315 research outputs found

    Universal Pairwise Interatomic van der Waals Potentials Based on Quantum Drude Oscillators

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    Repulsive short-range and attractive long-range van der Waals (vdW) forces have an appreciable role in the behavior of extended molecular systems. When using empirical force fields - the most popular computational methods applied to such systems - vdW forces are typically described by Lennard-Jones-like potentials, which unfortunately have a limited predictive power. Here, we present a universal parameterization of a quantum-mechanical vdW potential, which requires only two free-atom properties - the static dipole polarizability α1\alpha_1 and the dipole-dipole C6C_6 dispersion coefficient. This is achieved by deriving the functional form of the potential from the quantum Drude oscillator (QDO) model, employing scaling laws for the equilibrium distance and the binding energy as well as applying the microscopic law of corresponding states. The vdW-QDO potential is shown to be accurate for vdW binding energy curves, as demonstrated by comparing to ab initio binding curves of 21 noble-gas dimers. The functional form of the vdW-QDO potential has the correct asymptotic behavior both at zero and infinite distances. In addition, it is shown that the damped vdW-QDO potential can accurately describe vdW interactions in dimers consisting of group II elements. Finally, we demonstrate the applicability of the atom-in-molecule vdW-QDO model for predicting accurate dispersion energies for molecular systems. The present work makes an important step towards constructing universal vdW potentials, which could benefit (bio)molecular computational studies

    The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

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    The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations

    Crystal structure prediction for multicomponent systems: energy models and structure generation

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    Crystalline materials have a wide application in the pharmaceutical and agrochemical sectors. The aim of Crystal Structure Prediction (CSP) is to conduct polymorph screening by predicting all possible polymorphs given the chemical diagram of a compound. Various computational programmes have been developed for both academic and industrial use. However, the application to hydrate systems remains challenging. The aim of this thesis is to explore and improve the applicability of CSP for hydrates. In this thesis, I first examined the applicability of a current lattice energy model for hydrates: This model consists of anisotropic distributed multipole moments derived from isolated-molecule quantum mechanical calculations to model the electrostatic interactions, combined with isotropic atom-atom exp-6 Buckingham potential along with empirical pa- rameters to model repulsion and dispersion interactions. It has been shown to be successful in determining the low-energy structures of small organic crystals. By giving 107 exper- imental hydrates extracted from the Cambridge Structural Database as starting points, I found that the energy model is able to reproduce around 95% of the structural geometry with different quantum mechanical levels of theory. The relative stability ordering based on the lattice energy for computed structures was, however, not always satisfactory and varies with the level of theory adopted. The energy model also revealed an underestima- tion of the binding energy for hydrate and hydrogen-bonding systems. The accuracy of our current energy model was insufficient for modelling crystals with complex short-range interactions, especially hydrogen bonds. I postulated that this can be addressed with the inclusion of an explicit induction energy correction in the model. Hence I examined the use of the isolated-molecule assumption and the polarisable con- tinuum model (PCM) corrections within hydrate prediction. The electrostatics derived from ab initio molecular charge densities in the gas phase are replaced by simulations within a field of the surrounding molecules represented by point charges. Distributed multipolar representation of the electron density perturbation was applied in the classi- cal polarisation model for the evaluation of the induction energy. The integration of this process for modelling induction into a current CSP methodology was achieved. The im- plementation was based on the recently developed lattice energy minimisation programme known as Crystal Structure Optimizer – Rigid Molecules (CSO-RM) for rigid-body sys- tems, and its companion Crystal Structure Optimizer – Flexible Molecules (CSO-FM) to account for conformational flexibility. I assessed the energy rankings of experimental matches before and after induction corrections for three small organic hydrate systems, namely 2,6-diamino-4(3H)-pyrimidinone, gallic acid and theophylline, as well as demon- strating the importance of induction in the carbamazepine and diglycine crystals. The contribution to the lattice energy from explicit induction term was generally found to favour hydrogen-bonding systems, and has been found to result in significant improvement among polymorphic/computed forms. Another aspect of this work focused on improving the global search efficiency of the initial structure generation. I modified the current methodology, which suffers the frequent occurrence of molecular overlaps. The modification could increase the initial structure generation speed by to four times while preserving the quality of structures generated.Open Acces

