Aberrant JNK signaling can result in two main forms of disease in humans: 1) neurological, coronary, hepatobiliary, and respiratory diseases and 2) autoimmune, inflammatory, and cancer conditions. Enantiomers of the lignan zuonin A, (-)-zuonin A and (+)-zuonin A, have been shown to bind to JNK isoforms with similar affinity and disrupt protein-protein interactions at JNK's D-recruitment site, making them a good candidate for specific non-ATP competitive inhibitors. However, (-)-zuonin A inhibits 80% of JNK catalyzed reactions at saturating levels, while (+)-zuonin A only inhibits 15%. Molecular docking and molecular dynamics simulations were performed to gain a better understanding of how these inhibitors interact JNK. The results of this study provide an alternative binding mode for (-)-zuonin A, compared to one proposed in a previous study, that shows (-)-zuonin A interacting with JNK via an induced fit mechanism by forming a larger pocket for itself near the highly conserved [phi]A-X-[phi]B recognition site, a dynamic move not seen in (+)-zuonin A simulations, and may help explain their different inhibition patterns. / text

The results from this work are reported into two sections listed below: <br><br> Synthesis: <br><br> Following structural classes of compounds have been designed, synthesized and studied as inhibitors of pjDHFR, RTKs and tubulin: <br> 1. 2,4-Diamino-6-(substituted-arylmethyl)pyrido[2,3-d]pyrimidines <br> 2. 4-((3-Bromophenyl)linked)-6-(substituted-benzyl)-7H-pyrrolo[2,3-d]pyrimidin-2-amines<br> 3. 6-Methyl-5-((substitutedphenyl)thio)-7H-pyrrolo[2,3-d]pyrimidin-2-amines <br> A total of 35 new compounds (excluding intermediates) were synthesized, characterized and submitted for biological evaluation. Results from these studies will be presented in due course. Bulk synthesis of the potent lead compound 170 was carried out to facilitate in vivo evaluation. <br><br> Docking Studies <br><br> Docking studies were performed using LeadIT, MOE, Sybyl or Flexx for target compounds listed above and for other compounds reported by Gangjee et al. against the following targets: <br> 1. Dihydrofolate reductase: human, P. carinii, P. jirovecii (pjDHFR) and T. gondii (tgDHFR)<br> 2. Thymidylate synthase: human (hTS) and T. gondii (tgTS)<br> 3. Receptor tyrosine kinases: VEGFR2, EGFR and PDGFR-β<br> 4. Colchicine binding site of tublulin.<br> Novel homology models were generated and validated for pjDHFR, tgDHFR, tgTS, PDGFR-β and the F36C L65P pjDHFR double mutant. The tgTS homology model generated in this study and employed to design novel inhibitors shows remarkable similarity with the recently published X-ray crystal structures. Docking studies were performed to provide a molecular basis for the observed activity of target compounds against DHFR, RTKs or tubulin. Results from these studies support structure-based and ligand-based medicinal chemistry efforts in order to improve potency and/or selectivity of analogs of the docked compounds against these targets.<br> Novel topomer CoMFA models were developed for tgTS and hTS using a set of 85 bicyclic inhibitors and for RTKs using a set of 60 inhibitors reported by Gangjee et al. The resultant models could be used to explain the potency and/or selectivity differences for selected molecules for tgTS over hTS. Topomer CoMFA maps show differences in steric and/or electronic requirements among the three RTKs, and could be used, in conjuction with other medicinal chemistry approaches, to modulate the selectivity and/or potency of inhibitors with multiple RTK inhibitory potential. Drug design efforts that involve virtual library screening using these topomer CoMFA models in conjunction with traditional medicinal chemistry techniques and docking are currently underway. / Mylan School of Pharmacy and the Graduate School of Pharmaceutical Sciences; / Medicinal Chemistry / PhD; / Dissertation;

