Conventional screening of potential drug candidates through wet lab affinity experiments using libraries of thousands of modified molecules is time and resource consuming, along with the fact that it contributes to the widening time gap between the discovery of disease-causing mutations and the implementation of resulting novel treatments. It is necessary to explore whether the preliminary use of high-throughput virtual screening (HTVS) software such as PyRx will curb both the time and money spent in discovering novel treatments for diseases such as congenital heart defects (CHDs). For example, AXIN2, a protein involved in a negative feedback loop inhibiting the Wnt/β-catenin signaling pathway important for cardiogenesis, has recently been associated with CHD. The loss-of-function mutation L10F on the tankyrase-binding domain of AXIN2 has been shown to upregulate the pathway by loss of inhibition ability, leading to the accumulation of intracellular β-catenin. In a different paper, however, AXIN2 has been shown to be stabilized using XAV-939, a small-molecule drug which targets tankyrase. PyRx and VMD will be used to modify the drug in order to increase its binding affinity to AXIN2, stabilizing the protein and reinstating its inhibitory property to treat CHDs. When used in adjunction to wet lab experiments, HTVS software may decrease costs and the time required to bring a potentially life-saving treatment into use.

For structure-based drug discovery, structural information of a target protein is necessary. NMR, or X-ray crystallography can provide necessary information on active site configuration that can lead a successful virtual screening campaign into identifying binders that may then be optimized into potent inhibitors. However, many challenges exist in the structure-based drug discovery cycle. For instance, structure determination of a protein of interest can many times be a daunting task. In addition, complex structure determination, which can allow essential characterization of protein-ligand interactions, is also challenging and many times impossible. Virtual screening heavily relies on such structural information, but hit-to-lead optimization schemes do as well. Furthermore, inherent protein characteristics such as conformational flexibility only add to the complexities in using structural information to identifying and optimizing inhibitors. In the scope of the work presented here, a structure-based drug discovery approach against three different protein targets is described. Each is presented with it's own set of challenges, but each has successfully led to the identification of new ligands. The drug discovery project against CXCL12 will first be described. CXCL12 is a small chemokine (~10KDa) that binds to the CXCR4 receptor promoting chemotaxis of lymphocytes but also metastasis of cancer cells. This interaction is further supported by sulfated tyrosines on CXCR4 that bind specific sites on the CXCL12 surface. The CXCL12-CXCR4 signaling axis has been a major focus of drug discovery, but efforts are mainly focused on CXCR4, since CXCL12 is a small protein lacking surface characteristics that are thought to be druggable. Yet, through a combination of rigid, flexible, and ensemble docking in virtual screening studies, we have successfully identified compounds that bind each of the three sulfotyrosine recognition sites on CXCL12, which normally bind the sulfated tyrosines on CXCR4 (sY7, sY12, and sY21). Furthermore, we have led a hit-to-lead approach in optimizing compounds against the sY21-binding site, aided by trivial information gained through crystallographic complex structure determination of CXCL12 bound by such a compound. We aim to eventually link compounds against different sites together and greatly improve potency. Next, the drug discovery project against P. aeruginosa LpxA will be described. In Gram-negative bacteria, the first step of lipid A biosynthesis is catalyzed by UDP-N-acetylglucosamine acyltrasferase (LpxA) through the transfer of a R-3-hydroxyacyl chain from the acyl carrier protein (ACP) to the 3'-hydroxyl group of UDP-GlcNAc. Acyl chain length selectivity varies between species of bacteria, but is highly specific and conserved within certain species. In E. coli and L. interrogans for example, LpxA is highly selective for longer R-3-hydroxyacil chains (C14 and C12 respectively), while in P. aeruginosa the enzyme is highly selective for R-3-hydroxydecanoyl, a 10-hydrocarbon long acyl chain. Three P. aeruginosa LpxA crystal structures will be described here for the first time; the apo form, the complex with its substrate UDP-GlcNAc, and the complex with its product UDP-3-O-(R-3-hydroxydecanoyl)-GlcNAc. A comparison between the APO form and complexes identifies key residues that position UDP-GlcNAc appropriately for catalysis, and supports the role of His121 in generating the nucleophile by interacting with the UDP-GlcNAc 3'-hydroxyl group. Furthermore, the product-complex structure supports the role of Met169 as the "hydrocarbon ruler", providing structural information on how P. aeruginosa LpxA is granted its exceptional selectivity for the 10-hydrocarbon long acyl chain. Structural information of the active site was