, melting properties and specific heat capacity have been determined calorimetrically, and the solubility in several pure solvents measured at different temperatures with a gravimetric method. The solid-state activity (ideal solubility), and the free energy, enthalpy and entropy of fusion have been determined as functions of temperature for all solid phases through a thermodynamic analysis of multiple experimental data. It is shown that m-aminobenzoic acid is an enantiotropic system, with a stability transition point determined to be located at approximately 156°C, and that the difference in free energy at room temperature between the polymorphs is considerable. It is further shown that m-hydroxybenzoic acid is a monotropic system, with minor differences in free energy, enthalpy and entropy. 1393 primary nucleation experiments have been carried out for both compounds in different series of repeatability experiments, differing with respect to solvent, cooling rate, saturation temperature and solution preparation and pre-treatment. It is found that in the vast majority of experiments, either the stable or the metastable polymorph is obtained in the pure form, and only for a few evaluated experimental conditions does one polymorph crystallize in all experiments. The fact that the polymorphic outcome of a crystallization is the result of the interplay between relative thermodynamic stability and nucleation kinetics, and that it is vital to perform multiple experiments under identical conditions when studying nucleation of polymorphic compounds, is strongly emphasized by the results of this work. The main experimental variable which in this work has been found to affect which polymorph will preferentially crystallize is the solvent. For m-aminobenzoic acid, it is shown how a significantly metastable polymorph can be obtained by choosing a solvent in which nucleation of the stable form is sufficiently obstructed. For m-hydroxybenzoic acid, nucleation of the stable polymorph is promoted in solvents where the solubility is high. It is shown how this partly can be rationalized by analysing solubility data with respect to temperature dependence. By crystallizing solutions differing only with respect to pre-treatment and which polymorph was dissolved, it is found that the immediate thermal and structural history of a solution can have a significant effect on nucleation, affecting the predisposition for overall nucleation as well as which polymorph will preferentially crystallize. A set of polymorphic crystal structures has been compiled from the Cambridge Structural Database. It is found that statistically, about 50% crystallize in the crystallographic space group P21/c. Furthermore, it is found that crystal structures of polymorphs tend to differ significantly with respect to either hydrogen bond network or molecular conformation. Molecular mechanics based Monte Carlo simulated annealing has been used to sample different potential crystal structures corresponding to minima in potential energy with respect to structural degrees of freedom, restricted to one space group, for each of the polymorphic compounds. It is found that all simulations result in very large numbers of predicted structures. About 15% of the predicted structures have excess relative lattice energies of &lt;=10% compared to the most stable predicted structure; a limit verified to reflect maximum lattice energy differences between experimentally observed polymorphs of similar compounds. The number of predicted structures is found to correlate to molecular weight and to the number of rotatable covalent bonds. A close study of two compounds has shown that predicted structures tend to belong to different groups defined by unique hydrogen bond networks, located in well-defined regions in energy/packing space according to the close-packing principle. It is hypothesized that kinetic effects in combination with this structural segregation might affect the number of potential structures that can be realized experimentally. The experimentally determined crystal structures of several compounds have been geometry-optimized (relaxed) to the nearest potential energy minimum using ten different combinations of common potential energy functions (force fields) and techniques for assigning nucleus-centred point charges used in the electrostatic description of the energy. Changes in structural coordinates upon relaxation have been quantified, crystal lattice energies calculated and compared with experimentally determined enthalpies of sublimation, and the energy difference before and after relaxation computed and analysed. It is found that certain combinations of force fields and charge assignment techniques work reasonably well for modelling crystal structures of small aromatics, provided that proper attention is paid to electrostatic description and to how the force field was parameterized. A comparison of energy differences for randomly packed as well as experimentally determined crystal structures before and after relaxation suggests that the potential energy function for the solid state of a small organic molecule is highly undulating with many deep, narrow and steep minima. / QC 20110527

