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Shave, Steven R. "Development of high performance structure and ligand based virtual screening techniques." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4333.
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Virtual Sreening (VS) is an in silico technique for drug discovery. An overview of VS methods is given and is seen to be approachable from two sides: structure based and ligand based. Structure based virtual screening uses explicit knowledge of the target receptor to suggest candidate receptor-ligand complexes. Ligand based virtual screening can infer required characteristics of binders from known ligands. A consideration for all virtual screening techniques is the amount of computing time required to arrive at a solution. For this reason, techniques of high performance computing have been applied to both the structural and ligand based approaches. A proven structure based virtual screening code LIDAEUS (Ligand Discovery At Edinburgh University) has been ported and parallelised to a massively parallel computing platform, the University of Edinburgh’s IBM Bluegene/l, consisting of 2,048 processor cores. A challenge in achieving scaling to such a large number of processors required implementation of a minimal communication parallel sort algorithm. Parallel efficiencies achieved within this parallelisation exceeded 99%, confirming that a near optimum strategy has been followed and capacity for running the code on a greater number of processors exists. This implementation of the program has been successfully used with a number of protein targets. The development of a new ligand based virtual screening code has been completed. The program UFSRAT (Ultra Fast Shape Recognition with Atom Types) takes the features of known binders and suggests molecules which will be able to make similar interactions. This similarity method is both fast (1 million molecules per hour per processor) and independent of input orientation. Along with UFSRAT, some other methods (VolRAT and UFSRGraph) based on UFSRAT have been developed, addressing different approaches to ligand based virtual screening. UFSRAT as an approach to discovering novel protein-ligand complexes has been validated with the discovery of a number of inhibitors for 11β-Hydroxysteroid Dehydrogenase type 1 and FK binding protein 12.
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Mazalan, Lucyantie. "Evaluation of similarity measures for ligand-based virtual screening." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/21422/.
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Alaasam, Mohammed. "Identification of novel monoamine oxidase B inhibitors from ligand based virtual screening." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1405439915.
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Heikamp, Kathrin [Verfasser]. "Application and Development of Computational Methods for Ligand-Based Virtual Screening / Kathrin Heikamp." Bonn : Universitäts- und Landesbibliothek Bonn, 2014. http://d-nb.info/1052061036/34.
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Gregori, Puigjané Elisabet. "A new Ligand-Based approach to virtual screening, and prolifing or large chemical libraries." Doctoral thesis, Universitat Pompeu Fabra, 2008. http://hdl.handle.net/10803/7166.
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La representació de les molècules per mitjà de descriptors moleculars és la base de moltes de les eines computacionals pel disseny de fàrmacs. Aquests mètodes computacionals es basen en l'abstracció de l'estructura química per resumir aquelles característiques rellevants sent al mateix temps eficients en la comparació de grans llibreries de molècules. Una característica molt important d'aquests descriptors és la seva habilitat de capturar la informació rellevant per la interacció amb qualsevol proteïna independentment de l'esquelet del compost. Això permet detectar com a similars qualsevol parella de compostos amb les mateixes característiques ordenades de la mateixa manera al voltant d'esquelets essencialment diferents, una propietat a la qual hom es refereix com a "scaffold hopping". Tenint en compte això, un nou conjunt de descriptors basat en la distribució de parelles de característiques farmacofòriques centrades en els àtoms per mitjà del concepte de teoria de la informació de l'entropia de Shannon [1], anomenats SHED, s'han desenvolupat.Aquests descriptors han sigut usats amb èxit en nombroses aplicacions importants en el procés de descoberta de fàrmacs. Després de la implementació de noves tecnologies in vitro com ara el "high-throughput screening" i la química combinatòria, la capacitat de sintetitzar i assajar compostos va augmentar exponencialment però alhora la necessitat d'una selecció racional dels compostos va fer-se patent. La priorització dels compostos en termes de la predicció de la seva probabilitat de mostrar la activitat desitjada és per tant una de les primeres aplicacions del perfilat virtual basat en lligands usant els descriptors SHED.
En realitat, aquesta metodologia es pot estendre al punt de vista quimiogenòmic del procés de descoberta de fàrmacs, usant els descriptors per generar models basats en ligands de totes les proteïnes amb informació de lligands. Aquesta aproximació més ampla, el perfilat virtual de proteïnes, és un pas més per completar la matriu d'activitat entre tots els possibles compostos químics i totes les proteïnes rellevants. A més, una anàlisi més aprofundida d'aquesta matriu completa generada per mitjà del perfilat virtual de proteïnes pot dur-nos a una perspectiva de farmacologia en xarxa del procés de descoberta de fàrmacs. Aquesta direcció pot ser seguida afegint a aquesta informació de lligands i proteïnes la informació relativa a rutes de reaccions i anàlisi de sistemes, donant lloc a l'anomenada biologia química de sistemes que pot ajudar a entendre els processos biològics com un conjunt i a identificar de manera més racional noves i prometedores dianes terapèutiques.
The representation of molecules by means of molecular descriptors is the basis of most of the computational tools for drug design. These computational methods are based on the abstraction from the chemical structure to summarize its relevant features while being efficient in the comparison of large molecule libraries. A very important feature of these descriptors is their ability to capture the information relevant for the interaction with any target independently from the scaffold of the compound. This will allow detecting as similar any two compounds with the same features arranged in the same way around essentially different scaffolds, a property referred to as scaffold hopping. With this in mind, a new set of descriptors based on the distribution of atom-centred pharmacophoric feature pairs by means of the information theory concept of Shannon entropy [1], called SHED, have been developed.
These descriptors have been successfully used in a number of applications important in the drug discovery process. After the implementation of novel in vitro technologies like high-throughput screening and combinatorial chemistry, the capacity of synthesizing and testing compounds increased exponentially but the need for a rational selection of the compounds arose as well. The prioritisation of compounds in terms of their predicted chances of displaying the targeted activity is thus one of the first applications of the ligand-based virtual ligand screening based on SHED descriptors. This application has shown very good results, both in terms of enrichment of actives in the hit list and in terms of scaffold hopping ability, i.e. the novelty of the scaffolds of the found actives in the top ranked compounds.