subsequently used in designing virtual screening experiments that led to the identification of two ligands, confirmed by X-ray crystallography screening to bind to the active site. We aim to continue application of X-ray crystallography into screening compound binding, and to also use a hit-to-lead approach in compound optimization. Finally, the drug discovery project against the Tiam1 PDZ domain will be described. Tiam1 (T-cell lymphoma invasion and metastasis gene 1) is a GEF (guanine exchange factor) protein that activates Rac1 and initiates tumor formation. Tiam1 is regulated through its PDZ domain, which binds to syndecan1. We have successfully applied a virtual screening strategy to an existing crystallographic structure of the Tiam1 PDZ domain complexed to a syndecan1 peptide and identified four ligands that bind to the PDZ domain with low affinities. These compounds provide a starting point for future hit-to-lead optimization strategies.

Central infusion of the hexapeptide angiotensin IV (Ang IV) and its analogs have been demonstrated to markedly enhance memory retention and retrieval in rats using a range of learning and memory paradigms. This effect is mediated by the binding of the peptide to the specific binding site previously described as the AT4 receptor. The AT4 receptor has been isolated and identified as insulin-regulated aminopeptidase (IRAP), a type II transmembrane protein belonging to the M1 family of zinc-dependent aminopeptidases. Subsequently, AT4 receptor ligands, including Ang IV and its analogues and the unrelated peptide LVV-hemorphin-7, were demonstrated to be peptide inhibitors of IRAP. These findings suggest that AT4 ligands may exert their cognitive effects by inhibiting the catalytic activity of IRAP in the brain. Therefore, IRAP is an important target for the development of a new class of therapeutic agents for the treatment of memory loss. / To characterize IRAP at the molecular level and identify non-peptide inhibitors of IRAP for drug development, the aims of this study were to: 1) determine whether IRAP exists as a homodimer; 2) identify cysteine residue(s) involved in IRAP dimerization; 3) investigate the roles of the conserved residues of the HEXXH(X)18E Zn2+-binding motif and the GAMEN motif in substrate/inhibitor binding using site-directed mutagenesis; 4) use a molecular model of the catalytic domain of IRAP based on the crystal structure of a related M1 family metallopeptidase to: (i) identify key residues required for substrate/inhibitor binding; (ii) identify and characterize non-peptide IRAP inhibitors from a compound database by in silico virtual screening based on the homology model of IRAP. / Co-immunoprecipitation followed by Western blotting of IRAP under reducing and non-reducing conditions showed IRAP exists both as covalently- and non-covalently-bound homodimers. Serine scanning of cysteine residues potentially involved in forming inter-molecule disulfide-bonds was performed. Mutational analyses indicated that covalent homodimerization of IRAP is due to more than one cysteine residue. Limited trypsin digestion followed by co-immunoprecipitation suggests that non-covalent homodimerization of IRAP involves residues/regions within the last 130 amino acids of the protein. / The catalytic site of IRAP contains two consensus motifs, the H464EXXH468(X)18E487 Zn2+-binding motif and the G428AMEN432 motif. The role of conserved residues with these motifs was investigated using site-directed mutagenesis and pharmacological analyses. The conserved His and Glu residues of the Zn2+-binding motif were shown to be essential for IRAP catalytic activity. This was also observed for the Met and Glu residues of the GAMEN motif, while Asn mutant retained some catalytic activity. Residues important for substrate or inhibitor binding were identified as Gly, Ala and Asn. / A molecular model of the catalytic domain of IRAP based on the crystal structure of a homologous M1 metallopeptidase, leukotriene A4 hydrolase (LTA4H) was used to compare the catalytic sites of IRAP and LTA4H, and identified two amino acids at the putative substrate-binding pocket: Ala427 and Leu483 in IRAP, and the corresponding residues Tyr267 and Phe314 in LTA4H. A mutational analysis involving substitution of Ala427 and Leu483 with the corresponding residues revealed Ala427 and Leu483 characterize the enzyme S1 subsite, influencing the affinity and placement of substrates and peptide inhibitors in the catalytic site. / The molecular model of IRAP was also used for virtual screening of compound databases to identify novel non-peptide inhibitors. After two rounds of in silico screening, a family of compounds was identified and shown to be specific and competitive inhibitors of IRAP. Preliminary results suggest that one of these inhibitors, referred to as HFI 142, may possess memory-enhancing properties. The identification of non-peptide IRAP inhibitors will assist in pharmacological studies aimed at understanding the molecular mechanisms of IRAP aminopeptidase activity and physiological role of IRAP. In addition, the new inhibitors have the potential to form the basis for the development of a novel class of drugs useful for treating memory disorders.