    Metal Cations in Protein Force Fields: From Data Set Creation and Benchmarks to Polarizable Force Field Implementation and Adjustment

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    Metal cations are essential to life. About one-third of all proteins require metal cofactors to accurately fold or to function. Computer simulations using empirical parameters and classical molecular mechanics models (force fields) are the standard tool to investigate proteins’ structural dynamics and functions in silico. Despite many successes, the accuracy of force fields is limited when cations are involved. The focus of this thesis is the development of tools and strategies to create system-specific force field parameters to accurately describe cation-protein interactions. The accuracy of a force field mainly relies on (i) the parameters derived from increasingly large quantum chemistry or experimental data and (ii) the physics behind the energy formula. The first part of this thesis presents a large and comprehensive quantum chemistry data set on a consistent computational footing that can be used for force field parameterization and benchmarking. The data set covers dipeptides of the 20 proteinogenic amino acids with different possible side chain protonation states, 3 divalent cations (Ca2+, Mg2+, and Ba2+), and a wide relative energy range. Crucial properties related to force field development, such as partial charges, interaction energies, etc., are also provided. To make the data available, the data set was uploaded to the NOMAD repository and its data structure was formalized in an ontology. Besides a proper data basis for parameterization, the physics covered by the terms of the additive force field formulation model impacts its applicability. The second part of this thesis benchmarks three popular non-polarizable force fields and the polarizable Drude model against a quantum chemistry data set. After some adjustments, the Drude model was found to reproduce the reference interaction energy substantially better than the non-polarizable force fields, which showed the importance of explicitly addressing polarization effects. Tweaking of the Drude model involved Boltzmann-weighted fitting to optimize Thole factors and Lennard-Jones parameters. The obtained parameters were validated by (i) their ability to reproduce reference interaction energies and (ii) molecular dynamics simulations of the N-lobe of calmodulin. This work facilitates the improvement of polarizable force fields for cation-protein interactions by quantum chemistry-driven parameterization combined with molecular dynamics simulations in the condensed phase. While the Drude model exhibits its potential simulating cation-protein interactions, it lacks description of charge transfer effects, which are significant between cation and protein. The CTPOL model extends the classical force field formulation by charge transfer (CT) and polarization (POL). Since the CTPOL model is not readily available in any of the popular molecular-dynamics packages, it was implemented in OpenMM. Furthermore, an open-source parameterization tool, called FFAFFURR, was implemented that enables the (system specific) parameterization of OPLS-AA and CTPOL models. Following the method established in the previous part, the performance of FFAFFURR was evaluated by its ability to reproduce quantum chemistry energies and molecular dynamics simulations of the zinc finger protein. In conclusion, this thesis steps towards the development of next-generation force fields to accurately describe cation-protein interactions by providing (i) reference data, (ii) a force field model that includes charge transfer and polarization, and (iii) a freely-available parameterization tool.Metallkationen sind fĂŒr das Leben unerlĂ€sslich. Etwa ein Drittel aller Proteine benötigen Metall-Cofaktoren, um sich korrekt zu falten oder zu