Actually, this methodology can be extended to a chemogenomics view of the drug discovery process, using the descriptors to build ligand-based models of all the proteins with any ligand information. This broader approach, the virtual target profiling, is a step towards completing the activity matrix between all possible chemical compounds and all relevant targets. Moreover, a deeper analysis of this complete matrix generated by virtual target profiling can lead us to a network pharmacology perspective of the drug discovery process. This direction can be further followed by adding to ligand-target information the information about pathways and systems approaches, leading to a systems chemical biology approach that could help understanding biological processes as a whole and identifying more rationally novel and promising drug targets.
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Tai, Hio Kuan. "Protein-ligand docking and virtual screening based on chaos-embedded particle swarm optimization algorithm." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948431.
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Behren, Mathias Michael von Verfasser], and Matthias [Akademischer Betreuer] [Rarey. "Ligand-based Virtual Screening Utilizing Partial Shape Constraints / Mathias Michael von Behren ; Betreuer: Matthias Rarey." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2017. http://nbn-resolving.de/urn:nbn:de:gbv:18-86060.
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Behren, Mathias Michael von [Verfasser], and Matthias [Akademischer Betreuer] Rarey. "Ligand-based Virtual Screening Utilizing Partial Shape Constraints / Mathias Michael von Behren ; Betreuer: Matthias Rarey." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2017. http://d-nb.info/113732371X/34.
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Santos, Alan Diego dos. "Ranking ligands in structure-based virtual screening using siamese neural networks." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2017. http://tede2.pucrs.br/tede2/handle/tede/7763.
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Triagem virtual de bancos de dados de ligantes ? amplamente utilizada nos est?gios iniciais do processo de descoberta de f?rmacos. Abordagens computacionais ?docam? uma pequena mol?cula dentro do s?tio ativo de um estrutura biol?gica alvo e avaliam a afinidade das intera??es entre a mol?cula e a estrutura. Todavia, os custos envolvidos ao aplicar algoritmos de docagem molecular em grandes bancos de ligantes s?o proibitivos, dado a quantidade de recursos computacionais necess?rios para essa execu??o. Nesse contexto, estrat?gias de aprendizagem de m?quina podem ser aplicadas para ranquear ligantes baseadas na afinidade com determinada estrutura biol?gica e, dessa forma, reduzir o n?mero de compostos qu?micos a serem testados. Nesse trabalho, propomos um modelo para ranquear ligantes baseados na arquitetura de redes neurais siamesas. Esse modelo calcula a compatibilidade entre receptor e ligante usando grades de propriedades bioqu?micas. N?s tamb?m mostramos que esse modelo pode aprender a identificar intera??es moleculares importantes entre ligante e receptor. A compatibilidade ? calculada baseada em rela??o ? conforma??o do ligante, independente de sua posi??o e orienta??o em rela??o ao receptor. O modelo proposto foi treinado usando ligantes ativos previamente conhecidos e mol?culas chamarizes (decoys) em um modelo de receptor totalmente flex?vel (Fully Flexible Receptor - FFR) do complexo InhA-NADH da Mycobacterium tuberculosis, encontrando ?timos resultados.
Structure-based virtual screening (SBVS) on compounds databases has been widely applied in early stage of the drug discovery on drug target with known 3D structure. In SBVS, computational approaches usually ?dock? small molecules into binding site of drug target and ?score? their binding affinity. However, the costs involved in applying docking algorithms into huge compounds databases are prohibitive, due to the computational resources required by this operation. In this context,different types of machine learning strategies can be applied to rank ligands, based on binding affinity,and to reduce the number of compounds to be tested. In this work, we propose a deep learning energy-based model using siamese neural networks to rank ligands. This model takes as inputs grids of biochemical properties of ligands and receptors and calculates their compatibility. We show that the model can learn to identify important biochemical interactions between ligands and receptors. Besides, we demonstrate that the compatibility score is computed based only on conformation of small molecule, independent of its position and orientation in relation to the receptor. The proposed model was trained using known ligands and decoys in a Fully Flexible Receptor model of InhA-NADH complex (PDB ID: 1ENY), having achieved outstanding results.
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Nawsheen, Sabia. "Evaluation of Fragment-Based Virtual Screening by Applying Docking on Fragments obtained from Optimized Ligands." Thesis, Uppsala universitet, Institutionen för läkemedelskemi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446388.
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Fragment-based virtual screening is an in-silico method that potentially identifies new startingpoints for drug molecules and provides an inexpensive and fast exploration of the relevantchemical space compared to its experimental counterpart. It focuses on docking small potentialbinding fragments to a binding pocket and is used to design improved binders by growing thefragments or joining fragments using suitable linkers. In this project, a fragment-based virtualscreening was evaluated by docking 21 fragments that are obtained from 4 different drugs. Here,the fragments were evaluated using SP score in place and SP and XP flexible docking methodsand were compared to the results of the two decoy fragment datasets. Three of the investigatedfragments are positioned at the top and docked with the correct poses and pockets when comparedto the corresponding substructure in the crystal structure and thus could be considered a successfulfragment starting points. Out of the two flexible docking methods used, the SP method providedadditional correct poses and pockets than XP in this limited dataset.
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Nawsheen, Sabia. "Evaluation of Fragment-Based VirtualScreening by Applying Docking onFragments obtained from Optimized Ligands." Thesis, Uppsala universitet, Institutionen för läkemedelskemi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446388.
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Fragment-based virtual screening is an in-silico method that potentially identifies new startingpoints for drug molecules and provides an inexpensive and fast exploration of the relevantchemical space compared to its experimental counterpart. It focuses on docking small potentialbinding fragments to a binding pocket and is used to design improved binders by growing thefragments or joining fragments using suitable linkers. In this project, a fragment-based virtualscreening was evaluated by docking 21 fragments that are obtained from 4 different drugs. Here,the fragments were evaluated using SP score in place and SP and XP flexible docking methodsand were compared to the results of the two decoy fragment datasets. Three of the investigatedfragments are positioned at the top and docked with the correct poses and pockets when comparedto the corresponding substructure in the crystal structure and thus could be considered a successfulfragment starting points. Out of the two flexible docking methods used, the SP method providedadditional correct poses and pockets than XP in this limited dataset.
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Folly, da Silva Constantino Laura. "An effective layered workflow of virtual screening for identification of active ligands of challenging protein targets." Thesis, University of Iowa, 2017. https://ir.uiowa.edu/etd/5754.