From the reaction of ZnCl2 with the non-steroidal anti-inflammatory drug diflunisal (Hdifl), complex [Zn(difl-O)(2)(MeOH)(4)], 1 was formed, while in the presence of a N,N'-donor heterocyclic ligand 2,2'-bipyridylamine (bipyam), 2,2'-bipyridine (bipy), 1,10-phenanthroline (phen) and 2,2'-dipyridylketone oxime (Hpko), the complexes [Zn(difl-O,O')(2)(bipyam)], 2, [Zn(difl-O,O')(2)(bipy)], 3, [Zn(difl-O,O')(2)(phen)], 4 and [Zn(difl-O)2(Hpko)(2)], 5 were isolated, respectively. The complexes were characterized by physicochemical and spectroscopic techniques and the crystal structures of complexes 2, 3 and 5 were determined by X-ray crystallography. The ability of the complexes to scavenge 1,1-diphenyl-picrylhydrazyl, 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) and hydroxyl radicals and to inhibit soybean lipoxygenase was studied and the complexes were more active than free Hdifl. The interaction of the complexes with serum albumins was monitored by fluorescence emission spectroscopy and the corresponding binding constants were calculated. UV-vis spectroscopy, viscosity measurements and fluorescence emission spectroscopy for the competitive studies of the complexes with ethidium bromide were employed to investigate the interaction of the complexes with calf-thymus DNA and revealed intercalation as the most possible DNA-binding mode. Computational techniques were used to identify possible binding sites of albumins and DNA, and determine the druggability of human and bovine serum albumins with the five novel complexes. The majority of the complexes are stronger binders than the free Hdifl. This is the first study incorporating experimental and computational results to explore the binding activity of metal-NSAID complexes with DNA and serum albumins, suggesting their application as potential metallodrugs.

Cette thèse présente des résultats dans le domaine de la prédiction d’interactions protéine-ARN. C’est un domaine de recherche très actif, pour lequel la communauté internationale organise régulièrement des compétitions pour évaluer différentes techniques de prédictions in silico d’interactions protéine-protéine et protéine-ARN sur des données benchmarks (CAPRI, Critical Assessment of PRedictedInteractions), par prédiction en aveugle et en temps limité. Dans ce cadre, de nombreuses approches reposant sur des techniques d’apprentissage supervisé ont récemment obtenus de très bons résultats.Nos travaux s’inscrivent dans cette démarche.Nous avons travaillé sur des jeux de données de 120 complexes protéine-ARN extraits de la PRIDB non redondante (Protein-RNA Interface DataBase, banque de données de référence pour les interactions protéine-ARN). La méthodologie de prédiction d'interactions protéine-ARN a aussi été testée sur 40 complexes issus de benchmarks de l'état de l'art et indépendants des complexes de la PRIDB non redondante. Le faible nombre de structures natives et la difficulté de générer in silico des structures identiques à la solution in vivo nous a conduit à mettre en place une stratégie de génération de candidats par perturbation de l’ARN partenaire d’un complexe protéine-ARN natif. Les candidats ainsi obtenus sont considérés comme des conformations presque-natives si elles sont suffisamment proches du natif. Les autres candidats sont des leurres. L’objectif est de pouvoir identifier les presque natifs parmi l’ensemble des candidats potentiels, par apprentissage supervisé d'une fonction de score.Nous avons conçu pour l'évaluation des fonctions de score une méthodologie de validation croisée originale appelée le leave-"one-pdb"-out, où il existe autant de strates que de complexes protéine-ARN et où chaque strate est constituée des candidats générés à partir d'un complexe. L’une des approches présentant les meilleures performances à CAPRI est l’approche RosettaDock, optimisée pour la prédiction d’interactions protéine-protéine. Nous avons étendu la fonction de score native de RosettaDock pour résoudre la problématique protéine-ARN. Pour l'apprentissage de cette fonction de score, nous avons adapté l'algorithme évolutionnaire ROGER (ROC-based Genetic LearnER) à l'apprentissage d'une fonction logistique. Le gain obtenu par rapport à la fonction native est significatif.Nous avons aussi mis au point d'autres modèles basés sur des approches de classifieurs et de métaclassifieurs, qui montrent que des améliorations sont encore possibles.Dans un second temps, nous avons introduit et