funktionieren. Computersimulationen unter Verwendung empirischer Parameter und klassischer MolekĂŒlmechanik-Modelle (Kraftfelder) sind ein Standardwerkzeug zur Untersuchung der strukturellen Dynamik und Funktionen von Proteinen in silico. Trotz vieler Erfolge ist die Genauigkeit der Kraftfelder begrenzt, wenn Kationen beteiligt sind. Der Schwerpunkt dieser Arbeit liegt auf der Entwicklung von Werkzeugen und Strategien zur Erstellung systemspezifischer Kraftfeldparameter zur genaueren Beschreibung von Kationen-Protein-Wechselwirkungen. Die Genauigkeit eines Kraftfelds hĂ€ngt hauptsĂ€chlich von (i) den Parametern ab, die aus immer grĂ¶ĂŸeren quantenchemischen oder experimentellen Daten abgeleitet werden, und (ii) der Physik hinter der Kraftfeld-Formel. Im ersten Teil dieser Arbeit wird ein großer und umfassender quantenchemischer Datensatz auf einer konsistenten rechnerischen Grundlage vorgestellt, der fĂŒr die Parametrisierung und das Benchmarking von Kraftfeldern verwendet werden kann. Der Datensatz umfasst Dipeptide der 20 proteinogenen AminosĂ€uren mit verschiedenen möglichen Seitenketten-ProtonierungszustĂ€nden, 3 zweiwertige Kationen (Ca2+, Mg2+ und Ba2+) und einen breiten relativen Energiebereich. Wichtige Eigenschaften fĂŒr die Entwicklung von Kraftfeldern, wie Wechselwirkungsenergien, Partialladungen usw., werden ebenfalls bereitgestellt. Um die Daten verfĂŒgbar zu machen, wurde der Datensatz in das NOMAD-Repository hochgeladen und seine Datenstruktur wurde in einer Ontologie formalisiert. Neben einer geeigneten Datenbasis fĂŒr die Parametrisierung beeinflusst die Physik, die von den Termen des additiven Kraftfeld-Modells abgedeckt wird, dessen Anwendbarkeit. Der zweite Teil dieser Arbeit vergleicht drei populĂ€re nichtpolarisierbare Kraftfelder und das polarisierbare Drude-Modell mit einem Datensatz aus der Quantenchemie. Nach einigen Anpassungen stellte sich heraus, dass das Drude-Modell die Referenzwechselwirkungsenergie wesentlich besser reproduziert als die nichtpolarisierbaren Kraftfelder, was zeigt, wie wichtig es ist, Polarisationseffekte explizit zu berĂŒcksichtigen. Die Anpassung des Drude-Modells umfasste eine Boltzmann-gewichtete Optimierung der Thole-Faktoren und Lennard-Jones-Parameter. Die erhaltenen Parameter wurden validiert durch (i) ihre FĂ€higkeit, Referenzwechselwirkungsenergien zu reproduzieren und (ii) Molekulardynamik-Simulationen des Calmodulin-N-Lobe. Diese Arbeit demonstriert die Verbesserung polarisierbarer Kraftfelder fĂŒr Kationen-Protein-Wechselwirkungen durch quantenchemisch gesteuerte Parametrisierung in Kombination mit Molekulardynamiksimulationen in der kondensierten Phase. WĂ€hrend das Drude-Modell sein Potenzial bei der Simulation von Kation - Protein - Wechselwirkungen zeigt, fehlt ihm die Beschreibung von Ladungstransfereffekten, die zwischen Kation und Protein von Bedeutung sind. Das CTPOL-Modell erweitert die klassische Kraftfeldformulierung um den Ladungstransfer (CT) und die Polarisation (POL). Da das CTPOL-Modell in keinem der gĂ€ngigen Molekulardynamik-Pakete verfĂŒgbar ist, wurde es in OpenMM implementiert. Außerdem wurde ein Open-Source-Parametrisierungswerkzeug namens FFAFFURR implementiert, welches die (systemspezifische) Parametrisierung von OPLS-AA und CTPOL-Modellen ermöglicht. In Anlehnung an die im vorangegangenen Teil etablierte Methode wurde die Leistung von FFAFFURR anhand seiner FĂ€higkeit, quantenchemische Energien und Molekulardynamiksimulationen des Zinkfingerproteins zu reproduzieren, bewertet. Zusammenfassend lĂ€sst sich sagen, dass diese Arbeit einen Schritt in Richtung der Entwicklung von Kraftfeldern der nĂ€chsten Generation zur genauen Beschreibung von Kationen-Protein-Wechselwirkungen darstellt, indem sie (i) Referenzdaten, (ii) ein Kraftfeldmodell, das Ladungstransfer und Polarisation einschließt, und (iii) ein frei verfĂŒgbares Parametrisierungswerkzeug bereitstellt