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Docking is a computer simulation method used to predict the preferred orientation of two interacting chemical species that has been successfully applied to numerous macromolecules over the years. However, non-traditional targets have inherent difficulties associated with their screening. Large interfaces, lack of obvious binding sites, and transient pockets are some examples. Additionally, most natural ligands of challenging targets are inadequate models for identifying or designing new ligands. Therefore, it is not surprising that customary techniques of structure-based virtual screening are incompatible with these non-traditional targets.
We hypothesized that an integrative virtual screening campaign comprised of docking followed by refinement of best receptor–ligand complexes would effectively identify small-molecule ligands of challenging receptors. We targeted the single-stranded DNA (ssDNA) binding groove of the human RAD52, and a cryptic allosteric pocket of the Helicobacter pylori Glutamate Racemase (GR). In this project, we first determined which docking method was more appropriate for each studied non-traditional target, and then examined how good our two-step docking workflow was in finding novel active ligand scaffolds.
This research developed a powerful layered virtual screening workflow for the discovery of lead compounds against challenging protein targets. Furthermore, we successfully applied a statistical analysis method, which used receiver operating characteristic (ROC) curves, to validate the selected docking protocol that would be used in the screening campaigns. Using the validated workflow, we identified a natural compound that competes with ssDNA to bind to RAD52. The performed screening campaigns also provided new insights into the studied binding pockets, as well as structure-activity relationships (SAR) and binding determinants of the ligands. Our achievements reinforce the power of the ROC curve analysis approach in directing the search for the most appropriate docking protocol and helping to speed up drug discovery in pharmaceutical research.
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AbdulHameed, Mohamed Diwan Mohideen. "COMPUTATIONAL DESIGN OF 3-PHOSPHOINOSITIDE DEPENDENT KINASE-1 INHIBITORS AS POTENTIAL ANTI-CANCER AGENTS." UKnowledge, 2009. http://uknowledge.uky.edu/gradschool_diss/757.
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Computational drug design methods have great potential in drug discovery particularly in lead identification and lead optimization. 3-Phosphoinositide dependent kinase-1 (PDK1) is a protein kinase and a well validated anti-cancer target. Inhibitors of PDK1 have the potential to be developed as anti-cancer drugs. In this work, we have applied various novel computational drug design strategies to design and identify new PDK1 inhibitors with potential anti-cancer activity. We have pursued novel structure-based drug design strategies and identified a new binding mode for celecoxib and its derivatives binding with PDK1. This new binding mode provides a valuable basis for rational design of potent PDK1 inhibitors. In order to understand the structure-activity relationship of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The predictive ability of the developed 3D-QSAR models were validated using an external test set of compounds. An efficient strategy of the hierarchical virtual screening with increasing complexity was pursued to identify new hits against PDK1. Our approach uses a combination of ligand-based and structure-based virtual screening including shape-based filtering, rigid docking, and flexible docking. In addition, a more sophisticated molecular dynamics/molecular mechanics- Poisson-Boltzmann surface area (MD/MM-PBSA) analysis was used as the final filter in the virtual screening. Our screening strategy has led to the identification of a new PDK1 inhibitor. The anticancer activities of this compound have been confirmed by the anticancer activity assays of national cancer institute-developmental therapeutics program (NCI-DTP) using 60 cancer cell lines. The PDK1-inhibitor binding mode determined in this study may be valuable in future de novo drug design. The virtual screening approach tested and used in this study could also be applied to lead identification in other drug discovery efforts.
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Berry, Michael. "Massively-Parallel Computational Identification of Novel Broad Spectrum Antivirals to Combat Coronavirus Infection." University of the Western Cape, 2015. http://hdl.handle.net/11394/8321.
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Philosophiae Doctor - PhDGiven the significant disease burden caused by human coronaviruses, the discovery of an effective antiviral strategy is paramount, however there is still no effective therapy to combat infection. This thesis details the in silica exploration of ligand libraries to identify candidate lead compounds that, based on multiple criteria, have a high probability of inhibiting the 3 chymotrypsin-like protease (3CUro) of human coronaviruses. Atomistic models of the 3CUro were obtained from the Protein Data Bank or theoretical models were successfully generated by homology modelling. These structures served the basis of both structure- and ligand-based drug design studies. Consensus molecular docking and pharmacophore modelling protocols were adapted to explore the ZINC Drugs-Now dataset in a high throughput virtual screening strategy to identify ligands which computationally bound to the active site of the 3CUro . Molecular dynamics was further utilized to confirm the binding mode and interactions observed in the static structure- and ligand-based techniques were correct via analysis of various parameters in a IOns simulation. Molecular docking and pharmacophore models identified a total of 19 ligands which displayed the potential to computationally bind to all 3CUro included in the study. Strategies employed to identify these lead compounds also indicated that a known inhibitor of the SARS-Co V 3CUro also has potential as a broad spectrum lead compound. Further analysis by molecular dynamic simulations largely confirmed the binding mode and ligand orientations identified by the former techniques. The comprehensive approach used in this study improves the probability of identifying experimental actives and represents a cost effective pipeline for the often expensive and time consuming process of lead discovery. These identified lead compounds represent an ideal starting point for assays to confirm in vitro activity, where experimentally confirmed actives will be proceeded to subsequent studies on lead optimization.
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Kumari, Vandana. "Structure-Based Computer Aided Drug Design and Analysis for Different Disease Targets." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1311612599.
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Larsson, Malin. "Computational methods for analyzing dioxin-like compounds and identifying potential aryl hydrocarbon receptor ligands : multivariate studies based on human and rodent in vitro data." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-139487.