mis en oeuvre une nouvelle stratégie pour l’évaluation des candidats qui repose sur la notion de prédiction multi-échelle. Un candidat est représenté à la fois au niveau atomique, c'est-à-dire le niveau de représentation le plus détaillé, et au niveau dit “gros-grain”où nous utilisons une représentation géométrique basée sur des diagrammes de Voronoï pour regrouper ensemble plusieurs composants de la protéine ou de l’ARN. L'état de l'art montre que les diagrammes de Voronoï ont déjà permis d'obtenir de bons résultats pour la prédiction d'interactions protéine-protéine. Nous en évaluons donc les performances après avoir adapté le modèle à la prédiction d'interactions protéine-ARN. L’objectif est de pouvoir rapidement identifier la zone d’interaction (épitope) entre la protéine et l’ARN avant d’utiliser l’approche atomique, plus précise,mais plus coûteuse en temps de calcul. L’une des difficultés est alors de pouvoir générer des candidats suffisamment diversifiés. Les résultats obtenus sont prometteurs et ouvrent desperspectives intéressantes. Une réduction du nombre de paramètres impliqués de même qu'une adaptation du modèle de solvant explicite pourraient en améliorer les résultats. / My thesis shows results for the prediction of protein-RNA interactions with machine learning. An international community named CAPRI (Critical Assessment of PRedicted Interactions) regularly assesses in silico methods for the prediction of the interactions between macromolecules. Using blindpredictions within time constraints, protein-protein interactions and more recently protein-RNA interaction prediction techniques are assessed.In a first stage, we worked on curated protein-RNA benchmarks, including 120 3D structures extracted from the non redundant PRIDB (Protein-RNA Interface DataBase). We also tested the protein-RNA prediction method we designed using 40 protein-RNA complexes that were extracted from state-ofthe-art benchmarks and independent from the non redundant PRIDB complexes. Generating candidates identical to the in vivo solution with only a few 3D structures is an issue we tackled by modelling a candidate generation strategy using RNA structure perturbation in the protein-RNAcomplex. Such candidates are either near-native candidates – if they are close enough to the solution– or decoys – if they are too far away. We want to discriminate the near-native candidates from thedecoys. For the evaluation, we performed an original cross-validation process we called leave-”onepdb”-out, where there is one fold per protein-RNA complex and each fold contains the candidates generated using one complex. One of the gold standard approaches participating in the CAPRI experiment as to date is RosettaDock. RosettaDock is originally optimized for protein-proteincomplexes. For the learning step of our scoring function, we adapted and used an evolutionary algorithm called ROGER (ROC-based Genetic LearnER) to learn a logistic function. The results show that our scoring function performs much better than the original RosettaDock scoring function. Thus,we extend RosettaDock to the prediction of protein-RNA interactions. We also evaluated classifier based and metaclassifier-based approaches, which can lead to new improvements with further investigation.In a second stage, we introduced a new way to evaluate candidates using a multi-scale protocol. A candidate is geometrically represented on an atomic level – the most detailed scale – as well as on a coarse-grained level. The coarse-grained level is based on the construction of a Voronoi diagram over the coarse-grained atoms of the 3D structure. Voronoi diagrams already successfully modelled coarsegrained interactions for protein-protein complexes in the past. The idea behind the multi-scale protocolis to first find the interaction patch (epitope) between the protein and the RNA before using the time consuming and yet more precise atomic level. We modelled new scoring terms, as well as new scoring functions to evaluate generated candidates. Results are promising. Reducing the number of parameters involved and optimizing the explicit solvent model may improve the coarse-grained level predictions.

