DNA microarrays are chip-based, analysis tools which can perform hundreds of thousands of parallel assays to determine the identity of genes or gene expression levels present in a sample. They have been identified as a key technology in genomic sciences and emergent medical techniques; however, despite their abundant use in research laboratories, microarrays have not been used in the clinical setting to the fullest potential due to the difficulty of obtaining reproducible results. Microarrays work on the principle of DNA hybridization, and can only be as accurate as this process is robust. Fundamental, molecular-level understanding of hybridization on surfaces is needed to further refine these devices.This work shows how orientation of DNA probes with respect to the surface affects the thermodynamics and stability of hybridization. Ideal surface hybridization (a DNA duplex bound to the surface on one end) is compared to more realistic conditions such as interaction between DNA and the surface in multiple locations. This research also describes the effect of mismatch location and number of mismatches on a single target strand. The results clarify key details of the biophysics involved in microarray performance and this knowledge can be used to improve next-generation devices. The disparity between surface and bulk hybridization behavior is examined here in molecular level detail that is not currently possible with experimental techniques.

Through their motion, proteins perform essential functions in the living cell. Although we cannot observe protein motion directly, over 68,000 crystal structures are freely available from the Protein Data Bank. Computational protein rigidity analysis systems leverage this data, building a mechanical model from atoms and pairwise interactions determined from a static 3D structure. The rigid and flexible components of the model are then calculated with a pebble game algorithm, predicting a protein's flexibility with much more computational efficiency than physical simulation. In prior work with rigidity analysis systems, the available modeling options were hard-coded, and evaluation was limited to case studies. The focus of this thesis is improving accuracy and robustness of rigidity analysis systems. The first contribution is in new approaches to mechanical modeling of noncovalent interactions, namely hydrogen bonds and hydrophobic interactions. Unlike covalent bonds, the behavior of these interactions varies with their energies. I systematically investigate energy-refined modeling of these interactions. Included in this is a method to assign a score to a predicted cluster decomposition, adapted from the B-cubed score from information retrieval. Another contribution of this thesis is in new approaches to measuring the robustness of rigidity analysis results. The protein's fold is held in place by weak, noncovalent interactions, known to break and form during natural fluctuations. Rigidity analysis has been conventionally performed on only a single snapshot, rather than on an entire trajectory, and no information was made available on the sensitivity of the clusters to variations in the interaction network. I propose an approach to measure the robustness of rigidity results, by studying how detrimental the loss of a single interaction may be to a cluster's rigidity. The accompanying study shows that, when present, highly critical interactions are concentrated around the active site, indicating that nature has designed a very versatile system for transitioning between unique conformations. Over the course of this thesis, we develop the KINARI library for experimenting with extensions to rigidity analysis. The modular design of the software allows for easy extensions and tool development. A specific feature is the inclusion of several modeling options, allowing more freedom in exploring biological hypotheses and future benchmarking experiments.