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Polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) are omnipresent and persistent environmental pollutants. In particular, 29 congeners are of special concern, and these are usually referred to as dioxin-like compounds (DLCs). In the European Union, the risks associated with DLCs in food products are estimated by a weighted sum of the DLCs’ concentrations. These weights, also called toxic equivalency factors (TEFs), compare the DLCs’ potencies to the most toxic congener, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (2378- TCDD). The toxicological effects of PCDD/Fs and PCBs are diverse, ranging from chloracne and immunological effects in humans to severe weight loss, thymic atrophy, hepatotoxicity, immunotoxicity, endocrine disruption, and carcinogenesis in rodents. Here, the molecular structures of DLCs were used as the basis to study the congeneric differences in in vitro data from both human and rodent cell responses related to the aryl hydrocarbon receptor (AhR). Based on molecular orbital densities and partial charges, we developed new ways to describe DLCs, which proved to be useful in quantitative structure-activity relationship modeling. This thesis also provides a new approach, the calculation of the consensus toxicity factor (CTF), to condense information from a battery of screening tests. The current TEFs used to estimate the risk of DLCs in food are primarily based on in vivo information from rat and mouse experiments. Our CTFs, based on human cell responses, show clear differences compared to the current TEFs. For instance, the CTF of 23478-PeCDF is as high as the CTF for 2378-TCDD, and the CTF of PCB 126 is 30 times lower than the corresponding TEF. Both of these DLCs are common congeners in fish in the Baltic Sea. Due to the severe effects of DLCs and their impact on environmental and human health, it is crucial to determine if other compounds have similar effects. To find such compounds, we developed a virtual screening protocol and applied it to a set of 6,445 industrial chemicals. This protocol included a presumed 3D representation of AhR and the structural and chemical properties of known AhR ligands. This screening resulted in a priority list of 28 chemicals that we identified as potential AhR ligands.
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Totrov, Maxim. "Computational studies on protein-ligand docking." Thesis, Open University, 1999. http://oro.open.ac.uk/58005/.
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This thesis describes the development and refinement of a number of techniques for molecular docking and ligand database screening, as well as the application of these techniques to predict the structures of several protein-ligand complexes and to discover novel ligands of an important receptor protein. Global energy optimisation by Monte-Carlo minimisation in internal co-ordinates was used to predict bound conformations of eight protein-ligand complexes. Experimental X-ray crystallography structures became available after the predictions were made. Comparison with the X-ray structures showed that the docking procedure placed 30 to 70% of the ligand molecule correctly within 1.5A from the native structure. The discrimination potential for identification of high-affinity ligands was derived and optimised using a large set of available protein-ligand complex structures. A fast boundary-element solvation electrostatic calculation algorithm was implemented to evaluate the solvation component of the discrimination potential. An accelerated docking procedure utilising pre-calculated grid potentials was developed and tested. For 23 receptors and 63 ligands extracted from X-ray structures, the docking and discrimination protocol was capable of correct identification of the majority of native receptor-ligand couples. 51 complexes with known structures were predicted. 35 predictions were within 3A from the native structure, giving correct overall positioning of the ligand, and 26 were within 2A, reproducing a detailed picture of the receptor-ligand interaction. Docking and ligand discrimination potential evaluation was applied to screen the database of more than 150000 commercially available compounds for binding to the fibroblast growth factor receptor tyrosine kinase, the protein implicated in several pathological cell growth aberrations. As expected, a number of compounds selected by the screening protocol turned out to be known inhibitors of the tyrosine kinases. 49 putative novel ligands identified by the screening protocol were experimentally tested and five compounds have shown inhibition of phosphorylation activity of the kinase. These compounds can be used as leads for further drug development.
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Capuccini, Marco. "Structure-Based Virtual Screening in Spark." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-257028.
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Buonfiglio, Rosa <1985>. "Computational strategies to include protein flexibility in Ligand Docking and Virtual Screening." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6330/.
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The dynamic character of proteins strongly influences biomolecular recognition mechanisms. With the development of the main models of ligand recognition (lock-and-key, induced fit, conformational selection theories), the role of protein plasticity has become increasingly relevant. In particular, major structural changes concerning large deviations of protein backbones, and slight movements such as side chain rotations are now carefully considered in drug discovery and development. It is of great interest to identify multiple protein conformations as preliminary step in a screening campaign. Protein flexibility has been widely investigated, in terms of both local and global motions, in two diverse biological systems. On one side, Replica Exchange Molecular Dynamics has been exploited as enhanced sampling method to collect multiple conformations of Lactate Dehydrogenase A (LDHA), an emerging anticancer target. The aim of this project was the development of an Ensemble-based Virtual Screening protocol, in order to find novel potent inhibitors. On the other side, a preliminary study concerning the local flexibility of Opioid Receptors has been carried out through ALiBERO approach, an iterative method based on Elastic Network-Normal Mode Analysis and Monte Carlo sampling. Comparison of the Virtual Screening performances by using single or multiple conformations confirmed that the inclusion of protein flexibility in screening protocols has a positive effect on the probability to early recognize novel or known active compounds.
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Müller, Christoph H. P. "Similarity-based virtual screening using inference networks." Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.531182.
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Wang, Shao-Fang. "Biochemical and biophysical studies of MDM2-ligand interactions." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/9527.
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MDM2, murine double minute 2, is a RING type-E3 ligase protein and also an oncogene. MDM2 plays a critical role in determining the steady levels and activity of p53 in cells using two mechanisms. The N-terminal domain of MDM2 binds to the transactivation domain of p53 and inhibits its transcriptional activity. The RING domain of MDM2 plays a role in the ubiquitination (and degradation) of p53. Several proteins are responsible for the ubiquitination mechanism including the ubiquitin-activating enzyme (E1), ubiquitin-conjugating enzyme (E2) and ubiquitin ligase (E3). Since the E2-E3 interaction is essential for ubiquitination, the protein-protein recognition site is a potential drug target. Two different MDM2 RING constructs were expressed and purified: MDM2RING (residues 386-491) and MDM2RING△C (residues 386-478). Both constructs were characterised using dynamic light scattering, size exclusion chromatography, mass spectrometry, NMR and electron microscopy. E3 ligase activity in vitro was also studied. Taken together these results showed that the MDM2RING construct formed a concentration-dependent oligomeric structure. In contrast, the MDM2RING△C construct formed a dimer at all concentrations. Both MDM2RING and MDM2RING △ C retain E3 ligase activity. However, the MDM2RING△C construct is less active. Full length E2 enzyme UbcH5a was also purified. Various biophysical techniques were used to study its interaction with MDM2 as well as with potential small molecule inhibitors as in principle, small molecules which disrupt the interaction between MDM2 and UbcH5a, could prevent/promote ubiquitination of p53. The dimerisation of MDM2 is important for its E3 activity and the C8-binding site potentially provides a second druggable site. In this work, peptide 9, which has the same sequence as the C-terminus of MDMX (an MDM2 homologue) was found to inhibit MDM2 E3 activity. Various biological techniques including NMR, fluorescence anisotropy, and electrospray mass spectrometry were used to investigate the interaction between two inhibitory peptides and MDM2. A major part of project involved virtual screening (VS) to search for small molecules which can affect MDM2-dependent ubiquitination. Three potential targets were considered: (1) the C8-binding site of MDM2; (2) the UbcH5a-binding site of MDM2; and (3) the MDM2-binding site of UbcH5a. Several small molecules were identified using our virtual screening database-mining and docking programs that were shown to affect MDM2-dependent ubiquitination of p53. In terms of understanding the complex biochemical mechanism of MDM2 this work provides two interesting and functionally relevant observations: (i) the MDM2 RING△C construct is a dimer as this would not be expected form the existing studies, and has less E3 ligase activity than MDM2RING; (ii) small molecules that bind MDM2 on the E2 binding site enhanced E3 ligase activity. One model to explain these observations is that binding of small molecule activators family to the RING induces a change in the conformation of the Cterminal tail residues which may enhance E2 binding.