The current logistics chains must be designed to copy with short life cycles of products, high stock velocity, low degree of predictability etc. The conditions for accuracy, reliability and flexibility of deliveries at the markets have been changing, and that is why it is inevitable to seek new alternative methods that would lead to higher efficiency levels, remaining competitiveness and, at the same time, fulfil the needs and wishes of customers. In this paper I focuse on cross-docking which is a newer distribution method. In the first part of the paper, I try to determine the principles of this operation in distribution, identify individual types of cross-docking and in detail describe the assumptions and conditions under which the method should be implemented to reach the requested results. The paper then continues with an analysis of the current position of cross-docking and there are given several examples of demand driven supply chains that have been using this distribution method at a certain point. These examples are followed with a discussion on current options and problems of implementing cross-docking in the case of retailers and manufacturers.

Thesis advisor: Mary F. Roberts / Phosphatidylinositol-specific phospholipase C (PI-PLC) is an enzyme important in membrane-associated signal transduction in eukaryotes, and pathogenic factors in bacteria. It catalyzes the conversion of PI to DAG and cIP, which is further converted to I-1-P. The phospholipid PC has been shown to activate cIP hydrolysis. EPR and NMR were used to examine PI-PLC binding to PC and PMe vesicles through the use of spin labels attached to cysteine mutants. It was concluded that the spin label interacted more significantly with the phosphorus of PC than that of PMe. The results also suggested the -OCH3 group was preferred over the -N(CH3)3 group, and that the protein penetrated into the bulk methylene region of the phospholipid bilayer. / Thesis (BS) — Boston College, 2005. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Chemistry. / Discipline: College Honors Program.

Submitted by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-06-29T13:54:15Z No. of bitstreams: 1 DIS_MAURICIO_BOFF_DE_AVILA_COMPLETO.pdf: 2632464 bytes, checksum: 1a170d33bfad88ea8a7aba65cb0f4bea (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-06-29T13:54:25Z (GMT) No. of bitstreams: 1 DIS_MAURICIO_BOFF_DE_AVILA_COMPLETO.pdf: 2632464 bytes, checksum: 1a170d33bfad88ea8a7aba65cb0f4bea (MD5) / Made available in DSpace on 2017-06-29T13:54:34Z (GMT). No. of bitstreams: 1 DIS_MAURICIO_BOFF_DE_AVILA_COMPLETO.pdf: 2632464 bytes, checksum: 1a170d33bfad88ea8a7aba65cb0f4bea (MD5) Previous issue date: 2017-03-20 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / Microorganisms, in general, are the major agents of disease in humans. Data from the Brazilian Ministry of Health show bacterial diseases as the main causes of death in the country. In the therapy of these organisms, antibiotics are considered the most successful chemotherapy methods of 21st century medicine, as they represent the first, and only, line of combat against bacterial diseases. The development of new antibiotic drugs is becoming increasingly necessary, as bacterial resistance rates become higher each year. At this point, shikimate pathway is attractive to this type of research, since it is considered an essential pathway for the maintenance of these organisms in the environment, besides being absent in animals. The pathway is responsible for the formation of chorismate, precursor of aromatic amino acids (Phe, Trp and Tyr), folic acid and ubiquinones in the groups of organisms that presents it. The third reaction of the shikimate biosynthetic pathway is performed by the enzyme DHQD. In this step the reversible dehydration of the DHQ molecule is performed aiming to transform into 3-dehydroshikimate, the focus reaction of this study. In the search for new DHQD inhibitors, docking simulations were performed against the three-dimensional structure of a target protein, since it is a process that seeks to find, among possible orientations/conformations of a ligand in the active site, the one that presents the lower binding energy and, consequently, greater affinity. In addition to the docking simulations, machine learning methods were used to formulate polynomial scoring functions, based on the MVD scoring functions, which were able to predict protein/binder affinity. At the end of all the simulations and tests carried out throughout the project, we conclude that the Polscore56 equation was the most