In 2020, cancer impacted an estimated 1.8 million people and result in over 600,000 deaths in the United States. Some cancer treatments options are limited due to drug resistance, requiring additional drug development to improve patient survival rates. It is necessary to continuously develop new therapeutic approaches and identify novel targets, as cancer is ever-growing and adapting. Experimental research strategies have limitations when exploring how to target certain protein classes, including membrane-embedded or protein-protein bound, due to the complexity of their environments. These two domains of research are experimentally challenging to explore, and in silico research practices provide insight that would otherwise take years to study. Computer-aided drug design (CADD) routes can support the areas of drug discovery that are considered difficult to explore with experimental techniques. In this work, we provide research practices that are easily adaptable and translatable to other difficult protein targets and interfaces. First, we identified the morphological impact of a single-site mutation in the G-protein coupled receptor (GPCR), OR2T7, which had been identified as a novel prognostic marker for glioblastoma. Next, we explored the blockbuster target, Programmed Cell Death Protein 1 – (PD-1) and the agonistic vs antagonistic response that can be exploited for Non-Small Cell Lung Cancer (NSCLC) therapeutic development. Last, we explored the sphingolipid transport protein, Spns2, which has been demonstrated to be important in regulating the metastatic cancer enabling microenvironment. This work utilized molecular dynamics simulations (MDS) to explore the protein structure-function relationship for each protein of interest, allowing for the exploration of biophysical properties and protein dynamics. We identified that the D125V mutation in OR2T7 likely influences activation of the MAPK pathway by impacting G-protein binding via reducing the helical plasticity in the TM6 and TM7 regions. PD-1 was identified to have a domain near the PD-L1 binding interface that increases β-sheet stability and increases residue-residue distances with the membrane-proximal region within PD-1, thus leading to an active conformation. Lastly, Spns2 was identified to follow a rocker-switch transport model and provided preliminary insight into sphingolipid-Spns2 channel binding, interacting with residues Thr216, Arg227, and Met230, as well as highlighting the role of Arg119 in a salt-bridge network of interactions essential in substrate translocation. Collectively, this work illustrates the advantages of computational workflows in the drug discovery process and provides a framework that can be applied for additional GCPRs, transport proteins, or protein-protein interfaces to enhance and accelerate the CADD research. / Doctor of Philosophy / Cancer is an ever-evolving disease that requires continuous development of new treatment options. Experimental research strategies can be timely, expensive, or lack atomistic insight into drug development processes. Computer-aided drug design (CADD) routes provide research strategies to support areas of drug discovery that can be difficult to explore with experimental techniques. Membrane-bound proteins and protein-protein interfaces are two domains of research that are typically difficult to explore, and computational research practices provide insight that would otherwise take years to study. In this work, we provide research practices that are easily adaptable and translatable to other difficult protein targets and interfaces. First, we identified the impact of a single-site mutation in the G-protein coupled receptor (GPCR), OR2T7, which had been identified as a novel prognostic marker for glioblastoma. Next, we explored the blockbuster target, Programmed Cell Death Protein 1 – (PD-1) and active vs inactive states that can be exploited for Non-Small Cell Lung Cancer (NSCLC) therapeutic development. Last, we explored the sphingolipid transport protein, Spns2, which has been demonstrated to be important in metastatic cancer growth. This work utilized molecular dynamics simulations (MDS) to explore the protein structure-function relationship for each protein of interest, allowing for the exploration of biophysical properties and protein movement. We identified that the D125V mutation in OR2T7 likely influences activation of the MAPK pathway, which supports multiple cancer-regulation pathways, by impacting G-protein binding via reducing the structural flexibility. PD-1 was identified to have a domain near the PD-L1 binding interface that increases structural stability, thus leading to an upregulation of cancer survival pathways. Lastly, Spns2 analysis provided insight into movement involved in sphingolipid transport, provided preliminary insight into sphingolipid-Spns2 binding, as well as highlighting the role of Arg119 in a network of interactions essential in substrate translocation. Collectively, this work highlights the usefulness of computational workflows in the drug discovery process and provides a framework that can be utilized for additional GPCRs, transport proteins, or protein-protein interfaces to enhance and accelerate the CADD research.