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Schellhammer, Ingo. "Structure based molecule indexing for sublinear virtual screening." Berlin Logos-Verl, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2820891&prov=M&dok_var=1&dok_ext=htm.
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Arif, Shereena M. "Fragment weighting schemes for similarity-based virtual screening." Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540932.
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Hert, JeÌroÌ‚me. "Two-dimensional, similarity-based methods for virtual screening." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425602.
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Schlosser, Jochen [Verfasser]. "Structure-Based Virtual Screening Using Index Technology / Jochen Schlosser." Aachen : Shaker, 2011. http://d-nb.info/1080764321/34.
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Weaver, Shane George Thomas. "Stucture and accessibility based screening of virtual combinatorial libraries." Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417726.
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ten, Brink Tim [Verfasser]. "Automated Structure Preparation and Its Influences on Protein-Ligand Docking and Virtual Screening / Tim ten Brink." Konstanz : Bibliothek der Universität Konstanz, 2011. http://d-nb.info/101745504X/34.
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Schulz, Michèle Nadine. "Fragment based ligand discovery : library design and screening by thermal shift analysis." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/3133/.
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The central idea in Fragment Based Ligand Discovery (FBLD) is to identify small, low molecular weight compounds (MW < 250) that bind to a particular protein active site. Hits can be used to efficiently design larger compounds with the desired affinity and selectivity. Three approaches to FBLD are described in this thesis. The first topic is the development and assessment of different chemoinformatics procedures to select those fragments that maximally represent the chemical features of a larger compound library. Such a fragment library could be of great value in the so-called “SAR by Catalogue" approach, where the initial stage of fragment growth is by selecting existing compounds that contain sub-structures of the hit fragments. Five schemes implemented in the Pipeline Pilot software are described. The second project was to develop improved approaches to processing Thermal Shift Analysis (TSA) data. The shift in melting temperature can indicate that a ligand binds and thus stabilises a protein. A program, MTSA, has been written which allows more straightforward processing of the experimental data than existing available software. However, detailed analysis of fragment screening data highlighted difficulties in defining the melting temperature and suggest that TSA is not sufficiently reliable for routine screening use. Finally, a number of proteins were assessed experimentally for suitability for FBLD: N-myristoyl transferase (NMT), the bacterial homologue of a GlcNAcase enzyme (BtGH84) and the model system hen egg white lysozyme (HEWL). It was not possible to produce suitable NMT material due to the inherent instability of the protein produced in York. The screening results of HEWL with a new Surface Plasmon Resonance (SPR) assay, a cell based activity assay and TSA were inconsistent and difficult to interpret. However, BtGH84 was suitable for screening by both TSA and SPR. The resulting fragment hits are suitable starting points for further evolution.
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Langham, James J. "Discovering drug candidates in virtual chemical libraries : a novel graph-based method for virtual screening /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2006. http://uclibs.org/PID/11984.
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Jacobsson, Micael. "Structure-Based Virtual Screening : New Methods and Applications in Infectious Diseases." Doctoral thesis, Uppsala universitet, Avdelningen för organisk farmaceutisk kemi, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9302.
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A drug discovery project typically starts with a pharmacological hypothesis: that the modulation of a specific molecular biological mechanism would be beneficial in the treatment of the targeted disease. In a small-molecule project, the next step is to identify hits, i.e. molecules that can effect this modulation. These hits are subsequently expanded into hit series, which are optimised with respect to pharmacodynamic and pharmacokinetic properties, through medicinal chemistry. Finally, a drug candidate is clinically developed into a new drug. This thesis concerns the use of structure-based virtual screening in the hit identification phase of drug discovery. Structure-based virtual screening involves using the known 3D structure of a target protein to predict binders, through the process of docking and scoring. Docking is the prediction of potential binding poses, and scoring is the prediction of the free energy of binding from those poses. Two new methodologies, based on post-processing of scoring results, were developed and evaluated using model systems. Both methods significantly increased the enrichment of true positives. Furthermore, correlation was observed between scores and simple molecular properties, and identified as a source of false positives in structure-based virtual screening. Two target proteins, Mycobacterium tuberculosis ribose-5-phosphate isomerase, a potential drug target in tuberculosis, and Plasmodium falciparum spermidine synthase, a potential drug target in malaria, were subjected to docking and virtual screening. Docking of substrates and products of ribose-5-phosphate isomerase led to hypotheses on the role of individual residues in the active site. Additionally, virtual screening was used to predict 48 potential inhibitors, but none was confirmed as an inhibitor or binder to the target enzyme. For spermidine synthase, structure-based virtual screening was used to predict 32 potential active-site binders. Seven of these were confirmed to bind in the active site.
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Wood, David. "The use of kernel-based machine learning algorithms in virtual screening." Thesis, University of Sheffield, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489104.
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The high-throughput technologies of combinatorial chemistry and high-throughput screening have caused an explosion in the amount of data that pharmaceutical companies have available to them in the early stages of drug discovery. These large datasets are frequently analysed with machine learning tools and techniques. In this work, kernel-based machine learning algorithms are assessed and developed for virtual screening purposes using a wide range of molecular representations, and recommendations for improving the accuracy or the activity models are made.
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Xiang, Hua. "Similarity-based virtual screening : effect of the choice of similarity measure." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/5662/.