skilled to predict the affinity between the active site of DHQD and tested compounds. For this polynomial, the results of test set (? = 0,900; p-value = 0,037), AUC (74,686%), EF1 (540) and EF2 (159,23) were, in most of the categories evaluated, the best, confirming the formulated hypotheses on the equation and indicating it for further studies with the enzyme. / Microrganismos, em geral, apresentam-se como os principais agentes de doen?as em seres humanos. Dados do Minist?rio da Sa?de Brasileiro demonstram doen?as bacterianas como as principais causas de morte no pa?s. Na terap?utica desses organismos, os antibi?ticos s?o considerados os m?todos quimioter?picos de maior sucesso da medicina do s?culo XXI, pois representam a primeira, e ?nica, linha de combate contra doen?as bacterianas. O desenvolvimento de novas drogas antibi?ticas torna-se cada vez mais necess?rio, uma vez que os ?ndices de resist?ncia bacteriana se tornam mais altos a cada ano. Nesse ponto, a rota metab?lica do chiquimato ? atraente a esse tipo de pesquisa, por ser considerada uma via essencial para a manuten??o desses organismos no ambiente, al?m de estar ausente em animais. A via ? respons?vel pela forma??o do corismato, precursor de amino?cidos arom?ticos (Phe, Trp e Tyr), ?cido f?lico e ubiquinonas nos grupos de seres vivos onde est? presente. A terceira rea??o da via biossint?tica do chiquimato ? realizada pela enzima DHQD. Nesse passo ? realizada a desidrata??o revers?vel da mol?cula DHQ visando transform?-la em 3-desidrochiquimato, rea??o foco desse estudo. Na busca por novos inibidores de DHQD foram realizadas simula??es de docking de pequenos ligantes contra a estrutura tridimensional de uma prote?na alvo, pois ? um processo onde se visa encontrar, entre as poss?veis orienta??es/conforma??es de um ligante no s?tio ativo, aquela que apresenta a menor energia de liga??o e, consequente, maior finidade. Al?m das simula??es de docking, foram realizados m?todos de Aprendizagem de M?quina na formula??o de fun??es escores polinomiais, a partir de fun??es escores presentes no MVD, que fossem capazes de prever a afinidade entre prote?na/ligante. Ao final de todas as simula??es e testes realizados ao longo do projeto, chegamos ? conclus?o de que a equa??o Polscore56 apresentou-se como a mais h?bil para prever a afinidade entre o s?tio ativo de DHQD com compostos testados. Para esse polin?mio os resultados de test set (? = 0,900; p-value = 0,037), AUC (74,686%), EF1 (540) e EF2 (159,23) foram, na maioria das categorias avaliadas, os melhores, confirmando as hip?teses formuladas sobre a equa??o e indicando-a para estudos posteriores com a enzima.

<p>Protein conformational transitions are fundamental to the functions of many proteins, and computational methods are valuable for elucidating the transitions that are not readily accessible by experimental techniques. Here we developed accelerated sampling methods to calculate optimized all-atom protein conformational transition paths. Adaptively biased path optimization (ABPO) is a computational simulation method to optimize the conformational transition path between two states. We first examined the transition paths of three systems with relatively simple transitions. The ways to define reduced variables were explored and transition paths were built at convergence of the optimizations. We constructed the all-atom conformational transition path between the active and the inactive states of the Src kinase domain. The C helix rotation was identified as the main free energy barrier in the all‑atom system, and the intermediate conformations and key interactions along the transition path were analyzed. This is the first demonstration of the robustness of a computational method for calculating protein conformational transitions without restraints to a specified path. We also evaluated protein‑peptide interactions using both molecular dynamics simulations and peptide docking. Long unbiased simulations were used to evaluate Src‑SSP interactions and complex stability in both implicit and explicit solvent. Molecular docking was used to build possible protein‑peptide interaction models, using both Src regulatory domain SH2 and the kinase domain. Possible Src‑SSP complexes were built as the first Src‑substrate complex structure models.</p>

