Targeting Drug Resistance In HCV NS3/4A Protease: Mechanisms And Inhibitor Design Strategies

The Hepatitis C virus (HCV) NS3/4A protease inhibitors (PIs) have become a mainstay of newer all-oral combination therapies. Despite improvements in potency of this inhibitor class, drug resistance remains a problem with the rapid emergence of resistance-associated substitutions (RASs). In this thesis I elucidate the molecular mechanisms of drug resistance for PIs against a resistant variant and apply insights toward the design of inhibitors with improved resistance profiles using structural, biochemical and computational techniques. Newer generation PIs retain high potency against most single substitutions in the protease active site by stacking on the catalytic triad. I investigated the molecular mechanisms of resistance against the Y56H/D168A variant. My analysis revealed that the Y56H substitution disrupts these inhibitors’ favorable stacking interactions with the catalytic residue His57. To further address the impact of drug resistance, I designed new inhibitors that minimize contact with known drug resistance residues that are unessential in substrate recognition. The initially designed inhibitors exhibited flatter resistance profiles than the newer generation PIs but lost potency against the D168A variant. Finally, I designed inhibitors to extend into the substrate envelope (SE) and successfully regained potency against RAS variants maintaining a flat profile. These inhibitors both pack well in the enzyme and fit within the SE. Together these studies elucidate the molecular mechanisms of PI resistance and highlight the importance of substrate recognition in inhibitor design. The insights from this thesis provide strategies toward the development of diverse NS3/4A PIs that may one day lead to the eradication of HCV

Computational Modeling of Protein Kinases: Molecular Basis for Inhibition and Catalysis

Protein kinases catalyze protein phosphorylation reactions, i.e. the transfer of the γ-phosphoryl group of ATP to tyrosine, serine and threonine residues of protein substrates. This phosphorylation plays an important role in regulating various cellular processes. Deregulation of many kinases is directly linked to cancer development and the protein kinase family is one of the most important targets in current cancer therapy regimens. This relevance to disease has stimulated intensive efforts in the biomedical research community to understand their catalytic mechanisms, discern their cellular functions, and discover inhibitors. With the advantage of being able to simultaneously define structural as well as dynamic properties for complex systems, computational studies at the atomic level has been recognized as a powerful complement to experimental studies. In this work, we employed a suite of computational and molecular simulation methods to (1) explore the catalytic mechanism of a particular protein kinase, namely, epidermal growth factor receptor (EGFR); (2) study the interaction between EGFR and one of its inhibitors, namely erlotinib (Tarceva); (3) discern the effects of molecular alterations (somatic mutations) of EGFR to differential downstream signaling response; and (4) model the interactions of a novel class of kinase inhibitors with a common ruthenium based organometallic scaffold with different protein kinases. Our simulations established some important molecular rules in operation in the contexts of inhibitor-binding, substrate-recognition, catalytic landscapes, and signaling in the EGFR tyrosine kinase. Our results also shed insights on the mechanisms of inhibition and phosphorylation commonly employed by many kinases

Characterizing Signal Transduction Networks and Biological Responses Using Computer Simulations and Machine Learning

The use of computer simulations in biology is often limited due to the lack of experimentally measured parameters. In these scenarios, parameter exploration can be used to probe biological systems and refine understanding of biological mechanisms. For systems with few unknown parameters, parameter sweeps that concurrently vary all unknown parameters are tractable. In complex systems with many unknown parameters, supervised machine learning algorithms can be used to discover parameters leading to targeted system responses. In this thesis, we study three biological problems in which we use parameter exploration methods to gain mechanistic insights. We first explore the role of altered metabolism in cancer cells that reside in heterogeneous tumor microenvironments. We use a multiscale, hybrid cellular automaton model to evaluate tumor progression while varying malignant cell traits using a systematic parameter sweep. The results reveal distinct growth regimes associated with varied malignant cell traits. We then study kinetic mechanisms governing fixed-topology signal transduction networks and use evolutionary algorithms to discover kinetic parameters that produce specified network responses. We analyze the growth-response network in Arabidopsis with this supervised machine learning approach. This allows us to identify constraints on kinetic parameters that govern the observed responses. The evolved parameters are used to calculate the responses of individual network components, which are used to generate hypotheses that can be tested in vivo to help determine the network topology. We finally apply a similar approach to redesign signal transduction networks. We demonstrate that the T cell receptor network and an oscillator network show remarkable flexibility in generating altered responses to input, and we further use a nonlinear clustering method to identify design criteria for the underlying kinetic parameters. For each project, observations produced from in silico simulations lead to the formation of hypotheses that are experimentally testable