Introduction Understanding the intricate workings of biological systems at the molecular level is crucial for unraveling the complex mechanisms that underlie Life itself. Proteins and RNA, two essential components of cellular structure and processes, exhibit remarkable structural and functional diversity. Traditional experimental techniques have provided valuable insights into their behaviors; however, they often fall short in capturing the dynamic nature of these biomolecules. Over the past few decades, multiscale molecular dynamics (MD) simulations have emerged as a powerful computational tool to bridge this gap, enabling the study of biological systems at an atomistic resolution. My Ph.D. thesis aims to delve into the realm of multiscale MD simulations to unravel the dynamic landscape of proteins and RNA, shedding light on their folding mechanisms, conformational transitions, and functional dynamics. By integrating the principles of classical, atomistic MD and more advanced modelling techniques, such as coarse-grained models, this research endeavors to highlight and possibly propose ways to overcome the limitations of conventional simulations and offer a comprehensive understanding of the complex dynamics governing these biomolecules. Chapter 1 The first chapter of the thesis provides a comprehensive overview of the theoretical foundations and practical aspects of classical all-atom molecular dynamics (MD) simulations. It begins with a schematic derivation of the all-atom MD equations, emphasizing the integration of electronic degrees of freedom and their contribution to the potential energy. The chapter then focuses on the energy terms utilized in all-atom force fields, highlighting their significance in accurately representing the interactions among atoms. The discussion extends to the theoretical underpinnings of thermostats, drawing from statistical mechanics principles to elucidate their role in controlling temperature during simulations. Furthermore, the use of periodic boundary conditions and the particle mesh Ewald method are discussed, highlighting their importance in simulating (or at least mimicking) large systems and accounting for long-range electrostatic interactions. By delving into these foundational concepts and techniques, this chapter establishes the groundwork for subsequent investigations in multiscale molecular dynamics simulations. Chapter 2 The second chapter focuses on the characterization of the conformational space of the Shwachman-Bodian-Diamond syndrome (SBDS) protein, a critical component involved in cellular processes. Specifically, this research employs all-atom molecular dynamics simulations to study the wild-type SBDS and 12 missense mutations of clinical relevance. The simulations are initiated with two distinct NMR structures representing an open and a closed conformation, respectively, to capture a wide range of conformational variability. Each starting conformation is simulated for the wild type and all 12 mutations, resulting in a total of 26 simulations with a cumulative sampling time of 13$\mu s$. The analyses of these extensive simulations provide valuable insights into the effects of missense mutations on SBDS dynamics and function. The investigation reveals a common trend among all mutations, characterized by increased residue fluctuations in the hinge I-II region. This observation suggests potential interference with the conformational changes involving the reorientation of domains II-III and the detachment of eIF6 from the 60S subunit. Furthermore, the study highlights the structural similarity of the K67E mutation to the wild type, despite a lower exposed positive charge. This finding, supported by free energy analysis, suggests that the pathological mechanism associated with this mutation may be linked more closely to a decrease in binding affinity rather than structural deformation. Additionally, the simulations of R19Q and C84R exhibit lower binding affinity specifically in closed trajectories, corroborating experimental observations regarding their potential impact on RNA binding. Moreover, K151 and R218 reveal importance in stabilizing the conformation assumed by SBDS upon binding with the 60S subunit. Notably, the dynamics-based clustering and free energy analysis highlight the distinct behavior of the K151N mutation, both in open and closed simulations, suggesting that compromised dynamics may hinder the protein's ability to stabilize a functional conformation for effective cooperation with EFL1. Collectively, this chapter contributes to our understanding of SBDS dynamics and the effects of missense mutations, paving the way for further investigations into the molecular mechanisms underlying Shwachman-Diamond syndrome. Chapter 3 The third chapter explores the principles and applications of coarse-graining (CG) and multiscale modeling techniques in computational biophysics. After providing a theoretical foundation for CG, the chapter presents several examples of CG models employed in the research. This includes the implementation of Elastic Network Models (ENMs), which capture the essential dynamics of proteins by simplifying their atomistic representation. Additionally, the oxRNA model, designed specifically for RNA molecules, and the CANVAS multi-resolution model for proteins are introduced, showcasing their ability to capture the key features of the molecular system while significantly reducing computational complexity. Furthermore, the chapter delves into the theory of implicit solvation, as it plays a crucial role in some of the aforementioned models. Implicit solvation methods enable the efficient treatment of solvent effects without explicitly simulating water molecules, thereby reducing computational costs. Notably, the chapter sets the stage for the subsequent chapter by introducing the concept of implicit solvation, as chapter 5 presents a novel technique for implicit solvation based on Artificial Neural Networks. By providing an in-depth exploration of CG and multiscale modeling techniques, along with their associated solvation models, this chapter equips readers