    Electron Thermal Runaway in Atmospheric Electrified Gases: a microscopic approach

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    Thesis elaborated from 2018 to 2023 at the Instituto de AstrofĂ­sica de AndalucĂ­a under the supervision of Alejandro Luque (Granada, Spain) and Nikolai Lehtinen (Bergen, Norway). This thesis presents a new database of atmospheric electron-molecule collision cross sections which was published separately under the DOI : With this new database and a new super-electron management algorithm which significantly enhances high-energy electron statistics at previously unresolved ratios, the thesis explores general facets of the electron thermal runaway process relevant to atmospheric discharges under various conditions of the temperature and gas composition as can be encountered in the wake and formation of discharge channels

    Predicting and Understanding Binding Affinities of Synthetic Anion Receptors

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    Anion receptors are molecules that can recognise and bind anions. They have applications in organocatalysis, anion sensing and the removal of anions from wastewater. Some anion receptors are also able to transport anions across cell membranes and show promise for the treatment of diseases such as cystic fibrosis and cancer. As such, it is of interest to develop computational methods that can reliably predict the physicochemical properties and anion binding affinities of these molecules. However, efforts to computationally model these molecules are hampered by the sheer size of typical receptors, making them too expensive to treat using accurate quantum chemical methods. Whilst efficient approximations such as local-correlation methods have been developed, the broader accuracy of these methods, particularly in their application to ionic non-covalent systems remains unclear. To address this gap, this thesis has carried out an extensive validation of local-correlation methods, and economical density functional theory (DFT) methods for receptors with different binding motifs. Additionally, multiscale models have also been examined with the view to extending the scope of these methods to model very large anion receptors. DFT methods giving good agreement with highly accurate calculations at a fraction of the cost were identified. The use of semiempirical methods combined with DFT in a multiscale model for calculating anion binding affinities lead to unexpectedly large errors with modest savings of computational time, while some "three-fold corrected" methods show promise in reducing the cost of geometry optimisations of large receptors. These validated protocols were subsequently applied to investigate the structure-binding relationships of a wide range of dual-hydrogen bonding receptors. Notably, different receptor motifs were found to have different conformational preferences, which could explain why experimentally, thioureas, thiosquaramides and croconamides show weaker chloride binding affinities than would be expected based on their acidity. The results suggest that pre-organising anion receptors in the conformer that facilitates hydrogen bond formation could be a promising strategy for the development of anion receptors. It is envisaged that these findings will aid in the design and screening of novel anion receptors with increased binding affinity and selectivity

    Development of nonorthogonal wavefunction theories and application to multistate reaction processes.

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    Many prominent areas of technological development rely on exploiting the photochemical response of molecules. An application of particular interest is the control of molecular switches through a combination of different external stimuli. However, despite significant advances in theoretical approaches and numerous cases of successful application of theory, simulating photochemical reactions remains a computational challenge. Theoretical methods for describing excited states can be broadly divided into single-reference response methods and multireference methods. Single reference methods provide reliable semiquantitative results for single excitations. However, these methods cannot describe double-excited states, systems with strongly correlated ground states, or regions of degeneracy on the potential energy surface. The alternative, multireference methods, can provide more accurate results. However, multireference methods require significant technical and chemical insight and become computationally costly as the system size increases. I will discuss my work applying newly developed and well-known methods for understanding multistate processes. I will highlight the limitations and extent of current methodologies that prevent researchers from studying larger and more complex systems. I will also discuss new methodological developments using spin projection, which seeks to overcome several problems of single reference excited state models. I will illustrate the motivation and its performance compared to more established theories. Despite its success, the new method cannot account for ‘multiple correlation mechanisms’. As a result, I will introduce how multiple correlation mechanisms can be exploited to perform nonorthogonal active space decomposition, along with applications and paths for future improvements