Cortical Factor Feedback Model for Cellular Locomotion and Cytofission

Eukaryotic cells can move spontaneously without being guided by external cues. For such spontaneous movements, a variety of different modes have been observed, including the amoeboid-like locomotion with protrusion of multiple pseudopods, the keratocyte-like locomotion with a widely spread lamellipodium, cell division with two daughter cells crawling in opposite directions, and fragmentations of a cell to multiple pieces. Mutagenesis studies have revealed that cells exhibit these modes depending on which genes are deficient, suggesting that seemingly different modes are the manifestation of a common mechanism to regulate cell motion. In this paper, we propose a hypothesis that the positive feedback mechanism working through the inhomogeneous distribution of regulatory proteins underlies this variety of cell locomotion and cytofission. In this hypothesis, a set of regulatory proteins, which we call cortical factors, suppress actin polymerization. These suppressing factors are diluted at the extending front and accumulated at the retracting rear of cell, which establishes a cellular polarity and enhances the cell motility, leading to the further accumulation of cortical factors at the rear. Stochastic simulation of cell movement shows that the positive feedback mechanism of cortical factors stabilizes or destabilizes modes of movement and determines the cell migration pattern. The model predicts that the pattern is selected by changing the rate of formation of the actin-filament network or the threshold to initiate the network formation

THE PHARMACOGENOMICS OF EGFR-DEPENDENT NSCLC: PREDICTING AND ENHANCING RESPONSE TO TARGETED EGFR THERAPY

The introduction of tyrosine kinase inhibitors (TKI) targeting the epidermal growth factor receptor (EGFR) inhibitors to the clinic has resulted in an improvement in the treatment of non small cell lung cancer (NSCLC). However, many patients treated with EGFR TKIs do not respond to therapy. The burden of failed treatment is largely placed on the healthcare field, limiting the effectiveness of EGFR TKIs. Furthermore, responses are hindered by the emergence of resistance. Thus, two questions must be addressed to achieve maximum benefit of EGFR inhibitors: How can patients who will benefit from EGFR TKIs be selected a priori? How can patients who respond achieve maximal benefit? To answer these questions, two hypotheses were formed. First, the EGFR-dependent phenotype, which is displayed by the tumors cells of those patients who respond clinically to EGFR TKIs, can be captured by genomic profiling of NSCLC cell lines stratified by sensitivity to EGFR TKIs. This gene signature may be used to predict the outcome of EGFR TKI therapy in unknown samples. Secondly, the predictive signature of response to EGFR TKI could provide insights into the underlying biology of the phenotype of EGFR-dependency. This information could be exploited to identify inhibitors which could be combined with EGFR inhibitors to elicit a greater effect, thereby minimizing resistance. The work herein describes the testing of these hypotheses. Pharmacogenomics was utilized to define a signature of EGFR-dependency which effectively predicted response to EGFR TKI in vitro and in vivo. Furthermore, the signature was analyzed by bioinformatic approaches to identify the RAS/MAPK pathway as a candidate target in EGFR-dependent NSCLC. The RAS/MAPK pathway regulates expression and activation of EGF-like ligands. Furthermore, the RAS/MAPK pathway modulates EGFR stability in the EGFR-dependent phenotype. Further biochemical analyses demonstrated that the RAS/MAPK pathway mediates proliferation and survival of EGFR-dependent NSCLC cells. Finally, combinatorial treatment of EGFR-dependent NSCLC cell lines with small molecules targeting EGFR and the RAS/MAPK pathway yielded cytotoxic synergy. Thus, we have used pharmacogenomics methods to potentially improve NSCLC treatment by developing a method of predicting response and identifying an additional target to combine with EGFR TKIs to maximize responses

Doctor of Philosophy

dissertationThe coiled-coil is a common protein tertiary structural motif that is composed of two or more alpha helices intertwined together to formed a supercoil. In biological systems, the coiledcoil motif often forms the oligomerization domain of various proteins including DNA binding proteins, structural and transport proteins, and cellular transport and fusion proteins. It was first described by Crick in the 1950s while describing the structure of α-keratin and has since that time been the subject of numerous engineering and mutation studies. This versatile motif has been adapted to a number of nonbiological applications including environmentally responsive hydrogels, crosslinking agents, the construction of self-assembling fibers for tissue engineering, and biosensor surfaces. In this dissertation, we test the applicability of computational methods to understand the underlying energetics in coiled-coils as we apply molecular modeling approaches in the development of pharmaceutics. Two studies are described which test the limits of modern molecular dynamic force fields to understand the structural dynamics of the motif and to use energy calculation methodologies to predict favorable mutations for heterodimer formation and specificity. The first study considers the increasingly common use of fluorinated residues in protein pharmaceutics with regard to their incorporation in coiled-coils. Many studies find that fluorinated residues in the hydrophobic core increase protein stability against chemical and thermal denaturants. Often their incorporation fails to consider structural, energetic, and geometrical differences between these fluorinated residues and their nonfluorinated counterparts. To consider these differences, several variants of Hodges' very stable parallel heterodimer coiledcoil were constructed to examine the effect of salt bridge lengths and geometries with mixed fluorinated and nonfluorinated packed hydrophobic cores. In the second study, we collaborated with an experimental laboratory in the development of a mutant Bcr monomer with designed mutations to increase specificity and binding to the oncoprotein Bcr-Abl for use as an apoptosis inducing agent in chronic myelogenous leukemia (CML) cells. The final chapters of this dissertation discuss challenges and limitations that were encountered using force fields and energetic methods in our attempts to use computational chemistry to model this protein motif