with the necessary tools to understand the methods developed to effectively study large-scale biological systems with reduced computational demands. Chapter 4 The fourth chapter, extracted from the paper \textit{``In search of a dynamical vocabulary: a pipeline to construct a basis of shared traits in large-scale motions of proteins'', published in Applied Sciences, introduces a structure-based pipeline for capturing the main features of large-scale protein motions. The pipeline aims to provide a general description of protein motion, not only for those proteins whose structures are used as input but also for structurally similar proteins that were not included in the initial dataset. To demonstrate the effectiveness of the pipeline, the research applied it to a set of 116 chymotrypsin-related proteases. By employing the presented workflow, the study successfully captured dynamical features of proteins that are structurally similar to, but not part of, the input structures used to build the basis set of the dynamical space of the proteins. This allows a comprehensive understanding of the shared traits in large-scale motions, facilitating the characterization of protein dynamics beyond the limitations of specific protein structures. Overall, this chapter highlights the development and application of a structure-based pipeline that enables the extraction of essential dynamic features from proteins, contributing to the establishment of a comprehensive dynamical vocabulary in the study of protein motion. Chapter 5 The fifth chapter introduces a novel method for implicit solvation of biomolecules in molecular dynamics simulations, leveraging the power of artificial neural networks (ANN). The chapter begins by formally describing the methodology, starting with the architecture of ANN. The latter are trained to predict the free energy of solvation for each atom in the system at every time step of the simulation. The inputs to the network are derived from special symmetry functions, which capture the local environment of each atom. The output of the ANN provides the necessary information to extract forces, which are subsequently integrated into the equations of motion. Moreover, the chapter presents the algorithmic implementation of the method into the LAMMPS molecular dynamics software package, enabling its practical application to a wide range of molecular systems. To assess the performance and accuracy of the method, three test cases are examined: the alanine dipeptide, the icosalanine (a polymer composed of 20 alanine amino acids), and a small RNA fragment containing approximately 1000 atoms. The results of the tests indicate that the method performs well in describing general macroscopic features of the molecules. However, it exhibits limitations in accurately predicting high-resolution properties, such as specific minima in the Ramachandran space of dihedral angles for the alanine dipeptide or the precise hydrogen bond network among the bases in the RNA fragment. Despite these limitations, the method demonstrates promising computational performance and scalability, making it a valuable tool for efficient implicit solvation simulations. Overall, this chapter presents a new approach to implicit solvation using artificial neural networks, showcasing its potential for accurately describing general molecular features while highlighting areas for further refinement. The method holds promise for enabling large-scale simulations with reduced computational costs, thereby expanding the scope of molecular dynamics studies. Chapter 6 The sixth chapter presents the results of a comprehensive and multi-resolution molecular dynamics study focusing on a virion particle of the Chlorotic Cowpea Mottle Virus (CCMV) and its constituent molecules. The chapter encompasses various MD simulations, each offering unique insights into the dynamics and behavior of the viral components. The first set of simulations investigates the coarse-grained folding and relaxation of the RNA2 viral single-stranded RNA (ssRNA) fragment. The simulations start with a free, rod-like polymer chain configuration, and the subsequent folding and relaxation processes are examined. Furthermore, non-equilibrium squeezing of the folded RNA structure into a spherical region of space, mimicking the confinement within the capsid, is explored. These simulations are aimed at shedding light on the structural and dynamic aspects of viral RNA during the self-assembly process of the virus. The chapter then proceeds to present multi-resolution equilibrium simulations of a trimer, which consists of three capsid molecules. The trimer is studied using five different approaches: all-atom representation in explicit solvent, all-atom representation in implicit solvent employing Debye-Huckel electrostatics, and three different applications of the CANVAS model. The latter is a model multi-resolution for proteins, developed in my group, which combines atomistic force fields together with an elastic network model to describe protein dynamics with manually deployed levels of resolution. These simulations allow for a comprehensive analysis of the trimer's dynamics and interactions, highlighting the influence of different models on the observed behavior, as well as testing the applicability of the CANVAS model to this kind of system. Additionally, all-atom simulations are performed for both the capsid and the virion, which includes the RNA2 fragment within the capsid, in explicit solvent. These simulations provide detailed information about the structural and dynamic properties of the viral capsid and the interactions between the capsid and the encapsulated RNA2 fragment. By leveraging multi-resolution approaches and conducting various simulations at different levels of detail, this chapter offers a comprehensive understanding of the RNA dynamics, folding, relaxation, and interactions within the virion particle of CCMV. These findings contribute to our knowledge of viral assembly and the behavior of viral constituents, facilitating further insights into the functioning and stability of viral systems.