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The aim of the research was to identify novel similarity measures for similarity-based virtual screening. Similarity-based virtual screening is at the lead identification stage of drug discovery process and normally requires explorations in large scale databases. Thus, the improvement of accuracy of the methods employed could result in a significant enhancement of effectiveness of the whole process of drug discovery. There are three key components involved in similarity-based virtual screening, i.e., structural representations, similarity coefficients and weighting schemes. The research focuses on the choice of similarity coefficient and weighting scheme. Three investigations have been conducted: investigation of interactions between weighting schemes and similarity coefficients; comparison of binary coefficients and evaluation of similarity coefficients using weighted fingerprints. Four chemical databases were used, i.e., MDDR, WOMBAT, MUV and ChEMBL. The results show that there are strong, and often quite subtle, interactions between the similarity coefficient and the weighting scheme comprising a similarity measure. They also exhibit that, although the Tanimoto coefficient remains one of the most practical coefficients for use in similarity-based virtual screening on binary representations, it may not be the coefficient of choice when weighting schemes are applied. In addition, other coefficients were identified as favorable for similarity-based virtual screening when weighted fingerprints are available. The findings indicate that the study of the combinations of weighting schemes and similarity coefficients could make a significant contribution to similarity-based virtual screening.
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Ren, Xin. "Quantitative structure-activity relationship based virtual screening for novel androgen receptor antagonists." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43293.
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Androgen receptor (AR) plays a critical role in prostate cancer development and progression. All
current therapeutic AR inhibitors modulate the receptor via direct binding to its Hormone
Binding Site (HBS). Despite the identification of other small molecule binding areas on the AR
surface including Activation Function 2 (AF2), binding function 3 (BF3), and N-terminal domain
(NTD), HBS continues to be the major target site for AR antagonists (even though this site is
prone to resistant mutations). Thus, there is a high need for the identification and development of
novel antagonists targeting HBS of the AR.
In this study, an effective QSAR modeling pipeline was set up and proved to be capable of
identifying new AR antagonists from a large ZINC collection of purchasable chemicals. In
particular, we have utilized DRAGON, INDUCTIVE and MOE descriptors to create various
binary QSAR models of anti-AR activity. When we have applied the developed QSAR solutions
to screen more than 2 million chemicals from the ZINC database, we were able to identify 39
potential candidate AR HBS binders. When they were tested in the DHT displacement assay, 9
chemicals demonstrated the corresponding IC₅₀ values in efficient low-micromole range. Of
those, 9 compounds later exhibited ability to inhibit AR in the eGFP transcriptional assay with
the IC₅₀ values established at 1.04-16.18 μM level. Notably, 6 discovered chemicals
demonstrated concentration-dependent suppression of survival of LNCaP prostate cancer cell
lines.
The results of this study set a ground for the development of an entire novel chemical class of
AR antagonists that are distinct for the currently marketed drugs such as Nitalutamide,
Flutomide, Cassodex, and MDV3100 that all share significant structural similarity.
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Kirtay, Chrysi. "Development and application of a knowledge-based scoring function for virtual screening." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612957.
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Tunca, Guzin. "A virtual screening procedure combining pharmacophore filtering and molecular docking with the LIE method." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/284031.
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Actualment, el cribratge virtual juga un paper central en el món del descobriment de fàrmacs. L’anàlisi in silico permet el cribatge de milions de molècules petites i la tria de les més prometedores per a les proves experimentals. Per trobar candidats que puguin esdevenir fàrmacs, és crucial reunir una sèrie d’eines computacionals individuals i complementàries. En aquesta tesi, es descriu un procediment automatitzat de cribatge virtual que combina el modelat de farmacòfors i el seu ús en cerques, mètodes d’alt rendiment d’acoblament molecular, puntuació de consens i estimació d'energia lliure d'unió mitjançant el mètode d’energia d'interacció lineal (LIE) a partir de simulacions de dinàmica molecular.
Un dels objectius d'aquesta tesi ha estat el de construir una metodologia flexible i versàtil de cribratge virtual, que permeti la integració de diferents eines en les diferents etapes de l’estudi. El procediment, que es va iniciar com la combinació d'un senzill filtre per tamany, la simulació de l’acoblament molecular i una puntuació de consens, ha derivat en un procediment computacional elaborat i automatitzat amb l'addició de cerques basades en farmacòfor i l'estimació de l'energia lliure d'unió mitjançant el mètode LIE. Aquest mètode integrat té l’objectiu de compensar les debilitats individuals de les diferents tècniques usades i permet avaluar i comparar el rendiment i la l’exactitud d'aquestes tècniques. Una altra fita important ha estat l'aplicació del procediment computacional a proteïnes diana concretes per tal d’avaluar-ne la capacitat de trobar molècules que puguin ser candidats a fàrmacs. Tests experimentals realitzats per a la β-Glucosidasa àcida i la hidrolasa de Bleomicina humanes indiquen que diverses molècules petites seleccionades pel procediment computacional tenen activitat inhibitòria micromolar. El mètode LIE emprat en aquest treball es va aplicar sobre més de deu mil complexos proteïna-lligand per a tres proteïnes diana diferents, el que és, al nostre entendre, la primera aplicació del mètode LIE a aquesta escala.Virtual screening plays a central role in the world of drug discovery today. In silico testing allows to screen millions of small molecules and to choose only the most promising ones for experimental testing. To find potential drug candidates, it is crucial to bring together individual and complementary computational tools. In this thesis, I describe an automated virtual screening procedure that combines pharmacophore modeling and searches, high-throughput molecular docking, consensus scoring and binding free energy estimation with the linear interaction energy (LIE) method through molecular dynamics simulations. One goal of this thesis was to build an evolving and versatile virtual screening methodology, which enables integration of different tools at different steps. The procedure that started as a combination of a simple size filter, molecular docking and consensus scoring, advanced into an elaborate and automated computational workflow with the addition of pharmacophore searches and binding free energy estimation with LIE. This integrated method intends to compensate for weaknesses of individual structure-based techniques and allows the evaluation and comparison of the performance and accuracy of these techniques. Another important goal was to apply the computational workflow to target proteins and find hits that could be drug candidates. Experimental testing performed for human acid β-Glucosidase and bleomycin hydrolase indicate that several small molecules selected by the computational workflow display micromolar inhibitory activity. The standard LIE method used in this work was applied to more than ten thousand ligand-protein complexes for three different targets, which is, to our knowledge, the first time application of LIE at such large scale.