Influenza virus A and B are common pathogens that cause respiratory disease in humans. Recently, a highly virulent H5N1 subtype avian influenza virus caused disease outbreaks in poultry around the world. Drug resistant type A viruses rapidly emerged, and the recent H5N1 viruses were reported to be resistant to all current antiviral drugs. There is an urgent need for the development of new antiviral drugs target against both influenza A and B viruses. This dissertation describes work to identify small molecule inhibitors of influenza protein NS1 by a high throughput fluorescence polarization assay. The N-terminal GST fusion of NS1A (residue 1-215) and NS1B (residue 1-145) were chosen to be the NS1A and NS1B targets respectively for HT screening. In developing the assay, the concentrations of fluorophore and protein, and chemical additives were optimized. A total of 17,969 single chemicals from four compound libraries were screened using the optimized assay. Six true hits with dose-response activity were identified. Four of them show an IC₅₀ less than 1 [micromolar]. In addition, one compound, EGCG, has proven to reduce influenza virus replication in a cell based assay, presumably by interacting with the RNA binding domain of NS1. High throughput, computer based, virtual screenings were also performed using four docking programs. In terms of enrichment rate, ICM was the best program for virtual screening inhibitors against NS1-RBD. The compound ZINC0096886 was identified as an inhibitor showing an IC₅₀ around 19 [micromolars] against NS1A, and 13.8 [micromolars] against NS1B. In addition, the crystallographic structures of the NS1A effector domain (wild type, W187A, and W187Y mutants) of influenza A/Udorn/72 virus are presented. A hypothetical model of the intact NS1 dimer is also presented. Unlike the wild type dimer, the W187Y mutant behaved as a monomer in solution, but still was able to binding its target protein, CPSF30, with wild type binding affinity. This mutant may be a better target for the development of new antiviral drugs, as the CPSF30 binding pocket is more accessible to potential inhibitors. The structural information of those proteins would be very helpful for virtual screening and rational lead optimization. / text

Central infusion of the hexapeptide angiotensin IV (Ang IV) and its analogs have been demonstrated to markedly enhance memory retention and retrieval in rats using a range of learning and memory paradigms. This effect is mediated by the binding of the peptide to the specific binding site previously described as the AT4 receptor. The AT4 receptor has been isolated and identified as insulin-regulated aminopeptidase (IRAP), a type II transmembrane protein belonging to the M1 family of zinc-dependent aminopeptidases. Subsequently, AT4 receptor ligands, including Ang IV and its analogues and the unrelated peptide LVV-hemorphin-7, were demonstrated to be peptide inhibitors of IRAP. These findings suggest that AT4 ligands may exert their cognitive effects by inhibiting the catalytic activity of IRAP in the brain. Therefore, IRAP is an important target for the development of a new class of therapeutic agents for the treatment of memory loss. / To characterize IRAP at the molecular level and identify non-peptide inhibitors of IRAP for drug development, the aims of this study were to: 1) determine whether IRAP exists as a homodimer; 2) identify cysteine residue(s) involved in IRAP dimerization; 3) investigate the roles of the conserved residues of the HEXXH(X)18E Zn2+-binding motif and the GAMEN motif in substrate/inhibitor binding using site-directed mutagenesis; 4) use a molecular model of the catalytic domain of IRAP based on the crystal structure of a related M1 family metallopeptidase to: (i) identify key residues required for substrate/inhibitor binding; (ii) identify and characterize non-peptide IRAP inhibitors from a compound database by in silico virtual screening based on the homology model of IRAP. / Co-immunoprecipitation followed by Western blotting of IRAP under reducing and non-reducing conditions showed IRAP exists both as covalently- and non-covalently-bound homodimers. Serine scanning of cysteine residues potentially involved in forming inter-molecule disulfide-bonds was performed. Mutational analyses indicated that covalent homodimerization of IRAP is due to more than one cysteine residue. Limited trypsin digestion followed by co-immunoprecipitation suggests that non-covalent homodimerization of IRAP involves residues/regions within the last 130 amino acids of the protein. / The