The emergence of scientific applications embedded with multiple modes of parallelism has made heterogeneous computing systems indispensable in high performance computing. The popularity of such systems is evident from the fact that three out of the top five fastest supercomputers in the world employ heterogeneous computing, i.e., they use dissimilar computational units. A closer look at the performance of these supercomputers reveals that they achieve only around 50% of their theoretical peak performance. This suggests that applications that were tuned for erstwhile homogeneous computing may not be efficient for today's heterogeneous computing and hence, novel optimization strategies are required to be exercised. However, optimizing an application for heterogeneous computing systems is extremely challenging, primarily due to the architectural differences in computational units in such systems. This thesis intends to act as a cookbook for optimizing applications on heterogeneous computing systems that employ graphics processing units (GPUs) as the preferred mode of accelerators. We discuss optimization strategies for multicore CPUs as well as for the two popular GPU platforms, i.e., GPUs from AMD and NVIDIA. Optimization strategies for NVIDIA GPUs have been well studied but when applied on AMD GPUs, they fail to measurably improve performance because of the differences in underlying architecture. To the best of our knowledge, this research is the first to propose optimization strategies for AMD GPUs. Even on NVIDIA GPUs, there exists a lesser known but an extremely severe performance pitfall called partition camping, which can affect application performance by up to seven-fold. To facilitate the detection of this phenomenon, we have developed a performance prediction model that analyzes and characterizes the effect of partition camping in GPU applications. We have used a large-scale, molecular modeling application to validate and verify all the optimization strategies. Our results illustrate that if appropriately optimized, AMD and NVIDIA GPUs can provide 371-fold and 328-fold improvement, respectively, over a hand-tuned, SSE-optimized serial implementation. / Master of Science

The forces of interaction were measured between the siderophore azotobactin and the minerals goethite (FeOOH) and diaspore (AlOOH) in solution using force microscopy. Azotobactin was covalently linked to a hydrazide terminated atomic force microscope tip using a standard protein coupling technique. Upon contact with each mineral surface, the adhesion force between azotobactin and goethite was two to three times the value observed for the isostructural Al-equivalent diaspore. The affinity for the solid iron oxide surface reflected in the force measurements correlates with the specificity of azotobactin for aqueous ferric iron. Further, the adhesion force between azotobactin and goethite significantly decreases when small amounts of soluble iron are added to the system suggesting a significant specific interaction between the azotobactin and the mineral surface. Changes in the force signature with pH and ionic strength were fairly predictable when considering mineral solubility, the charge character of the mineral surfaces, the molecular structure of azotobactin, and the intervening solution. Molecular and quantum mechanical calculations which were completed to further investigate the interaction between azotobactin and iron/aluminum oxide surfaces, and to more fully understand the force measurements, also showed an increased force affinity for Fe over Al. Ab initio calculations on siderophore fragment analogs suggest the iron affinity can be attributed to increased electron density associated with the Fe-O bond compared to the Al-O bond; an observation that correlates with iron's larger electronegativity compared to aluminum. Attachment of the ligand to each surface was directed by steric forces within the molecule and coulombic interactions between the siderophore oxygens and the metals in the mineral. Chelating ligand pairs coordinated with neighboring metal atoms in a bidentate, binuclear geometry. Upon simulated retraction of azotobactin from each surface, the Fe-O(siderophore) bonds persisted into a higher force regime than Al-O(siderophore) bonds, and surface metals were removed from both minerals. Extrapolation of the model to more realistic hydrated conditions using a PCM model in the quantum mechanical calculations and water clusters in the molecular mechanical model demonstrated that the presence of water energetically favors and enhances metal extraction, making this a real possibility in a natural system. / Ph. D.

