Evaluating and improving the description of London dispersion interactions in molecular mechanical force fields using the exchange-hole dipole moment model

Molecular simulations are used extensively to model processes in biophysics and biochemistry. These methods approximate the intramolecular and intermolecular interactions of the molecules in the system with a set of simplified mathematical expressions. London dispersion forces account for a significant portion of intermolecular interactions. These interactions play an important role in condensed matter physics and many biophysical phenomena. In this thesis, the eXchange-hole Dipole Moment model (XDM) of density functional theory was used to evaluate the dispersion coefficients in popular molecular mechanical models that are often used for simulations of water, organic molecules, and proteins. The dispersion coefficients derived from XDM calculations were compared to those extracted from molecular mechanical models with parameters from the GAFF, CGenFF, and OPLS force fields. For the generalized force fields, 88 organic molecules were evaluated. The Amber ff14sb, OPLS-AA, and CHARMM36 protein force fields were also evaluated using side chains models. Generally, the force field molecular C₆ dispersion coefficients overestimate the XDM C₆ dispersion coefficients by 50{60%. Despite this, these models predict the solvation energies of these molecules correctly. This trend was attributed to the neglect of higher order dispersion terms. In the empirical parameterization of these force fields, the interaction energy that should arise from these higher order terms will be spuriously added to the C₆ term. In the final chapter, a water model was developed with an improved non-bonded potential that describes repulsive forces more accurately using an exponential Buckingham-type term and includes C₆ and C₈ dispersion terms. High-performance GPU-CUDA and vectorized expressions for this potential were implemented in OpenMM. The model is able to predict the structural, physical, and transport properties of liquid water accurately

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

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 für das Leben unerlässlich. Etwa ein Drittel aller Proteine benötigen Metall-Cofaktoren, um sich korrekt zu falten oder zu funktionieren. Computersimulationen unter Verwendung empirischer Parameter und klassischer Molekülmechanik-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 hängt hauptsächlich von (i) den Parametern ab, die aus immer größeren 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 für die Parametrisierung und das Benchmarking von Kraftfeldern verwendet werden kann. Der Datensatz umfasst Dipeptide der 20 proteinogenen Aminosäuren mit verschiedenen möglichen Seitenketten-Protonierungszuständen, 3 zweiwertige Kationen (Ca2+, Mg2+ und Ba2+) und einen breiten relativen Energiebereich. Wichtige Eigenschaften für die Entwicklung von Kraftfeldern, wie Wechselwirkungsenergien, Partialladungen usw., werden ebenfalls bereitgestellt. Um die Daten verfügbar zu machen, wurde der Datensatz in das NOMAD-Repository hochgeladen und seine Datenstruktur wurde in einer Ontologie formalisiert. Neben einer geeigneten Datenbasis für die Parametrisierung beeinflusst die Physik, die von den Termen des additiven Kraftfeld-Modells abgedeckt wird, dessen Anwendbarkeit. Der zweite Teil dieser Arbeit vergleicht drei populäre 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 berücksichtigen. 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 Fähigkeit, Referenzwechselwirkungsenergien zu reproduzieren und (ii) Molekulardynamik-Simulationen des Calmodulin-N-Lobe. Diese Arbeit demonstriert die Verbesserung polarisierbarer Kraftfelder für Kationen-Protein-Wechselwirkungen durch quantenchemisch gesteuerte Parametrisierung in Kombination mit Molekulardynamiksimulationen in der kondensierten Phase. Während 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 gängigen Molekulardynamik-Pakete verfügbar 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 Fähigkeit, quantenchemische Energien und Molekulardynamiksimulationen des Zinkfingerproteins zu reproduzieren, bewertet. Zusammenfassend lässt sich sagen, dass diese Arbeit einen Schritt in Richtung der Entwicklung von Kraftfeldern der nächsten 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 verfügbares Parametrisierungswerkzeug bereitstellt