catalytic site of IRAP contains two consensus motifs, the H464EXXH468(X)18E487 Zn2+-binding motif and the G428AMEN432 motif. The role of conserved residues with these motifs was investigated using site-directed mutagenesis and pharmacological analyses. The conserved His and Glu residues of the Zn2+-binding motif were shown to be essential for IRAP catalytic activity. This was also observed for the Met and Glu residues of the GAMEN motif, while Asn mutant retained some catalytic activity. Residues important for substrate or inhibitor binding were identified as Gly, Ala and Asn. / A molecular model of the catalytic domain of IRAP based on the crystal structure of a homologous M1 metallopeptidase, leukotriene A4 hydrolase (LTA4H) was used to compare the catalytic sites of IRAP and LTA4H, and identified two amino acids at the putative substrate-binding pocket: Ala427 and Leu483 in IRAP, and the corresponding residues Tyr267 and Phe314 in LTA4H. A mutational analysis involving substitution of Ala427 and Leu483 with the corresponding residues revealed Ala427 and Leu483 characterize the enzyme S1 subsite, influencing the affinity and placement of substrates and peptide inhibitors in the catalytic site. / The molecular model of IRAP was also used for virtual screening of compound databases to identify novel non-peptide inhibitors. After two rounds of in silico screening, a family of compounds was identified and shown to be specific and competitive inhibitors of IRAP. Preliminary results suggest that one of these inhibitors, referred to as HFI 142, may possess memory-enhancing properties. The identification of non-peptide IRAP inhibitors will assist in pharmacological studies aimed at understanding the molecular mechanisms of IRAP aminopeptidase activity and physiological role of IRAP. In addition, the new inhibitors have the potential to form the basis for the development of a novel class of drugs useful for treating memory disorders.

Locating a ligand-binding site is an important first step in structure-guided drug discovery, but current methods typically assess the pocket as a whole, doing little to suggest which regions and interactions are the most important for binding. This thesis introduces Fragment Hotspot Maps, a grid-based method that samples atomic propensities derived from interactions in the Cambridge Structural Database (CSD) with simple molecular probes. These maps specifically highlight fragment-binding sites and their corresponding pharmacophores, offering more precision over other binding site prediction methods. The method is validated by scoring the positions of 21 fragment and lead pairs. Fragment atoms are found in the highest scoring parts of the map corresponding to their atom type, with a median percentage rank of 98%. This is reduced to 72% for lead atoms, showing that the method can differentiate between the hotspots, and the warm spots later used during fragment elaboration. For ligand-bound structures, they provide an intuitive visual guide within the binding site, directing medicinal chemists where to grow the molecule and alerting them to suboptimal interactions within the original hit. These calculations are easily accessible through a simple to use web application, which only requires an input PDB structure or code. High scoring specific interactions predicted by the Fragment Hotspot Maps can be used to guide existing computer aided drug discovery methods. The Hotspots Python API has been created to allow these work flows to be executed programmatically through a single Python script. Two of the functions use scores from the Fragment Hotspot Maps to guide virtual screening methods, docking and field-based ligand screening. Docking virtual screening performance is improved by using a constraint selected from the highest scoring polar interaction. The field-based ligand screener uses modified versions of the Fragment Hotspot Maps directly to predict and score the binding pose. This workflow gave comparable results to docking, and for one target, Glucocorticoid receptor (GCR), showed much better results, highlighting its potential as an orthogonal approach. Fragment Hotspot Maps can be used at multiple stages of the drug discovery process, and research into these applications is ongoing. Their utility in the following areas are currently being explored: to assess ligandability for both individual structures and across proteomes, to aid in library design, to assess pockets throughout a molecular dynamics trajectory, to prioritise crystallographic fragment hits and to guide hit-to-lead development.