Binding free energy calculations and molecular dynamics simulations on complexes of viral proteases with their ligands

Ein Ziel der biomolekularen Modellierung ist die Berechnung der Affinität deltaG von Liganden an Proteine, insbesondere Enzyme. Das Spektrum der Methoden, die zu diesem Zweck entwickelt wurden, reicht von theoretisch genauen aber aufwändigen Verfahren zu einfachen, eher qualitativen Verfahren. Während letztere häufig empirische Scoring-Funktionen und eine einzelne Struktur als Eingabe verwenden, wird für kompliziertere Methoden der möglichst vollständige Konformationsraum eines Protein-Ligand-Komplexes benötigt. Dieser wird mit Sampling-Verfahren wie der Molekulardynamik (MD) durchmustert. In dieser Promotionsarbeit sollten Verfahren zur Berechnung von deltaG, insbesondere Varianten der Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) Methode, getestet und nach Möglichkeit weiterentwickelt werden. Desweiteren sollte die Auswirkung bestimmter Resistenzmutationen auf Struktur und Dynamik von Proteinen mit unterschiedlichen Maßen aus MD Simulationen heraus erfasst werden. Der erste Schritt der quantitativen Modellierung mit MD ist die Beschreibung der Moleküle durch die Parametrisierung eines Kraftfelds. Anhand des sulfatierten Tyrosins wurde eine solche molekulare Parametrisierung für ein Nicht-Standard-Molekül durchgeführt. Sodann wurden Varianten der tendenziell weniger aufwändigen MMPBSA-Methode getestet im Hinblick auf ihre Konvergenz und ihre Eignung zur Bestimmung genauer deltaG-Werte oder zumindest verschiedene Enzym-Ligand-Komplexe in eine richtige Rangfolge gemäß ihrer deltaG-Werte zu bringen. Die Varianten unterscheiden sich durch verschiedene Solvatisierungsmodelle und Methoden zur Berechnung der Entropie. Als molekulares Referenzsystem wurden Mutanten der HIV Protease im Komplex mit Wirkstoffen verwendet, da es hierzu experimentelle Daten gibt, mit denen die berechneten Werte verglichen werden können. Am anderen Ende des methodischen Spektrums liegt die aufwändige Thermodynamische Integration (TI). Bei einer guten Kraftfeldparametrisierung sollte TI in der Lage sein, deltaG-Effekte in der Größenordnung weniger kJ/mol quantitativ zu bestimmen. Dies wurde anhand der Mutante L76V der HIVProtease, die für einige Wirkstoffe zu einer Resensitivierung (erhöhte Affinität) führt, getestet. Schließlich sollten MD-Simulationen verwendet werden, um die molekularen Effekte von Mutationen der NS3/4A-Protease des humanen Hepatitis C Virus auf die Bindung von Liganden (Substrat, Inhibitoren) zu verstehen.A major aim of biomolecular modelling is the calculation of binding affinities deltaG of ligands to proteins, especially enzymes. The spectrum of methods that has been developed for this task ranges from theoretically exact but expensive to more simple and qualitative ones. While the latter are often empirical scoring functions using one single structure as an input, the more complex methods require the preferably complete conformational space of a protein-ligand complex which can be sampled using methods such as molecular dynamics (MD). The intention of this thesis was to test and further develop methods for the calculation of deltaG, in particular variants of the molecular mechanics Poisson-Boltzmann surface area (MMPBSA) method. Furthermore, the effects of specific resistance mutations on the structure and dynamics of proteins should be determined using different metrics on MD simulation data. The first step to quantitative modelling using MD is the description of the molecules by parameterizing a forcefield. Such a molecular parameterization was performed for the non-standard amino acid sulpho-tyrosine. Subsequently, variants of the less expensive MMPBSA method were tested with regard to their ability to converge and determine deltaG estimates or at least establish the correct ranking of deltaG values for a set of enzyme-ligand complexes. Different solvation models and procedures to calculate the entropy have been used. As a molecular reference system, mutants of the HIV protease complexed with inhibitors were used. For these systems, experimental data are available to which the calculated values can be compared. At the other end of the methodological spectrum is the more expensive thermodynamic integration (TI). With a proper forcefield parameterization, TI should be able to quantitatively determine deltaG effects in the order of a few kJ/mol. This was tested on the HIV protease mutation L76V which is known to lead to a resensitivation (increased affinity) for some drugs. Eventually, MD simulations were used to understand the molecular effects of mutations of the NS3/4A protease, an enzyme of the human hepatitis C virus, on the binding of ligands (substrate, inhibitors)