Development of polarizable methods for molecular mechanics simulations

University of Minnesota Ph.D. dissertation. January 2014. Major: Chemistry. Advisor: Jiali Gao. 1 computer file (PDF); xiv, 156 pages.This dissertation presents the development of two different types of polarization methods for molecular simulation methods, including Monte Carlo and molecular dynamics (MD) simulations. The first model, which is a polarizable intermolecular potential function (PIPF) method, is based on the point dipole method, where polarization energy is obtained from induced dipole moments and is added as correction to a force field. Hydrogen sulfide (H2S) molecule is studied and parameterized for the PIPF method, and this study displays that the PIPF method reproduces experimental gas-phase dipole moment, molecular polarizability, liquid density, and heat of vaporization very well with a relative error of less than 1.0%. Due to the over-polarization of the model, however, some liquid properties and liquid structure failed to reproduce experimental values, which indicates further improvement is necessary for the PIPF method. The second one is an explicit polarization (X-Pol) method, which is a self-consistent fragment-based electronic structure theory in which molecular orbitals are block-localized within fragments of a cluster, macromolecule, or condensed-phase system. The Lennard-Jone potential function is incorporated into the X-Pol potential in order to express short-range exchange repulsion and long-range dispersion interactions. The X-Pol potential is first developed at the B3LYP hybrid density functional with the 6-31G(d) basis set, and the Lennard-Jones parameters have been optimized on a dataset consisting of 105 hydrogen-bonded bimolecular complexes. It is shown that the X-Pol potential can be optimized to provide a good description of hydrogen bonding interactions; the root mean square deviation (RMSD) of the computed binding energies from CCSD(T)/aug-ccpVDZ results is 0.8 kcal/mol, and that of the calculated hydrogen bond distances is about 0.1 Å from B3LYP/aug-cc-pVDZ optimizations. In addition, the explicit polarization with three-point charge potential (XP3P) model is introduced using the polarized molecular orbital model for water (PMOw). The XP3P model is shown to be suitable for modeling both gas-phase clusters and liquid water, which is demonstrated from simulations of gas-phase water and protonated water clusters, and pure liquid consisting of 267 water molecules in a periodic system. This model is anticipated to be useful for simulating biological system in the condensed phase

Hydration free energies in the FreeSolv database calculated with polarized iterative Hirshfeld charges

Computer simulations of biomolecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in biomolecular systems and are therein described by atomic point charges. In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute’s electron density computed with an implicit solvent model, and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the AM1-BCC and the MBIS atomic charge methods. The latter includes the solvent polarization and presents a root-mean-square error of 2.0 kcal mol–1 for the 613 organic molecules studied. The largest deviation was observed for phosphorus-containing molecules and the molecules with amide, ester and amine functional groups