P2X receptors (P2XRs) are trimeric cation channels activated by extracellular ATP. Human P2XRs (P2X1-7) are expressed in nearly all mammalian tissues, and they are an important drug target because of their involvement in inflammation and neuropathic pain. The aim of this thesis is to address the following questions. P2XR crystal structures have revealed an unusual U-shape conformation for bound ATP; how does the U-shape conformation of ATP and its derivatives affect channel activation? Where and how do the selective, non-competitive inhibitors AZ10606120 and A438079 bind to P2X7R? What is the structure of the hP2X1R intracellular domain in the closed state? Molecular modelling and bioinformatics were used to answer these questions, hypotheses resulting from this work were tested in collaboration with Prof. Evans. Investigating the binding modes of ATP and its deoxy forms in hP2X1R showed that the ribose 2′-hydroxyl group is stabilising the U-shape conformation by a hydrogen bond to the γ-phosphate. The reduced ability of 2′-deoxy ATP to adopt the U-shape conformation could explain its weak agonist action in contrast to full agonists ATP and 3′-deoxy ATP. Ligand docking of AZ10606120 and A438079 into the hP2X7R predicted an allosteric binding site, this site has meanwhile been confirmed by P2X7R/antagonist X-ray structures. MD simulations suggested that unique P2X7R regions (residues 73-79 and T90/T94) contribute to an increase of the allosteric pocket volume compared to the hP2X1R. This difference in size might be the key for selectivity. The hP2X1R intracellular domain in the closed state was modelled ab initio, and interpreted in context of chemical cross-links (collaboration with Prof. Evans). This suggests a symmetrical arrangement of two short b-antiparallel strands within the Nterminal region and short a-helix in the C-terminal region and additional asymmetrical states.

O Atracamento Molecular é uma importante ferramenta utilizada no descobrimento de novos fármacos. O atracamento com ligante flexível é um processo computacionalmente custoso devido ao número alto de graus de liberdade do ligante e da rugosidade do espaço de busca conformacional representando a afinidade entre o receptor e uma molécula ligante. O problema é definido como a busca pela solução de menor energia de ligação proteína-ligante. Considerando uma função suficientemente acurada, a solução ótima coincide com a melhor orientação e afinidade entre as moléculas. Assim, o método de busca e a função de energia são partes fundamentais para a resolução do problema. Muitos desafios são enfrentados para a resolução do problema, o tratamento da flexibilidade, algoritmo de amostragem, a exploração do espaço de busca, o cálculo da energia livre entre os átomos, são alguns dos focos estudados. Esta dissertação apresenta uma técnica baseada em um Algoritmo Genético de Chaves Aleatórias Viciadas, incluindo a discretização do espaço de busca e métodos de agrupamento para a multimodalidade do problema de atracamento molecular. A metodologia desenvolvida explora o espaço de busca gerando soluções diversificadas. O método proposto foi testado em uma seleção de complexos proteína-ligante e foi comparado com softwares existentes: AutodockVina e Dockthor. Os resultados foram estatisticamente analisados em termos estruturais. O método se mostrou eficiente quando comparado com outras ferramentas e uma alternativa para o problema de Atracamento Molecular. / Molecular Docking is a valuable tool for drug discovery. Receptor and flexible Ligand docking is a very computationally expensive process due to a large number of degrees of freedom of the ligand and the roughness of the molecular binding search space. A Molecular Docking simulation starts with a receptor and ligand unbounded structures and the algorithm tests hundreds of thousands of ligands conformations and orientations to find the best receptor-ligand binding affinity by assigning and optimizing an energy function. Despite the advances in the conception of methods and computational strategies for search the best protein-ligand binding affinity, the development of new strategies, the adaptation, and investigation of new approaches and the combination of existing and state-of-the-art computational methods and techniques to the Molecular Docking problem are clearly needed. We developed a Biased Random-Key Genetic Algorithm as a sampling strategy to search the protein-ligand conformational space. The proposed method has been tested on a selection of protein-ligand complexes and compared with existing tools AutodockVina and Dockthor. Compared with other traditional docking software, the proposed method has the best average Root-Mean-Square Deviation. Structural results were statistically analyzed. The proposed method proved to be efficient and a good alternative to the molecular docking problem.