The research detailed herein has been conducted in two different areas. The first research goal was to develop and study a supramolecular system coupling two light absorber units to a central metal site capable of collecting two electrons; this has been accomplished. The complex {[(bpy)2Ru(dpp)]2RhCl2}(PF6)5 was synthesized and characterized using electrochemistry and electronic absorption spectroscopy. The electrochemical properties of {[(bpy)2Ru(dpp)]2RhCl₂}(PF6)5 were probed with cyclic voltammetry and bulk electrolysis studies to investigate the behavior of the system upon two-electron reduction of the rhodium metal center. Bulk electrolysis showed that the rhodium center underwent two-electron reduction. A water modulation of product distribution for the bulk electrolysis studies was found, and the nature of this process was studied. In the presence of water, two-electron reduction of the rhodium metal center afforded a RhI complex that had lost two chloride ligands: {[(bpy)2Ru(dpp)]2RhI}+5. In the absence of sufficient water, two-electron reduction yields a breakdown of the trimetallic resulting in [(bpy)2Ru(dpp)]+2 and {[(bpy)2Ru(dpp)]RhICl₂}+. The second research goal was to develop integrated molecular modeling, synthesis, and characterization laboratory experiments, to incorporate into the undergraduate inorganic laboratory curriculum, and to evaluate and modify this curricular approach. This was accomplished with organometallic [Mo(CO)4(N-N)] complexes, where N-N is a bidentate nitrogen donor ligand. [Mo(CO)4(N-N)] complexes were selected because they were amendable to molecular modeling and could be synthesized via two synthetic routes with reasonable yields, low reaction times, and were air stable. Many of these complexes are new, so a series of [Mo(CO)4(N-N)] complexes were synthesized by either thermal or photochemical substitution reactions. The systems were characterized using cyclic voltammetry, electronic absorption spectroscopy, and ¹H NMR spectroscopy. Molecular modeling was performed on the series of [Mo(CO)4(N-N)] complexes using the CAChe software from Oxford Molecular, Ltd. These calculations typically provided reasonable structures, orbital locations, and relative orbital energies for the [Mo(CO)4(N-N)] systems. Correlations between the computational and experimental data were established. The electronic absorption spectral MLCT frequency versus calculated HOMO-LUMO energy gap, Epa versus calculated HOMO energy, E1/2red versus calculated LUMO energy, and 1H NMR chemical shift for [Mo(CO)4(1,10-phen)] and the substituted [Mo(CO)4(1,10-phen)] complexes versus ZINDO calculated partial charge were compared, where 1,10-phen = 1,10-phenanthroline. From the analyses of physical versus computational data, it was concluded that molecular modeling results are useful in predicting physical data for these complexes. The integrated molecular modeling, synthesis, and characterization experiment was developed and incorporated into the undergraduate inorganic chemistry laboratory. In both 1998 and 1999, a qualitative evaluation of student response was completed. This was done using a recorded interview technique; interviews were subsequently transcribed and rendered to extract themes. This interview style was an effective evaluation technique for this project, providing the detailed comments and student feedback that were desired. These interviews showed that the majority of the students both enjoyed this experiment and felt that the exposure to molecular modeling was worthwhile within this type of integrated lab forum. The students felt this experiment aided in their understanding of the orbital properties of inorganic systems. Student comments and suggestions facilitated modifications for future offerings in 1999 and 2000. Continued evaluation and expansion of this curricular approach are in progress. / Ph. D.