Molecular Modeling of Ions in Biological Systems

Ions are ubiquitous in biological systems. Metal ions contribute to biological function as counter ions, as triggers to cellular response, and as catalytic cofactors. They play structural roles and are part of the catalytic active site of metalloenzymes. NH4+ ions provide a source of nitrogen for amino acid synthesis in plants and bacteria and help maintaining the acid-base balance in mammals. The cationic side chains of amino acids Lys and Arg contribute to the stability of proteins and protein-DNA complexes through cation–π interactions with the π electrons of aromatic amino acids. Developing molecular models for ion-protein interactions is required to investigate and understand the various biological functions of ions and to complement and interpret experimental data. In this regard, the aims of this thesis are to: 1- Investigate the selectivity of alkali ions toward N, O, and S-containing ligands (a step toward understanding protein selectivity to metal ions). 2- Optimize new semiempirical quantum mechanical models for calcium and magnesium metalloproteins. 3- Study the strength and directionality of cation–π interactions involving inorganic and organic cations interacting with model compounds of aromatic amino acid side chains in both gas phase and aqueous solution. 4- Investigate the selectivity and binding affinity of AmtB and RhCG ammonium transport proteins toward various ions and study the function of amino acids that line the transport pathway of these proteins. Proteins bind metal ions through N, O, and S atoms from the side chains of the amino acids His, Asp, Glu, Ser, Tyr, Asn, Gln, Cys, and Met and from main chain carbonyl and amino groups. NH3, H2O, and H2S are used as minimal models for N, O, and S ligands to investigate the selectivity of alkali metal ions. Polarizable potential models for NH3 and H2S that accurately reproduce the experimental properties of the pure and aqueous liquids are developed. The models are used, together with a previously developed model for water, to study the solvation structures and solvation free energies of the ions in the pure liquids and to investigate the selectivity of alkali ions toward the three ligands. The models yield solvation structures and solvation free energies in good agreement with experiments and show a selectivity of alkali ions toward the three ligands that follows the order H2O > NH3 > H2S. Magnesium and Calcium are two of the most bioavailable metals and are known to play roles in signal transduction and in muscular contraction and are cofactors in many enzymes. Semiempirical models are optimized for the two metals based on the ab initio structures and binding energies of complexes formed between Mg2+ and Ca2+ with ligands that model binding groups in biological and chemical systems. Optimized models are tested on the ab initio properties of ~170 ion-ligand binary and ion-water-ligand ternary complexes. Optimized models of Mg underestimate the binding energies of S-containing complexes but give structures and binding energies of other complexes in agreement with ab initio data. Models for Ca reproduce the ab initio properties of all complexes, including S complexes. Cation–π interactions are common among protein structures and are believed to play key roles in stabilizing proteins and protein complexes with ligands and DNA. Polarizable potential models for the interaction of Rb+, Cs+, Tl+, ammonium, tetramethylammonium, and tetraethylammonium with aromatic amino acid side chains are calibrated based on the ab initio properties of the different cation–π complexes. The models are used to study the binding affinity and complexation geometry of the different pairs in water. Results are showing that cation–π interactions persist in aqueous solutions and are stronger than charge-dipole interactions (such as interactions of Rb+, Cs+, Tl+ with ethanol and acetamide). It is also found that cation–π complexes have geometries in aqueous solution similar to gas phase. In addition, results suggest that cation–π interactions influence the solubility of aromatic compounds in aqueous solutions. Proteins of the Amt/Mep/Rh family —ammonium transporters (Amt) in plants and bacteria, methylamine permease (Mep) in yeast, and rhesus (Rh) blood-group associated glycoproteins in animals— facilitate the permeation of ammonium across cell membranes. Crystal structures of AmtB and RhCG proteins reveal structural differences along the transport pathways. Amt proteins are selective toward NH4+ over Na+ and K+, yet their activity can be inhibited by ions such as Cs+ and Tl+. Polarizable potential models for NH3, NH4+, Na+, K+, Rb+, Cs+, and Tl+ interacting with model compounds to side chains of amino acids that line the transport pathway are optimized. The models are used to calculate the binding affinity of both proteins toward the various ligands and to study the functional roles of amino acids along the transport pathway. Results show that among the various ligands, only Cs+ and Tl+ can compete with NH4+ for binding the two proteins and hence inhibit the protein activity. Results also show that the large hydrophobicity of the pore lumen in RhCG protein destabilizes NH4+ and water molecules in the pore which suggests a net NH3 transport mechanism of the protein

A Physics-Based Intermolecular Potential for Biomolecular Simulation

The grand challenge of biophysics is to use the fundamental laws of physics to predict how biological molecules will move and interact. The atomistic HIPPO (Hydrogen-like Intermolecular Polarizable Potential) force field is meant to address this challenge. It does so by breaking down the intermolecular potential energy function of biomolecular interactions into physically meaningful components (electrostatics, polarization, dispersion, and exchangerepulsion) and using this function to drive molecular dynamics simulations. This force field is able to achieve accuracy within 1 kcal/mol for each component when compared with ab initio Symmetry Adapted Perturbation Theory calculations. HIPPO is capable of this accuracy because it introduces a model electron density on every atom in the molecular system. Since the model is built on first-principles physics, it is transferable from small model systems to bulk phase. In the first test case, the HIPPO force field for water was able to reproduce the experimental density, heat of vaporization and dielectric constant to within 1%. Importantly, HIPPO has been shown to be only 10% more computationally expensive than the widely-used AMOEBA force field, meaning that more accurate simulations of larger biological molecules are well within reach