La méthylation anormale de l'ADN est l'une des principales caractéristiques du cancer. La nature dynamique et réversible de cette modification épigénétique en a fait une cible potentielle pour le traitement du cancer. UHRF1, une protéine essentielle dans la maintenance de la méthylation de l'ADN, est également impliquée dans la tumorogenèse. UHRF1 est surexprimée dans une variété de cancers et est liée à l’inhibition des TSGs et à la prolifération cellulaire. Dans ce contexte, le but de ma thèse est d’identifier de potentiels inhibiteurs d’UHRF1 qui pourront être efficaces en clinique comme thérapie anti-cancéreuse. Pour atteindre cet objectif, une approche diversifiée a été adoptée qui inclue le criblage virtuel, des techniques biophysiques et biologiques qui permettent à caractériser l'activité inhibitrice des molécules actives et à comprendre leur mécanisme d'action. Nous avons identifié un composé positif de la famille des anthraquinones qui inhibe UHRF1 en se liant à son domaine SRA et perturbe son interaction avec DNMT1, l'enzyme responsable du maintien de la méthylation de l'ADN. Ce composé présente une activité antiproliférative dans différentes lignées cancéreuses. / Abnormal DNA methylation is one of the major hallmarks of cancer. The dynamic and reversible nature of this epigenetic modification has made it a potential target for cancer treatment. UHRF1, a pivotal DNA methylation maintenance protein, is also strongly involved in tumorogenesis. It isoverexpressed in a wide array of cancers and leads to silencing of TSGs and tumor growth. In this context, the aim of the thesis is to develop potential UHRF1 inhibitors that may be clinically effective for anti-cancer therapy. To reach this objective, a diverse approach was adopted including virtual screening, biophysical and biological techniques that helped to characterize the inhibitory activity of active molecules and understand their mechanism of action. The tests revealed one positive compound from the anthraquinone family that inhibited UHRF1 by binding to its SRA domain and impairing its interaction with DNMT1, the enzyme responsible for DNA methylation maintenance. This compound showed an anti-proliferative activity in various cancer cells.