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Jaiyong, Panichakorn. "Computational modelling of ligand shape and interactions for medicines design." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/computational-modelling-of-ligand-shape-and-interactions-for-medicines-design(28d49921-447f-4ea1-aaf2-aa764f45b2f2).html.
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Computational methods have been extensively developed at various levels of approximation in recent years to model biomolecular interactions and for rational drug design. This research work aims to explore the feasibility of using quantum mechanical (QM) methods within the two broad categories of in silico ligand-based and structure-based drug design. First, density functional theory at the M06L level of theory was employed to examine structure-activity relationships of boron-based heterocyclic compounds, anti-inflammatory inhibitors targetting the interleukin-1β (IL-1β) cytokine. Our findings from computed energies and shapes of the molecular orbitals provide understanding of electronic effects associated with the inhibitory activity. We also found that the boron atom, specifically its electrostatic polarity, appears to be essential for the anti-IL-1β activity as evidenced by the biological assay of non-boron analogues selected from the ligand-based virtual screening results. Secondly, we aimed to dock ligands at the active sites of zinc-containing metalloproteins with reasonable computational cost and with accuracy. Therefore, an in-house docking scheme based on a Monte Carlo sampling algorithm using the semiempirical PM6/AMBER force field scoring function was compiled for the first time within the Gaussian 09 program. It was applied to four test cases, docking to cytidine deaminase and human carbonic anhydrase II proteins. The docking results show the method’s promise in resolving false-positive docking poses and improving the predicted binding modes over a conventional docking scheme. Finally, semiempirical QM methods which include dispersion and hydrogen-bond corrections were assessed for modelling conformations of β-cyclodextrin (βCD) and their adsorption on graphene. The closed in vacuo βCD cccw conformer was found to be in the lowest energy, in good agreement with previous ab initio QM studies. DFTB3, PM6-DH2 and PM7 methods were applied to model the intermolecular interactions of large βCD/graphene complexes, over a thousand atoms in size. We found that the binding preference of βCD on graphene is in a closed conformation via its C2C3 rim, agreeing with reported experimental and computational findings.
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Pevzner, Yuri. "Development and application of web-based open source drug discovery platforms." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5550.
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Computational modeling approaches have lately been earning their place as viable tools in drug discovery. Research efforts more often include computational component and the usage of the scientific software is commonplace at more stages of the drug discovery pipeline. However, as software takes on more responsibility and the computational methods grow more involved, the gap grows between research entities that have the means to maintain the necessary computational infrastructure and those that lack the technical expertise or financial means to obtain and include computational component in their scientific efforts. To fill this gap and to meet the need of many, mainly academic, labs numerous community contributions collectively known as open source projects play an increasingly important role. This work describes design, implementation and application of a set of drug discovery workflows based on the CHARMMing (CHARMM interface and graphics) web-server. The protocols described herein include docking, virtual target screening, de novo drug design, SAR/QSAR modeling as well as chemical education. The performance of the newly developed workflows is evaluated by applying them to a number of scientific problems that include reproducibility of crystal poses of small molecules in protein-ligand systems, identification of potential targets of a library of natural compounds as well as elucidating molecular targets of a vitamin. The results of these inquiries show that protocols developed as part of this effort perform comparably to commercial products, are able to produce results consistent with the experimental data and can substantially enrich the research efforts of labs with otherwise little or no computational component.
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Spink, Ian. "Ligand discovery for protein-protein interaction targets using 19F NMR-based screening of novel peptide and fragment libraries." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31536.
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The main aim of this thesis was to discover and design new ligands for difficult, under-explored and clinically relevant protein targets. A number of protein-protein interaction complexes (PPIs) are introduced as the target focus for the methods employed and developed herein. This thesis is separated into two sections to independently address both peptides and small molecules as screening agents. The project examines both approaches through comprehensive library design strategies and screening by NMR spectroscopic methods. ATAD2 is the first PPI investigated and was expressed and purified in good yield and was also isotopically labelled with Nitrogen-15 for enhanced sensitivity and orthogonal ligand and protein-observed NMR methods. A known pentapeptide was synthesised by solid-phase peptide synthesis (SPPS) using Fmoc chemistry for target validation and tool compound development. A one-bead one-compound (OBOC) tripeptide library was synthesised by SPPS in good yield and purity, determined using single-bead labelling techniques with a fluorescent dye (TMR) and HPLC analysis. This library contained 3072 unique tripeptides with 12 central non-natural, lysine derivatives flanked by 16 natural L amino acids. The library screening technique was based on using a fluorescently labelled protein and Confocal Nanoscanning to detect binding. However, fluorescent labelling of ATAD2 was unsuccessful due to difficult protein handling conditions, therefore this library was not screened. The advent of small molecule, high affinity inhibitors of this target protein generated by GSK shifted focus to a different PPI target, the ubiquitin conjugating enzyme, UbE2L3. A novel 'on-protein peptide building' approach was introduced with the aim of screening a library of fluorinated dipeptides and extending the most potent via the 'N' and 'C' terminus to increase the affinity. A proof-of-concept tetrapeptide to survivin was synthesised by SPPS by incorporation of a non-natural, fluorinated amino acid in the known tetrapeptide sequence. This fluorinated derivative showed target binding activity by 19F NMR spectroscopy. The tripeptide and dipeptide truncates were synthesised by SPPS and binding was still observable by 19F NMR. This method was extended to screening a library of synthesised fluorinated dipeptides by 19F NMR against UbE2L3. A single dipeptide was identified with low affinity and the dipeptide was extended C and N terminally by SPPS to increase affinity. However, there were no tripeptides identified for this protein using this method. The proof of concept tetrapeptide was a success, therefore further protein targets are required to conclusively assess the viability of the approach. Fragment based screening is then introduced as a second approach to novel ligand discovery. Coupled with cheminformatics analysis and in silico library design, we created an in-house fluorinated fragment library consisting of 109 fluorinated fragments using three parallel methods. Compounds were purchased and quality checked by LCMS, HPLC and 19F-NMR. These fragment libraries were screened in a 19F NMR assay against the UbE2L3 and NusE/NusB protein targets. In a primary mixture screen, two fragment hits were identified against the NusE/NusB PPI and there were no fragment hits identified against the UbE2L3 protein. The two fragments against NusE/NusB were validated using orthogonal ligand-binding NMR methods. A mini-series, consisting of six commercially available analogues, were purchased and two fragment analogues showed increased affinity and were active against E. coli in a bacterial inhibition assay. The dissociation constants of the six active compounds were determined by 15N-HSQC NMR titration experiments and shown to be in 100-500 μM range. The binding sites of each compound were also determined by 15N-HSQC chemical shift mapping. These fragment hits represent a novel chemical scaffold identified against the NusE/NusB PPI and demonstrate the potential druggability of this new, complex target. The use of fluorine as a sensor for binding detection is evaluated by incorporating into both peptides and fragments. Through the use of novel library design strategies, a campaign to discover novel ligands of difficult protein targets is presented.