Developing and validating Fuzzy-Border continuum solvation model with POlarizable Simulations Second order Interaction Model (POSSIM) force field for proteins

The accurate, fast and low cost computational tools are indispensable for studying the structure and dynamics of biological macromolecules in aqueous solution. The goal of this thesis is development and validation of continuum Fuzzy-Border (FB) solvation model to work with the Polarizable Simulations Second-order Interaction Model (POSSIM) force field for proteins developed by Professor G A Kaminski. The implicit FB model has advantages over the popularly used Poisson Boltzmann (PB) solvation model. The FB continuum model attenuates the noise and convergence issues commonly present in numerical treatments of the PB model by employing fixed position cubic grid to compute interactions. It also uses either second or first-order approximation for the solvent polarization which is similar to the second-order explicit polarization applied in POSSIM force field. The FB model was first developed and parameterized with nonpolarizable OPLS-AA force field for small molecules which are not only important in themselves but also building blocks of proteins and peptide side chains. The hydration parameters are fitted to reproduce the experimental or quantum mechanical hydration energies of the molecules with the overall average unsigned error of ca. 0.076kcal/mol. It was further validated by computing the absolute pKa values of 11 substituted phenols with the average unsigned error of 0.41pH units in comparison with the quantum mechanical error of 0.38pH units for this set of molecules. There was a good transferability of hydration parameters and the results were produced only with fitting of the specific atoms to the hydration energy and pKa targets. This clearly demonstrates the numerical and physical basis of the model is good enough and with proper fitting can reproduce the acidity constants for other systems as well. After the successful development of FB model with the fixed charges OPLS-AA force field, it was expanded to permit simulations with Polarizable Simulations Second-order Interaction Model (POSSIM) force field. The hydration parameters of the small molecules representing analogues of protein side chains were fitted to their solvation energies at 298.15K with an average error of ca.0.136kcal/mol. Second, the resulting parameters were used to reproduce the pKa values of the reference systems and the carboxylic (Asp7, Glu10, Glu19, Asp27 and Glu43) and basic residues (Lys13, Lys29, Lys34, His52 and Lys55) of the turkey ovomucoid third domain (OMTKY3) protein. The overall average unsigned error in the pKa values of the acid residues was found to be 0.37pH units and the basic residues was 0.38 pH units compared to 0.58pH units and 0.72 pH units calculated previously using polarizable force field (PFF) and Poisson Boltzmann formalism (PBF) continuum solvation model. These results are produced with fitting of specific atoms of the reference systems and carboxylic and basic residues of the OMTKY3 protein. Since FB model has produced improved pKa shifts of carboxylic residues and basic protein residues in OMTKY3 protein compared to PBF/PFF, it suggests the methodology of first-order FB continuum solvation model works well in such calculations. In this study the importance of explicit treatment of the electrostatic polarization in calculating pKa of both acid and basic protein residues is also emphasized. Moreover, the presented results demonstrate not only the consistently good degree of accuracy of protein pKa calculations with the second-degree POSSIM approximation of the polarizable calculations and the first-order approximation used in the Fuzzy-Border model for the continuum solvation energy, but also a high degree of transferability of both the POSSIM and continuum solvent Fuzzy Border parameters. Therefore, the FB model of solvation combined with the POSSIM force field can be successfully applied to study the protein and protein-ligand systems in water