<p>In this thesis, we present novel computational methods and frameworks to address the challenges associated with the determination of free energy profiles for condensed-phase chemical reactions using combined quantum mechanical and molecular mechanical (QM/MM) approaches. We focus on overcoming issues related to force matching, molecular polarizability, and convergence of free energy profiles. First, we introduce a method called Reaction Path-Force Matching in Collective Variables (RP-FM-CV) that efficiently carries out ab initio QM/MM free energy simulations through mean force fitting. This method provides accurate and robust simulations of solution-phase chemical reactions by significantly reducing deviations on the collective variables forces, thereby bringing simulated free energy profiles closer to experimental and benchmark AI/MM results. Second, we explore the role of pairwise repulsive correcting potentials in generating converged free energy profiles for chemical reactions using QM/MM simulations. We develop a free energy correcting model that sheds light on the behavior of repulsive pairwise potentials with large force deviations in collective variables. Our findings contribute to a deeper understanding of force matching models, paving the way for more accurate predictions of free energy profiles in chemical reactions. Next, we address the underpolarization problem in semiempirical (SE) molecular orbital methods by introducing a hybrid framework called doubly polarized QM/MM (dp-QM/MM). This framework improves the response property of SE/MM methods through high-level molecular polarizability fitting using machine learning (ML)-derived corrective polarizabilities, referred to as chaperone polarizabilities. We demonstrate the effectiveness of the dp-QM/MM method in simulating the Menshutkin reaction in water, showing that ML chaperones significantly reduce the error in solute molecular polarizability, bringing simulated free energy profiles closer to experimental results. In summary, this thesis presents a series of novel methods and frameworks that improve the accuracy and reliability of free energy profile estimations in condensed-phase chemical reactions using QM/MM simulations. By addressing the challenges of force matching, molecular polarizability, and convergence, these advancements have the potential to impact various fields, including computational chemistry, materials science, and drug design.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / In this thesis, we present novel computational methods and frameworks to address the challenges associated with the determination of free energy profiles for condensed-phase chemical reactions using combined quantum mechanical and molecular mechanical (QM/MM) approaches. We focus on overcoming issues related to force matching, molecular polarizability, and convergence of free energy profiles. First, we introduce a method called Reaction Path-Force Matching in Collective Variables (RP-FM-CV) that efficiently carries out ab initio QM/MM free energy simulations through mean force fitting. This method provides accurate and robust simulations of solution-phase chemical reactions by significantly reducing deviations on the collective variables forces, thereby bringing simulated free energy profiles closer to experimental and benchmark AI/MM results. Second, we explore the role of pairwise repulsive correcting potentials in generating converged free energy profiles for chemical reactions using QM/MM simulations. We develop a free energy correcting model that sheds light on the behavior of repulsive pairwise potentials with large force deviations in collective variables. Our findings contribute to a deeper understanding of force matching models, paving the way for more accurate predictions of free energy profiles in chemical reactions. Next, we address the underpolarization problem in semiempirical (SE) molecular orbital methods by introducing a hybrid framework called doubly polarized QM/MM (dp-QM/MM). This framework improves the response property of SE/MM methods through high-level molecular polarizability fitting using machine learning (ML)-derived corrective polarizabilities, referred to as chaperone polarizabilities. We demonstrate the effectiveness of the dp-QM/MM method in simulating the Menshutkin reaction in water, showing that ML chaperones significantly reduce the error in solute molecular polarizability, bringing simulated free energy profiles closer to experimental results. In summary, this thesis presents a series of novel methods and frameworks that improve the accuracy and reliability of free energy profile estimations in condensed-phase chemical reactions using QM/MM simulations. By addressing the challenges of force matching, molecular polarizability, and convergence, these advancements have the potential to impact various fields, including computational chemistry, materials science, and drug design.