    Subsystem-Based Methods for Global and Local Optical Properties

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    The accurate calculation of optical properties in complex systems is a challenging task due to the quantum-mechanical nature of optical processes and the computational demands involved. Subsystem-based methods offer a promising approach to tackle these challenges by partitioning the full system into smaller, computationally more efficient subsystems while maintaining the necessary accuracy. In this thesis, two distinct subsystem-based approaches for calculating optical properties are investigated: local embedding methods and global fragmentation schemes. In the local embedding methods, different polarizable embedding schemes were employed to calculate optical excitations on solvated para-nitroaniline and pentameric formyl thiophene acetic acid. By dissecting the individual interaction effects in a common theoretical framework and developing an extensive computational setup, a one-to-one comparison between polarizable embedding and frozen-density embedding was performed. This comparison provided valuable insights into the importance of separate interaction effects and highlighted the strengths and limitations of each approach. The results revealed the major significance of mutual ground-state polarization in the embedding schemes and the partial importance of dynamical environment effects. Specifically, a strong dependence on the underlying structural geometries could be observed. The global fragmentation schemes allow the calculation of optical properties for various zeolitic imidazolate frameworks (ZIFs). In an extensive study, a general computational protocol was established in order to obtain geometries and calculate refractive indices for ZIF-8, yielding accurate results with reduced computational demands. The implemented fragmentation schemes were then extended to predict optical properties for ZIFs with the same topology but substituted organic linkers. The results indicated the high potential of this approach for the rationalization and prediction of optical properties in empty ZIFs. Moreover, the framework was adapted to incorporate guest molecules into the porous structure of the ZIFs, providing rough estimates of the number of molecules incorporated per pore. Overall, this thesis introduces novel theoretical and computational frameworks for the accurate calculation of optical properties in complex systems. The presented approaches offer a promising direction for future research in the field of optical properties, enabling a deeper understanding of the interactions and their effects on the optical response of various molecules. This paves the way for an improved characterization and design of advanced materials with precise optical functionalities

    Towards Understanding Differential Ion Mobility and its Applications for Analytical and Medicinal Chemistry

