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1	Adaptação e avaliação de triagem virtual em arquiteturas paralelas híbridas Jesus, Éverton Mendonça de 22 November 2016 (has links) Submitted by Mayara Nascimento (mayara.nascimento@ufba.br) on 2017-05-31T11:34:12Z No. of bitstreams: 1 dissertacao-everton-mendonca Copy.pdf: 756322 bytes, checksum: 010382d1618c37e3db7570c6c156e7fa (MD5) / Approved for entry into archive by Vanessa Reis (vanessa.jamile@ufba.br) on 2017-06-02T14:02:16Z (GMT) No. of bitstreams: 1 dissertacao-everton-mendonca Copy.pdf: 756322 bytes, checksum: 010382d1618c37e3db7570c6c156e7fa (MD5) / Made available in DSpace on 2017-06-02T14:02:16Z (GMT). No. of bitstreams: 1 dissertacao-everton-mendonca Copy.pdf: 756322 bytes, checksum: 010382d1618c37e3db7570c6c156e7fa (MD5) / A Triagem Virtual é uma metodologia computacional de busca de novos fármacos que verifica a interação entre moléculas (ligantes) e alvos macromoleculares. Este trabalho Objetivou a adaptação de uma ferramenta de Triagem Virtual para arquiteturas paralelas com GPUs e multicore e avaliação dos seus resultados, buscando com isso aumentar o desempenho da triagem, reduzindo seu tempo de execução e, consequentemente, permitindo a escalabilidade do número de moléculas envolvidas no processo. A ferramenta escolhida Para este propósito foi o Autodock devido a sua ampla adoção dentre os pesquisadores de novos fármacos que utilizam a Triagem Virtual. Três implementações foram criadas abordando diferentes técnicas de paralelismo. A primeira foi uma versão multicore onde foi utilizado OpenMP, a segunda foi uma implementação em GPUs utilizando CUDA e porém, foi criada uma implementação híbrida utilizando a versão multicore e a versão para GPUs em conjunto. Em todas as abordagens foram alcançados bons resultados em relação ao tempo de execução total, porém a versão híbrida foi a que obteve os melhores resultados. A versão multicore alcançou speedups, ou ganhos de desempenho, da ordem de 10 vezes. A versão para GPUs alcançou speedups da ordem de 28 vezes e a híbrida de 85 vezes. Com estes resultados foi possível determinar que o uso de plataformas de execução paralelas podem, efetivamente, melhorar o desempenho Triagem Virtual. Computação de Alto Desempenho Bioinformática Triagem Virtual Autodock CUDA
2	Accelerating a Molecular Docking Application by Leveraging Modern Heterogeneous Computing Systems / Accelerering av en Molekylär Dockningsapplikation genom att Utnyttja Moderna Heterogena Datorsystem Schieffer, Gabin January 2023 (has links) In drug development, molecular docking methods aim at characterizing the binding of a drug-like molecule to a protein. In a typical drug development process, a docking task is repeated millions of time, which makes optimization efforts essential. In particular, modern heterogeneous architectures, such as GPUs, allow for significant acceleration opportunities. AutoDock-GPU, a state-of-the-art GPU-accelerated molecular docking software, estimates the geometrical conformation of a docked ligand-protein complex by minimizing an energy-based scoring function. Our profiling results indicated that a reduction operation, which is performed several millions times in a single docking run, limits performance in AutoDock-GPU. Thus, we proposed a method to accelerate the block-level sum reduction of four-element vectors by using matrix operations. We implemented our method to make use of the high throughput capabilities offered by NVIDIA Tensor Cores to perform matrix operations. We evaluated our approach by designing a simple benchmark, and achieved a 4 to 7-fold runtime improvement compared to the original method. We then integrated our reduction operation into AutoDock-GPU and evaluated it on multiple chemical complexes on three GPUs. This evaluation allowed to assess the possibility to use half-precision reduction operations in parts of AutoDock-GPU code, without detrimental effects on the simulation result. In addition, our implementation achieved an average 27% improvement on the overall docking time during a real-world docking run. / Vid läkemedelsutveckling syftar molekylär dockningsmetoder till att karakterisera bindningen av en läkemedelsliknande molekyl till ett protein. I en typisk läkemedelsutvecklingsprocess upprepas en dockinguppgift miljontals gånger, vilket gör optimeringsinsatser nödvändiga. Framför allt moderna heterogena arkitekturer som GPU:er ger betydande accelerationsmöjligheter. AutoDock-GPU, en modern GPU-accelererad programvara för molekylär dockning, uppskattar den geometriska konformationen hos ett ligand-protein-komplex genom att minimera en energibaserad poängsättningsfunktion. Våra profileringsresultat visade att en reduktionsoperation, som utförs flera miljoner gånger i en enda dockningskörning, begränsar prestandan i AutoDock-GPU. Vi har därför föreslagit en metod för att accelerera summareduktionen på blocknivå av vektorer med fyra element med hjälp av matrisoperationer. Vi implementerade vår metod för att utnyttja den höga genomströmningskapacitet som erbjuds av NVIDIA Tensor Cores för att utföra matrisoperationer. Vi utvärderade vårt tillvägagångssätt genom att utforma ett enkelt testfall och uppnådde en 4- till 7-faldig förbättring av körtiden jämfört med den ursprungliga metoden. Vi integrerade sedan vår reduktionsoperation i AutoDock-GPU och utvärderade den på flera kemiska komplex på tre GPU:er. Denna utvärdering lät oss bedöma möjligheten att använda reduktionsoperationer med halvprecision i delar av AutoDock-GPU-koden, utan negativa effekter på simuleringsresultatet. Dessutom uppnådde vår version en genomsnittlig förbättring på 27% av den totala dockningstiden under en riktig dockningskörning. Molecular docking AutoDock GPU Tensor Core Drug Discovery Molekylär dockning AutoDock GPU Tensor Core Läkemedelsutveckling Computer Sciences Datavetenskap (datalogi) Computer and Information Sciences Data- och informationsvetenskap