Interactions between (bio)molecules, ions and solid surfaces play crucial role in many biological processes as well as in many scientific applications and understanding of this phenomenon on molecular level is a challenging task for today science. Computer simulations can provide detailed view on atomic level if carefully prepared and evaluated models are used. In this thesis, interactions of several types of (bio)molecules with inorganic surfaces are studied by classical and ab initio molecular dynamics. Chemisorbed biomolecules, namely DNA and oligopeptide, covalently attached to graphene and mercury surface, respectively, were studied to make link with DNA chip design and experimental label-free electrochemical measurements, respectively. Quartz (101) surface model applicable to wide range of pH conditions was developed and evaluated against experimental X-ray data. Physisorption of the nucleobases on quartz (101) surface and oxalate dianion on rutile (110) was examined and discussed.

Doctor of Philosophy / Department of Biochemistry and Molecular Biophysics / Jianhan Chen / Intrinsically disordered proteins (IDPs) are functional proteins that lack stable tertiary structures under physiological conditions. IDPs are key components of regulatory networks that dictate various aspects of cellular decision-making, and are over-represented in major disease pathways. For example, about 30% of eukaryotic proteins contain intrinsic disordered regions, and over 70% of cancer-associated proteins have been identified as IDPs. The highly heterogeneous nature of IDPs has presented significant challenge for experimental characterization using NMR, X-ray crystallography, or FRET. These challenges represent a unique opportunity for molecular mod- eling to make critical contributions. In this study, computer simulations at multiple scales were utilized to characterize the structural properties of unbound IDPs as well as to obtain a mechanistic understanding of IDP interactions. These studies of IDPs also reveal significant limitations in the current simulation methodology. In particular, successful simulations of biomolecules not only require accurate molecular models, but also depend on the ability to sufficiently sample the com- plex conformational space. By designing a realistic yet computationally tractable coarse-grained protein model, we demonstrated that the popular temperature replica exchange enhanced sampling is ineffective in driving faster reversible folding transitions for proteins. The second original contribution of this dissertation is the development of novel simulation methods for enhanced sampling of protein conformations, specifically, replica exchange with guided-annealing (RE-GA) method and multiscale enhanced sampling (MSES) method. We expect these methods to be highly useful in generating converged conformational ensembles.

<p>Multifunctional enzymes have alternative functions or activities, known as “moonlighting” or “promiscuous”, which are often hidden behind a native enzyme activity and therefore only visible under special environmental conditions. In this thesis, the active-site of Pseudozyma (formerly Candida) antarctica lipase B was explored for a promiscuous conjugate addition activity. Pseudozyma antarctica lipase B is a lipase industrially used for hydrolysis or transacylation reactions. This enzyme contains a catalytic triad, Ser105-His224-Asp187, where a nucleophilic attack from Ser105 on carboxylic acid/ester substrates cause the formation of an acyl enzyme. For conjugate addition activity in Pseudozyma antarctica lipase B, replacement of Ser105 was assumed necessary to prevent competing hemiacetal formation. However, experiments revealed conjugate addition activity in both wild-type enzyme and the Ser105Ala variant. Enzyme-catalyzed conjugate additions were performed by adding sec-amine, thiols or 1,3-dicarbonyl compounds to various α,β-unsaturated carbonyl compounds in both water or organic solvent. The reactions followed Michaelis-Menten kinetics and the native ping pong bi bi reaction mechanism of Pseudozyma antarctica lipase B for hydrolysis/transacylation was rerouted to a novel ordered bi uni reaction mechanism for conjugate addition (Paper I, II, III). The lipase hydrolysis activity was suppressed more than 1000 times by the replacement of the nucleophilic Ser105 to Ala (Paper III).</p>