Ma thèse introduit cinq logiciels de trois différents domaines: le calcul parallèle et distribué, la biologie structurale computationnelle et la chémoinformatique. Le logiciel pour le calcul parallèle et distribué s'appelle PAR. PAR permet d'exécuter des expériences indépendantes de manière parallèle et distribuée. Les logiciels pour la biologie structurale computationnelle sont Durandal, EleKit et Fragger. Durandal exploite la propagation de contraintes géométriques afin d'accélérer l'algorithme de partitionnement exact pour des modèles de protéines. EleKit permet de mesurer la similarité électrostatique entre une petite molécule et la protéine qu'elle est conçue pour remplacer sur une interface protéine-protéine. Fragger est un cueilleur de fragments de protéines permettant de sélectionner des fragments dans la banque de protéines mondiale. Enfin, le logiciel de chémoinformatique est ACPC. ACPC permet l'encodage fin, d'une manière rotation-translation invariante, d'une molécule dans un ou une combinaison des trois espaces chimiques (électrostatique, stérique ou hydrophobe). ACPC est un outil de criblage virtuel qui supporte les requêtes consensus, l'annotation de la molécule requête et les processeurs multi-coeurs. / This thesis introduces five software useful in three different areas : parallel and distributed computing, computational structural biology and chemoinformatics. The software from the parallel and distributed area is PAR. PAR allows to execute independent experiments in a parallel and distributed way. The software for computational structural biology are Durandal, EleKit and Fragger. Durandal exploits the propagation of geometric constraints to accelerate the exact clustering algorithm for protein models. EleKit allows to measure the electrostatic similarity between a chemical molecule and the protein it is designed to replace at a protein-protein interface. Fragger is a fragment picker able to select protein fragments in the whole protein data-bank. Finally, the chemoinformatics software is ACPC. ACPC encodes in a rotation-translation invariant way a chemical molecule in any or a combination of three chemical spaces (electrostatic, steric or hydrophobic). ACPC is a ligand-based virtual screening tool supporting consensus queries, query molecule annotation and multi-core computers.

Structure based drug design is a rapidly advancing discipline that examines how protein targets structurally interact with small molecules, or known inhibitors, and then uses this information to lead inhibitor optimization efforts. In the case of novel inhibitors, protein structural information is first obtained via X-ray crystallography, NMR studies, or a combination of both approaches. Then, computational molecular docking is often used to screen, in silico, millions of small molecules and calculate the potential interactions they may have with the target protein’s binding pocket, in hopes of identifying novel low affinity inhibitors. By examining the interactions these small, low affinity, inhibitors have with the binding pocket, optimization efforts can be focused on maximizing interactions with “hot spots” within the pocket, thus leading to larger, high affinity inhibitors. A similar optimization technique can also be applied to known inhibitors. By examining the interactions of a known inhibitor with the binding site, new compounds can be designed to target “hot spots” in the binding pocket using the known inhibitors core structure as a starting point. The affinity of the newly designed compounds can then be compared to the affinity of the original inhibitor, and further rounds of optimization can be carried out. While simple in design, there are many challenges associated with structure based drug design studies, and there is no guarantee novel inhibitors will be found, but ultimately, it is an extremely powerful methodology that results in a much higher hit rate than other, similar, techniques. The work herein describes the use of structure based drug design to target several different proteins involved in bacterial antibiotic resistance, and a protein that has been implicated in the development of Alzheimer’s disease. The goal of the first project was to design a new PBP inhibitor based upon an existing scaffold, and to better understand the binding mechanism and molecular interactions between penicillin binding proteins and their inhibitors. PBPs are a group of proteins that catalyze the last steps of bacterial cell wall formation, and are the targets of the β-lactam antibiotics. Two compounds were designed which conjugated a ferrocene or ruthenocene group to 6-aminopenicillinic acid, and their antibiotic properties were tested against a range of bacterial strains. To get a better understanding of how the 6-APA organometallic compounds interacted with the PBP active site, a CTX-M-14 β-lactamase model system was used for X-ray crystallographic studies. CTX-M-14 was