3	Computational Structure Activity Relationship Studies on the CD1d/Glycolipid/TCR Complex using AMBER and AUTODOCK Nadas, Janos Istvan 29 September 2009 (has links) No description available. Biophysics Chemistry Immunology &945 -galactosylceramide CD1d TCR computational molecular dynamics AMBER AUTODOCK
4	Exploring Ligand Binding in HIV-1 Protease and K+ Channels Using Computational Methods Österberg, Fredrik January 2005 (has links) Understanding protein-ligand interactions is highly important in drug development. In the present work the objective is to comprehend the link between structure and function using molecular modelling. Specifically, this thesis has been focused on implementation of receptor flexibility in molecular docking and studying structure-activity relationships of potassium ion channels and their blockers. In ligand docking simulations protein motion and heterogeneity of structural waters are approximated using an ensemble of protein structures. Four methods of combining multiple target structures within a single grid-based lookup table of interaction energies are tested. Two weighted average methods permit consistent and accurate ligand docking using a single grid representation of the target protein structures. Quaternary ammonium ions (QAIs) are well known K+ channel blockers. Conformations around C–N bonds at the quaternary centre in tetraalkylammonium ions in water solution are investigated using quantum mechanical methods. Relative solvation free energies of QAIs are further estimated from molecular dynamics simulations. The torsion barrier for a two-step interconversion between the conformations D2d and S4 is calculated to be 9.5 kcal mol–1. Furthermore D2d is found to be more stable than the S4 conformation which is in agreement with experimental studies. External QAI binding to the K+ channel KcsA is also studied. Computer simulations and relative binding free energies of the KcsA complexes with QAIs are calculated. This is done with the molecular dynamics free energy perturbation approach together with automated ligand docking. In agreement with experiment, the Et4N+ blocker in D2d symmetry has better binding than the other QAIs. Binding of blockers to the human cardiac hERG potassium channel is studied using a combination of homology modelling, automated docking and molecular dynamics simulations. The calculations reproduce the relative binding affinities of a set of drug derivatives very well and indicate that both polar interactions near the intracellular opening of the selectivity filter as well as hydrophobic complementarity in the region around F656 are important for blocker binding. Hence, the derived model of hERG should be useful for further interpretations of structure-activity relationships. Cell and molecular biology hERG KcsA AutoDock LIE molecular dynamics ion channels Cell- och molekylärbiologi Cell and molecular biology Cell- och molekylärbiologi
5	Development of a Computational Mechanism to Generate Molecules with Drug-likeCharacteristics Ghiasi, Zahra 10 September 2021 (has links) No description available. Biomedical Engineering Bioinformatics Chemical Engineering GSK-3 protein computational drug discovery ligand docking RDKit Rosetta AutoDock Vina
6	Techniques de Modélisation Moléculaire appliquées à l'Etude et à l'Optimisation de Molécules Immunogènes et de Modulateurs de la Chimiorésistance. Fortuné, Antoine 21 December 2006 (has links) (PDF) L'objet de ce travail est de présenter de facon détaillée des méthodes de modélisation appliquées à l'analyse des mécanismes de reconnaissance moléculaire et à la conception de nouveaux composés bioactifs selon deux approches : la conception basée sur la structure des récepteurs et la conception basée sur la structure des ligands.<br />Dans le cadre du premier axe, la méthode de construction de protéines par homologie de Blundell, implémentée dans le module COMPOSER de SYBYL et la méthode d'amarrage de Morris, implémentée dans le logiciel AUTODOCK3, sont décrites et appliquées à la modélisation et à l'étude des mécanismes de reconnaissance moléculaire d'un antigène polysaccharidique de la bactérie Shigella flexneri 5a et de mimes peptidiques immunogènes par un anticorps humain protecteur : IgA I3.<br />Dans le cadre du second axe, l'analyse statistique de descripteurs de champs d'interaction moléculaire de type CoMSIA et les méthodes de validation des modèles qu'elle génère sont présentées et appliquées à l'étude des relations structure activité en trois dimensions d'une série de 27 analogues de flavonoïdes modulateurs du transporteur ABCG2 (BCRP), impliqué dans le mécanisme de résistance multiple aux anticancéreux que développent les cellules tumorales. La production de modèles statistiquement fiables et performants a permis de concevoir de nouveaux composés biologiquement actifs. modélisation moléculaire amarrage Autodock anticorps polysaccharides mimes peptidiques QSAR3D CoMSIA flavonoïdes résistance multiple (MDR) BCRP ABCG2