Modelling covalent modification of cysteine residues in proteins

Cysteine is a unique amino acid because of the chemical reactivity of its thiol (–SH) side chain. For that reason, cysteine serves several essential roles in biochemistry, and its reactivity is critical for the catalytic activity of several biological enzymes. This significance of cysteine residues has been exploited in designing covalent-modifier drugs, particularly kinase inhibitors, which have proven to be successful cancer chemotherapeutic agents in recent years. The reactivity of cysteine thiol group is complex, but a measure of its acidity or pKₐ is a strong determinant of its reactivity towards druggable targets—and can help guide the selection of appropriate druggable targets for covalent modification. Relatively few experimental pKₐ’s of cysteine residues in proteins have been reported, and methods for the computation of cysteine pKₐ’s have received little attention. This thesis presents studies undertaken to investigate the reactivity and covalent modification of cysteine residues in proteins. The introductory chapter lays the groundwork that becomes the basis for subsequent chapters in the thesis. This chapter provides a general introduction to covalent modification and the techniques used to investigate the biophysical properties of residue-specific nucleophilic targets for covalent modification. The first two chapters following the introduction are focused on predictive pKa assessments and validation studies on different computational methods in accurately calculating experimental cysteine pKₐ’s. In the latter chapters, advanced computational and multiscale methods are adopted to investigate the reactivity of druggable cysteines in protein kinases commonly implicated in diverse clinical indications, as well as model all the steps in the covalent modification mechanism of a kinase target. The thesis concludes by providing a concise summary of the research findings and future directions stemming from the work. The fundamental studies presented herein expand our current knowledge of modelling the covalent inhibition of druggable cysteines in enzyme targets and could go a long way to inform drug design and discovery

Towards Improving The Accuracy of Implicit Solvent Models and Understanding Electrostatic Catalysis in Complex Solvent Environment

This thesis develops improved protocols for studying reactions in solution and uses them to explore the possibility of harnessing complex non-standard solvent environments to catalyse chemical reactions. The thesis covers different but related topics: Improving the accuracy of implicit solvent models. Implicit solvent models are simple cost-effective strategies for modelling solvent as a polarizable continuum. However, the accuracy of this approach can be quite variable. Herein, we examine approaches to improving their accuracy through cavity scaling, the choice of theoretical level and the inclusion of explicit solvent molecules. For SMD, we show that the best performance is achieved when cavity scaling is not employed, while for PCM we present a series of electrostatic scale factors that are radii, solvent and ion type dependent. For both families of method, we also highlight the importance choosing an appropriate level of theory, and identify when explicit solvent molecules are required.. Modelling electrostatic catalysis in complex solvent environment. Recent nanoscale experiments have shown that electric fields are capable of catalysing and controlling chemical reactions, but experimental platforms for scaling these effects remain elusive. Herein, two different approaches to addressing this challenge are explored. The first is using the internal electric field of ordered solvents and ionic liquids, the second is using the electric fields that form naturally at the gas-water interface. A multi-scale modelling approach was developed using polarizable force field based molecular dynamic simulation, post-HF, DFT and semi-empirical quantum chemical calculations. We showed that after exposure to an external electric field, ensembles of solvent or ionic liquid molecules become ordered and this ordering can generate an internal electric field, which persists even after the external potential is removed. Experimental collaborators subsequently detected this field as an open-circuit potential that is strong and long-lived. Computationally we showed that this field is enough to lower reaction barriers by as much as 20 kcal mol-1, and we also developed a predictive structure-reactivity model to choose ionic liquids that optimize this field. In the second approach, we harnessed the electric fields of the gas-water interface. A collaborator showed that in the presence of static, inert gas bubbles, the oxidation potential of HO anion/HO radical was dramatically lowered (by more than 0.5V), much more than any subtle concentration effects predicted by the Nernst equation. Further experiments showed that a high unbalanced concentration of HO- ions (as much as 5M) accumulate at the interface. Our multi-scale modelling calculations showed that this reduction in potential was due to the mutual repulsion of the HO- ions and as little as 1M unbalanced excess was enough to explain the experimental results. The work raises opportunities in reducing the cost of electrochemical processes, and points to electrostatic effects contributing to the well-known catalytic effects of "on water" reactions. Works in this thesis are expected to be useful in the future studies of solution-phase pKa, redox potential, electrostatic catalysis and ionic liquids-based electrochemical devices