chosen as its active site shares many key catalytic features with PBPs, and it easily, and reproducibly, yields crystals capable of diffracting to sub-atomic (< 1.0 Å) resolution. I determined a 1.18 Å structure of 6-APA-Ru in complex with CTX-M-14 E166A β-lactamase and was able to gain unprecedented details of the interactions of the ruthenocene group with the CTX-M active site. This structure also revealed that the compound bound in the CTX-M active site was actually the decarboxylated and hydrolyzed product, which was the first time a decarboxylated product had been captured in the CTX-M active site. A second, 0.85 Å, structure of CTX-M in complex with 6-APA-Ru was determined and shed light on how the hydrogen bonding network in the CTX-M active site changes in response to the 6-APA-Ru product binding. A final, 1.30 Å, structure captured the carboxylated and hydrolyzed 6-APA-Ru product in complex with CTX-M, which was the first time the carboxylated product had been captured in the CTX-M active with the catalytic Ser70 residue intact. The results show the potential of the ruthenocene group in improving antibiotic potency, and help to better elucidate the changes that occur in the CTX-M active site upon inhibitor binding, while at the same time, telling us what changes could occur in the active site of PBPs. The next project was focused on novel inhibitor discovery against several different PBPs. PBPs have been successfully inhibited by β-lactam antibiotics for decades, but the alarming rise of bacteria resistant to these antibiotics has placed increased urgency on the discovery of novel PBP inhibitors. A fragment based molecular docking approach was employed to virtually screen millions of small compounds for interactions with the targeted active sites, and then high scoring compounds were selected for visual inspection and inhibitory testing. Virtual screening was first done against Staphylococcus aureus monofunctional transglycosylase, a type of PBP. MTG provided a good binding pocket for virtual screening, but proved challenging to purify and crystallize. However, through great effort MTG crystals were eventually obtained. After repeated rounds of virtual screening against MTG, multiple compounds were selected for inhibition testing, and testing is currently ongoing. Virtual screening was also done against Pseudomonas aeruginosa PBP5 and PBP1a. Purification and crystallization of these proteins proved to be easier than MTG, and both yielded diffraction quality crystals. The final project focused on virtual screening against a protein implicated in the development of Alzheimer’s disease, Slingshot Phosphatase 1. The brains of AD patients have been found to contain elevated levels of active Cofilin, and these elevated levels of active Cofilin may lead to the overproduction of amyloid β. Aβ overproduction, and its resulting accumulation, is believed to be one of the pathways that lead to AD symptoms. Cofilin is activated when it is dephosphorylated by SSH1, and inhibiting this activation may decrease the production of Aβ and the development of AD symptoms. There is no known structure of SSH1, so to perform virtual screening a SSH1 homology model was constructed using the homolog SSH2 as a starting point. Virtual screening was then performed using the SSH1 homology model and many compounds were selected for inhibition testing. Initial testing found several compounds that could prevent Cofilin dephosphorylation at levels > 10μM. However, three compounds were found to be exceptionally active, and could prevent Cofilin dephosphorylation at both 1 and 10 μM. One of these three compounds was tested directly against purified SSH1 and found to inhibit its activity, and reduce Aβ production. Crystallization of purified SSH1, and SSH2, was attempted in order to get complex structures with the three best compounds. SSH2 crystals were obtained which diffracted to 1.91 Å, and several initial hits were found for SSH1. Optimization of crystals for both proteins is currently ongoing. The SSH1 inhibitor, along with the two other highly active compounds, provides an excellent starting point for the development of highly potent SSH1 inhibitors.

A doença de Chagas, uma infecção parasitária amplamente distribuída na América Latina, é um problema grave de saúde pública com consequências devastadoras em termos de morbidade e mortalidade humana. O arsenal terapêutico contra a doença é bastante limitado e insuficiente em todos os aspectos clínicos. Visando o desenvolvimento de novos agentes antichagásicos, várias proteínas do parasita têm sido exploradas como alvos terapêuticos. Neste contexto, a enzima cruzaína, uma cisteíno protease envolvida nos estágios de desenvolvimento e diferenciação do Trypanosoma cruzi, foi selecionada para os nossos estudos, visando a identificação de inibidores através do uso do método de planejamento de fármacos baseado na estrutura do receptor (SBDD, do