7	Computational studies of biomolecules Chen, Sih-Yu January 2017 (has links) In modern drug discovery, lead discovery is a term used to describe the overall process from hit discovery to lead optimisation, with the goal being to identify drug candidates. This can be greatly facilitated by the use of computer-aided (or in silico) techniques, which can reduce experimentation costs along the drug discovery pipeline. The range of relevant techniques include: molecular modelling to obtain structural information, molecular dynamics (which will be covered in Chapter 2), activity or property prediction by means of quantitative structure activity/property models (QSAR/QSPR), where machine learning techniques are introduced (to be covered in Chapter 1) and quantum chemistry, used to explain chemical structure, properties and reactivity. This thesis is divided into five parts. Chapter 1 starts with an outline of the early stages of drug discovery; introducing the use of virtual screening for hit and lead identification. Such approaches may roughly be divided into structure-based (docking, by far the most often referred to) and ligand-based, leading to a set of promising compounds for further evaluation. Then, the use of machine learning techniques, the issue of which will be frequently encountered, followed by a brief review of the "no free lunch" theorem, that describes how no learning algorithm can perform optimally on all problems. This implies that validation of predictive accuracy in multiple models is required for optimal model selection. As the dimensionality of the feature space increases, the issue referred to as "the curse of dimensionality" becomes a challenge. In closing, the last sections focus on supervised classification Random Forests. Computer-based analyses are an integral part of drug discovery. Chapter 2 begins with discussions of molecular docking; including strategies incorporating protein flexibility at global and local levels, then a specific focus on an automated docking program – AutoDock, which uses a Lamarckian genetic algorithm and empirical binding free energy function. In the second part of the chapter, a brief introduction of molecular dynamics will be given. Chapter 3 describes how we constructed a dataset of known binding sites with co-crystallised ligands, used to extract features characterising the structural and chemical properties of the binding pocket. A machine learning algorithm was adopted to create a three-way predictive model, capable of assigning each case to one of the classes (regular, orthosteric and allosteric) for in silico selection of allosteric sites, and by a feature selection algorithm (Gini) to rationalize the selection of important descriptors, most influential in classifying the binding pockets. In Chapter 4, we made use of structure-based virtual screening, and we focused on docking a fluorescent sensor to a non-canonical DNA quadruplex structure. The preferred binding poses, binding site, and the interactions are scored, followed by application of an ONIOM model to re-score the binding poses of some DNA-ligand complexes, focusing on only the best pose (with the lowest binding energy) from AutoDock. The use of a pre-generated conformational ensemble using MD to account for the receptors' flexibility followed by docking methods are termed “relaxed complex” schemes. Chapter 5 concerns the BLUF domain photocycle. We will be focused on conformational preference of some critical residues in the flavin binding site after a charge redistribution has been introduced. This work provides another activation model to address controversial features of the BLUF domain. 572
8	COMPUTATIONAL AND SYNTHETIC STUDIES ON ANTIMETABOLITES FOR ANTICANCER-, ANTIVIRAL-,AND ANTIBIOTIC DRUG DISCOVERY Tiwari, Rohit 23 August 2010 (has links) No description available. Chemistry Organic Chemistry Radiation Carborane Pd-catalyzed isotope exchange reaction Halogen exchange Iodine monochloride Deglycosylation Anomerization Epstein-Barr Virus Human thymidine kinase 1 Autodock Glide FlexX Surflex B-halogenation radiohalogenation