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    This PhD thesis, titled “Towards Understanding Differential Ion Mobility Spectrometry and its Applications in Analytical and Medicinal Chemistry,” encompasses a broad effort to understand the principles that underpin differential mobility spectrometry (DMS), and how the DMS technique can be employed within the analytical and medicinal facets of chemistry. Specifically, this work highlights the components of the ion-neutral interaction potential that are pertinent to rationalize an ion’s DMS behaviour and how such information can be modelled using in silico and machine-learning approaches. Understanding the nature of ion-neutral interactions is especially important when DMS experiments are conducted in microsolvating environments (i.e., those in which the carrier gas is seeded with small amounts of a volatile solvent vapour), as components of the interaction potential can be used to predict molecular properties that are routinely screened during drug discovery. In the Chapter 1, we introduce the ion-solvent interactions that are intrinsic to DMS experiments and how microsolvation can impact an ion’s mobility. We specifically emphasize the significance of ion solvent clusters and how the waveform used in DMS separations fosters a dynamic solvation environment. Because field-induced heating is modulated such that an analyte undergoes many cycles of solvent condensation and evaporation at charge-dense regions of the analyte, DMS effectively samples interactions that may resemble the dynamics of solvation within the analyte’s primary solvation shell. In this regard, DMS can be utilized to probe characteristics of a molecule related to its insipient solvation, which, when used in conjunction with quantum-chemical calculations and/or machine learning algorithms, affords accurate predictions of that molecule’s physicochemical properties. In addition to the information regarding an analyte’s physicochemical properties that can be gleaned from DMS measurements in microsolvating environments (Chapter 2), ion microsolvation can help alleviate complications related to field-induced heating. This phenomenon is explored in Chapter 3, where microsolvation was found to stabilize analytes through the formation of localized ion-solvent clusters. In particular, the chapter explores the DMS behaviour of the MP1 peptide, which, when exposed to a microsolvation partner, underwent chemical transformations that reduced the observed charge state of MP1 from [MP1 + 3H]3+ to [MP1 + 2H]2+, and shielded protonated MP1 from fragmentation induced by collisional activation within the DMS cell. This behaviour suggests that microsolvation provides analytes with a solvent “air-bag,” which could play a role in retaining native-like ion configurations during DMS separations that operate well above the low-field limit. Chapter 4, titled Protonation-Induced Chirality Drives Separation by DMS, explores a fascinating phenomenon that can be probed by DMS. In short, chiral species possessing a permanent stereocenter and a prochiral, tertiary amine can form two diastereomers upon protonation during electrospray ionization. The resulting diastereomers exhibit distinct conformations that are resolvable by DMS, constituting the first measurement of this behaviour in the gas phase. Protonation-induced chirality appears to be a general phenomenon, as N-protonation at the tertiary amino moiety of 13 chiral compounds that contained a prochiral, tertiary amine moiety. The analytical utility of DMS is further exemplified in Chapter 5, where DMS and tandem mass spectrometry (MS) were used to distinguish a set of seven cannabinoids. Detection of analytes as argentinated species (i.e., [M + Ag]+ adducts) also led to the discovery that argentination promotes distinct fragmentation patterns for each cannabinoid, enabling their partial distinction by tandem-MS. By adding DMS to the tandem-MS workflow, each cannabinoid was resolved in a pure N2 DMS environment, allowing for accurate assessment of cannabinoid levels within commercial products with excellent accuracy and limits of detection/quantitation. In addition to the analytical utility provided by DMS and the other ion mobility spectrometry (IMS) techniques, IMS-based separation prior to mass spectrometry has become an invaluable tool in the structural elucidation of gas phase ions and in the characterization of complex mixtures. Application of ion mobility to structural studies requires an accurate methodology to bridge theoretical modelling of chemical structure with experimental determination of an ion’s collision cross section (CCS). Chapter 6 discusses the software package MobCal-MPI, which was developed to calculate CCSs efficiently and accurately at arbitrary field strengths via the trajectory method, including those accessed during DMS experiments. While significant progress has been made towards modelling the phenomenon of differential mobility, there are still several properties that have yet to be captured by in silico models. This thesis concludes with Chapter 7, which outlines unresolved issues in the field and suggests several directions in which future research endeavours can be directed

    Exploring Humoral Immune Responses by Mass Spectrometry: Resolving Structures, Interactions, and Clonal Repertoires of Antibodies

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    In his thesis “Exploring Humoral Immune Responses by Mass Spectrometry”, Maurits den Boer uses mass spectrometry to shed new light on antibody responses. Antibodies play a crucial role in the immune protection against threats like bacteria, viruses, and cancers. When valuable antibodies are discovered, they can therefore be reproduced for use as a medicine. A better understanding of their structures, interactions, and repertoires is therefore key to finding novel treatments for many diseases. In the first part of his thesis, Maurits and coworkers used mass spectrometry to study antibody structures and interactions, leading to two major findings. They first uncovered a mechanism by which Staphylococcus aureus bacteria can evade antibody responses, and how this mechanism may be circumvented in future therapies. Second, he redefined the textbook structure of circulating IgM antibodies by showing that they are universally attached to an extra protein. This may have major implications for how these antibodies function, and their use as therapeutics. In a second line of research, Maurits focused on the development of innovative techniques for antibody repertoire analysis and discovery. Together with coworkers, he explored the use of electron-based fragmentation mass spectrometry, developing methods to obtain valuable pieces of antibody sequence information. Finally, he combined multiple layers of mass spectrometry analysis to discover and fully determine the sequence of a malignant patient antibody. Combined, this demonstrates the promise of mass spectrometry as a compelling new approach for therapeutic antibody discovery
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