inglês, structure-based drug design). Esta metodologia engloba uma diversidade de estratégias, empregando estruturas cristalográficas de proteínas alvo, disponíveis usualmente no Protein Data Bank (PDB). Entre as técnicas modernas utilizadas no SBDD, destaca-se a triagem virtual baseada na estrutura do receptor (SBVS, do inglês, structure-based virtual screening), que possibilita a seleção de novos candidatos a ligantes de proteínas alvo, a partir de grandes bases de dados de compostos. No presente trabalho de dissertação, a seleção de 19 estruturas da enzima cruzaína, em complexo com ligantes, permitiu a aplicação de métodos de SBDD. Um conjunto com cerca de 3,4 milhões de compostos, com característica líder-similar (do inglês, lead-like), e outro conjunto com aproximadamente 450.000 compostos, com característica fragmento-similar (do inglês, fragment-like), foram coletados da base de dados ZINC. O programa DOCK 3.5.54 foi empregado na triagem virtual das bases de dados utilizando-se a estrutura cristalográfica PDB ID: 3KKU. Um subconjunto com 35.000 moléculas foi selecionado para estudos posteriores com os programas GOLD e Surflex. As 500 melhores moléculas selecionadas por cada um dos programas foram analisadas visualmente considerando-se diversas características estruturais dos subsítios da enzima cruzaína e dos ligantes (e.g., complementaridade molecular, flexibilidade, lipofilia do subsítio S2, presença de doadores e aceptores de hidrogênio entre os subsítios S2 e S1). Desta forma, um conjunto final de 18 compostos foi priorizado para os ensaios bioquímicos frente a enzima cruzaína. Destes 18 compostos, 6 apresentaram atividade inibitória frente a cruzaína, com destaque para os 2 mais promissores, com valores de IC50 (concentração de inibidor necessária para reduzir em 50% a atividade enzimática) de 20 &micro;M e 580 nM. O inibidor mais potente da série foi selecionado da base fragmento-similar e apresentou um valor de eficiência do ligante (EL) de 0,53 kcal/mol/átomo, considerado significativo para otimização em química medicinal. A integração de técnicas computacionais e experimentais permitiu a descoberta de ligantes com inovação estrutural, abrindo novas perspectivas para o planejamento de inibidores mais potentes e seletivos da enzima cruzaína de T. cruzi. / Chagas disease, a parasitic infection widely distributed in Latin America, is a serious public health problem with devastating consequences in terms of human morbidity and mortality. The therapeutic arsenal against the disease is very limited and insufficient in all clinical aspects. This has led to a new paradigm for the discovery of new agents that act on specific enzymes or metabolic pathways. The enzyme cruzain, a cysteine protease essential for the survival of the parasite Trypanosoma cruzi, has been selected in this work as an attractive target for the development of new inhibitors through the use of structure-based drug design (SBDD). This approach brings together a diversity of strategies, which employs crystal structures of target proteins, usually available in the Protein Data Bank (PDB). Structure-based virtual screening (SBVS), one of the most important techniques used in SBDD, allows the selection of new ligands of target proteins from large libraries of compounds. In this work, 19 crystal structures of the cruzain enzyme, in complex with ligands, allowed the application of SBDD methods. A data set of about 3.4 million compounds, with lead-like characteristics, and a second data set, with approximately 450,000 compounds, with fragment-like characteristics, were collected from the ZINC data base. The docking program DOCK 3.5.54 was employed in the virtual screening of the data sets using the crystal structure PDB ID: 3KKU. A subset of 35,000 compounds was selected for further studies with the programs GOLD and Surflex. The 500 top ranked molecules for each of the programs were visually inspected considering a number of structural characteristics of the subsites of the cruzain enzyme, as well as of the ligands (e.g., molecular complementarity, flexibility, the hydrophobic S2 subsite, and the presence of hydrogen donors and acceptors between the subsites S2 and S1). Thus, a final subset of 18 compounds was prioritized for the biochemical assays against the cruzain enzyme. Six out of 18 compounds exhibited enzyme inhibition, with the most two promising inhibitors having IC50 values (IC50 refers to the concentration of compound required for 50% inhibition of cruzain) of 20 &micro;M e 580 nM. The most potent inhibitor of the series was selected from the fragment-like data set and showed a ligand efficiency of 0,53 kcal/mol/atom, which is considered significant in drug design. The integration of computational and experimental approaches allowed the discovery of compounds with innovative structures, providing new perspectives for the design of inhibitors of T.cruzi cruzain having increased potency and selectivity.