En sjukdom kan många gånger härledas till en kaskadereaktion mellan proteiner, co-faktorer och substrat. Denna kaskadreaktion blir många gånger målet för att behandla sjukdomen med läkemedel. För att designa nya läkemedelsmoleyler används vanligen datorbaserade verktyg. Denna design av läkemedelsmolekyler drar stor nytta av att målproteinet är känt och då framförallt dess tredimensionella (3D) struktur. Är 3D-strukturen känd kan man utföra så kallad struktur- och datorbaserad molekyldesign, 3D-geometrin (f.f.a. för inbindningsplatsen) blir en vägledning för designen av en ny molekyl. Många faktorer avgör interaktionen mellan en molekyl och bindningsplatsen, till exempel fysikalisk-kemiska egenskaper hos molekylen och bindningsplatsen, flexibiliteten i molekylen och målproteinet, och det omgivande lösningsmedlet. För att strukturbaserad molekyldesign ska fungera väl måste två viktiga steg utföras: i) 3D anpassning av molekyler till bindningsplatsen i ett målprotein (s.k. dockning) och ii) prediktion av molekylers affinitet för bindningsplatsen. Huvudsyftena med arbetet i denna avhandling var som följer: i) skapa modeler för att prediktera affiniteten mellan en molekyl och bindningsplatsen i ett målprotein; ii) förfina molekyl-protein-geometrin som skapas vid 3D-anpassning mellan en molekyl och bindningsplatsen i ett målprotein (s.k. dockning); iii) karaktärisera proteiner och framför allt deras sekundärstruktur; iv) bedöma effekten av olika matematiska beskrivningar av lösningsmedlet för förfining av 3D molekyl-protein-geometrin skapad vid dockning och prediktion av molekylers affinitet för proteiners bindningsfickor. Ett övergripande syfte var att använda kemometriska metoder för modellering och dataanalys på de ovan nämnda punkterna. För att sammanfatta så presenterar denna avhandling metoder och resultat som är användbara för strukturbaserad molekyldesign. De rapporterade resultaten visar att det är möjligt att skapa kemometriska modeler för prediktion av molekylers affinitet för bindningsplatsen i ett protein och att dessa presterade lika bra som andra vanliga metoder. Dessutom kunde kemometriska modeller skapas för att beskriva effekten av hur inställningarna för olika parametrar i dockningsprogram påverkade den 3D molekyl-protein-geometrin som dockingsprogram skapade. Vidare kunde kemometriska modeller andvändas för att öka förståelsen för deskriptorer som beskrev sekundärstrukturen i proteiner. Förfining av molekyl-protein-geometrin skapad genom dockning gav liknande och ickesignifikanta resultat oberoende av vilken matematisk modell för lösningsmedlet som användes, förutom för ett fåtal (sex av 30) fall. Däremot visade det sig att användandet av en förfinad geometri var värdefullt för prediktion av molekylers affinitet för bindningsplatsen i ett protein. Förbättringen av prediktion av affintitet var markant då en Poisson-Boltzmann beskrivning av lösningsmedlet användes; jämfört med prediktionerna gjorda med ett dockningsprogram förbättrades korrelationen mellan beräknad affintiet och uppmätt affinitet med 0,7 (R2). / A disease is often associated with a cascade reaction pathway involving proteins, co-factors and substrates. Hence to treat the disease, elements of this pathway are often targeted using a therapeutic agent, a drug. Designing new drug molecules for use as therapeutic agents involves the application of methods collectively known as computer-aided molecular design, CAMD. When the three dimensional (3D) geometry of a macromolecular target (usually a protein) is known, structure-based CAMD is undertaken and structural information of the target guides the design of new molecules and their interactions with the binding sites in targeted proteins. Many factors influence the interactions between the designed molecules and the binding sites of the target proteins, such as the physico-chemical properties of the molecule and the binding site, the flexibility of the protein and the ligand, and the surrounding solvent. In order for structure-based CAMD to be successful, two important aspects must be considered that take the abovementioned factors into account. These are; i) 3D fitting of molecules to the binding site of the target protein (like fitting pieces of a jigsaw puzzle), and ii) predicting the affinity of molecules to the protein binding site. The main objectives of the work underlying this thesis were: to create models for predicting the affinity between a molecule and a protein binding site; to refine the geometry of the molecule-protein complex derived by or in 3D fitting (also known as docking); to characterize the proteins and their secondary structure; and to evaluate the effects of different generalized-Born (GB) and Poisson-Boltzmann (PB) implicit solvent models on the refinement of the molecule-protein complex geometry created in the docking and the prediction of the molecule-to-protein binding site affinity. A further objective was to apply chemometric methodologies for modeling and data analysis to all of the above. To summarize, this thesis presents methodologies and results applicable to structure-based CAMD. Results show that predictive chemometric models for molecule-to-protein binding site affinity could be created that yield comparable results to similar, commonly used methods. In addition, chemometric models could be created to model the effects of software settings on the molecule-protein complex geometry using software for molecule-to-binding site docking. Furthermore, the use of chemometric models provided a more profound understanding of protein secondary structure descriptors. Refining the geometry of molecule-protein complexes created through molecule-to-binding site docking gave similar results for all investigated implicit solvent models, but the geometry was significantly improved in only a few examined cases (six of 30). However, using the geometry-refined molecule-protein complexes was highly valuable for the prediction of molecule-to-binding site affinity. Indeed, using the PB solvent model it yielded improvements of 0.7 in correlation coefficients (R2) for binding affinity parameters of a set of Factor Xa protein drug molecules, relative to those obtained using the fitting software.