9	Structural Investigation of Processing α-Glucosidase I from Saccharomyces cerevisiae Barker, Megan 20 August 2012 (has links) N-glycosylation is the most common eukaryotic post-translational modification, impacting on protein stability, folding, and protein-protein interactions. More broadly, N-glycans play biological roles in reaction kinetics modulation, intracellular protein trafficking, and cell-cell communications. The machinery responsible for the initial stages of N-glycan assembly and processing is found on the membrane of the endoplasmic reticulum. Following N-glycan transfer to a nascent glycoprotein, the enzyme Processing α-Glucosidase I (GluI) catalyzes the selective removal of the terminal glucose residue. GluI is a highly substrate-specific enzyme, requiring a minimum glucotriose for catalysis; this glycan is uniquely found in biology in this pathway. The structural basis of the high substrate selectivity and the details of the mechanism of hydrolysis of this reaction have not been characterized. Understanding the structural foundation of this unique relationship forms the major aim of this work. To approach this goal, the S. cerevisiae homolog soluble protein, Cwht1p, was investigated. Cwht1p was expressed and purified in the methyltrophic yeast P. pastoris, improving protein yield to be sufficient for crystallization screens. From Cwht1p crystals, the structure was solved using mercury SAD phasing at a resolution of 2 Å, and two catalytic residues were proposed based upon structural similarity with characterized enzymes. Subsequently, computational methods using a glucotriose ligand were applied to predict the mode of substrate binding. From these results, a proposed model of substrate binding has been formulated, which may be conserved in eukaryotic GluI homologs. Biomolecular structure Eukaryotic glycosylation glycosylation N-glycan N-linked glycosylation Carbohydrate biochemistry Inhibitor antiviral therapeutic Structure-based drug design glucosidase glycosidase glycoside hydrolase post-translational modification enzyme enzyme mechanism inverting mechanism structural biology protein structure 6xHis tag Crystal packing sugar substrate X-ray crystallography single anomalous dispersion PHENIX heavy-atom phasing docking autodock autodock vina biophysical methods tryptophan fluorescence glucose oxidase activity assay enzyme inhibition Protein expression Protein purification Pichia pastoris Saccharomyces cerevisiae fondue AOX1 promoter glycoside hydrolysis substrate binding model molecular dynamics molecular dynamics Binding site Active site cleft Endoplasmic reticulum Glycan processing Glycan trimming protein-protein interactions cell-cell communication X-ray diffraction crystallization secreted protein substrate selectivity kojibiose glucotriose miglitol deoxynojirimycin Glc3Man9GlcNAc2 free oligosaccharide species GluI Cwh41 Cwh41p Cwht1p 0306 0307 0760
10	Structural Investigation of Processing α-Glucosidase I from Saccharomyces cerevisiae Barker, Megan 20 August 2012 (has links) N-glycosylation is the most common eukaryotic post-translational modification, impacting on protein stability, folding, and protein-protein interactions. More broadly, N-glycans play biological roles in reaction kinetics modulation, intracellular protein trafficking, and cell-cell communications. The machinery responsible for the initial stages of N-glycan assembly and processing is found on the membrane of the endoplasmic reticulum. Following N-glycan transfer to a nascent glycoprotein, the enzyme Processing α-Glucosidase I (GluI) catalyzes the selective removal of the terminal glucose residue. GluI is a highly substrate-specific enzyme, requiring a minimum glucotriose for catalysis; this glycan is uniquely found in biology in this pathway. The structural basis of the high substrate selectivity and the details of the mechanism of hydrolysis of this reaction have not been characterized. Understanding the structural foundation of this unique relationship forms the major aim of this work. To approach this goal, the S. cerevisiae homolog soluble protein, Cwht1p, was investigated. Cwht1p was expressed and purified in the methyltrophic yeast P. pastoris, improving protein yield to be sufficient for crystallization screens. From Cwht1p crystals, the structure was solved using mercury SAD phasing at a resolution of 2 Å, and two catalytic residues were proposed based upon structural similarity with characterized enzymes. Subsequently, computational methods using a glucotriose ligand were applied to predict the mode of substrate binding. From these results, a proposed model of substrate binding has been formulated, which may be conserved in eukaryotic GluI homologs. Biomolecular structure Eukaryotic glycosylation glycosylation N-glycan N-linked glycosylation Carbohydrate biochemistry Inhibitor antiviral therapeutic Structure-based drug design glucosidase glycosidase glycoside hydrolase post-translational modification enzyme enzyme mechanism inverting mechanism structural biology protein structure 6xHis tag Crystal packing sugar substrate X-ray crystallography single anomalous dispersion PHENIX heavy-atom phasing docking autodock autodock vina biophysical methods tryptophan fluorescence glucose oxidase activity assay enzyme inhibition Protein expression Protein purification Pichia pastoris Saccharomyces cerevisiae fondue AOX1 promoter glycoside hydrolysis substrate binding model molecular dynamics molecular dynamics Binding site Active site cleft Endoplasmic reticulum Glycan processing Glycan trimming protein-protein interactions cell-cell communication X-ray diffraction crystallization secreted protein substrate selectivity kojibiose glucotriose miglitol deoxynojirimycin Glc3Man9GlcNAc2 free oligosaccharide species GluI Cwh41 Cwh41p Cwht1p 0306 0307 0760

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