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Masuka, Raban Wilfred. "Chemogenomic approaches to drug design : docking-based virtual screening of nematode GPCRs for potential anthelmintic agents." Doctoral thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20968.
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Among common problems affecting human health and veterinary medicines, helmintic infections are major. The pathogens affect 550-750 Million people worldwide, and affect childhood growth, pregnancies, and development of the intellect. Helminths affects the well-being of animals as well including livestock and reduce the animal populations. However, the current anthelmintics are no longer as effective and some strains have developed resistance thus increasing the need for new anthelmintics. Unfortunately, not too much information is available detailing the physiology of helminths. The published genomic sequence of nematode Caenorrhabdtis elegans as well the primary sequence of the FLP18R1 G-Protein Coupled Receptor are available. GPCRs play a significant role as targets for therapeutics and are responsible for signal transduction in cells. Thus, nematode GPCRs offer an alternative target to design new anthelmintics. Unfortunately, very little information exists about these targets and there are no known x-ray or NMR structures. In this work, the 3D structure of nematode GPCR receptor (FLP18R1) was determined using homology modeling using the beta-2-adrenergic receptor as a template. The homology model developed had 24.87 % sequence identity with the template. Explicit membrane molecular dynamic simulations were used to optimize and refine the helices of the model over 100 ns. The homology model was of acceptable quality.
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Alegre, Aragonés Sabina. "Screening of modular sugar derived phosphite-based ligand libraries for m-catalyzed reactions. A green approach to catalysts discovery." Doctoral thesis, Universitat Rovira i Virgili, 2013. http://hdl.handle.net/10803/129285.
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Actualment la creixent demanda de compostos enantiomèricament purs (fàrmacs, productes agroquímics, additius…) ha impulsat el desenvolupament de la catàlisi asimètrica, sobretot emprant compostos organometàl•lics quirals com a catalitzadors. La síntesi de nous lligands quirals és essencial per descobrir bons sistemes catalítics en catàlisi asimètrica. Els sucres són una font important de lligands per l’elevada disponibilitat i baix preu.
Els objectius d’aquesta tesi son el desenvolupament de dues noves llibreries de lligands derivats de sucre. Concretament tioèter-fosfit i furanòsid monofosfit, per la seva aplicació en diverses reaccions asimètriques catalitzades per metall de transició, tals com la hidrogenació d’olefines funcionalitzades catalitzades per rodi, la hidrogenació d’olefines mínimament funcionalitzades catalitzada per iridi, les reacció de substitució al•lílica catalitzades per pal•ladi, i les adicions 1,2 a aldehids catalitzades per níquel.The growing demand for enantiomerically pure compounds has led to important advances in asymmetric catalysis, especially using chiral organometallic compounds. In this context the search of new catalysts is very important, mainly focusing on the properties of the chiral ligands. This has led to the development of new chiral ligands. An important source of chiral ligands is derivatives carbohydrate derivatives because of their high availability, their low cost and their high functionality. The objectives of this thesis are to develop two new chiral ligands carbohydrate derivatives. Specifically thioether-phosphite and furanoside monophosphite, for application in several important asymmetric catalytic reactions as Rh- and Ir-catalyzed hydrogenation of functionalized and unfunctionalized olefins, respectively; Pd-catalyzed allylic substitution; and Ni-catalyzed 1,2-addition of trialkylaluminum reagents to aldehydes.
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Cao, Yu. "I. Synthesis Of Anthraquinone Derivatives For Electron Transfer Studies In DNA. II. Characterization Of The Interaction Between Heme And Proteins." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/chemistry_diss/55.
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Anthraquinone (AQ) derivatives with relatively high reduction potentials have been synthesized to afford good candidates for electron transfer studies in DNA. Electron withdrawing groups on the anthraquinone ring gave derivatives with less negative reduction potentials. The anthraquinone imide (AQI) derivatives had reduction potentials less negative than AQ derivatives. The AQI ring system was subject to base-induced hydrolysis.
Water-soluble sulfonated tetraarylporphyrins have been studied in a wide variety of contexts. Herein, we report the first synthesis of a pentasulfonated porphyrin bearing an internal cyclic sulfone ring. Treatment of 5,10,15,20-tetrakis(4-sulfonatophenyl)porphyrin (TPPS4) with fuming H2SO4 gave a structure consistent with initial sulfonation followed by dehydration to give a sulfone bridge between an ortho-position of one of the phenyl groups and a β-pyrrole position on the porphine ring (TPPS4Sc). The structure was established by ESI-MS and 1HNMR. The Soret absorption is red shifted by about 32 nm compared to that of TPPS4.
Streptococcus pyogenes obtains iron by taking up heme from the environment during infection. One of the heme uptake pathways is the Sia or Hts pathway. The initial protein in this pathway is Shr, which has two heme-binding NEAT domains, NEAT1 nearer the N-terminus, and NEAT2 nearer the C-terminus. We report biophysical characteristics of these two NEAT domains. To assess stability of this domain towards heme release, denaturation studies of the Fe(II) and Fe(III) forms were performed. For each domain, both the Fe(II) and the Fe(III) forms behave similarly in thermal denaturation and guanidinium denaturation. Overall, NEAT2 is more stable than NEAT1. Spectral signatures, sequence alignment and homology modeling for both domains suggest that one of the axial ligands is methionine. NEAT2 autoreduces as the pH increases and autooxidizes as the pH decreases. Heme uptake from the host environment is the only iron acquisition pathway in S. pyogenes; inhibition of this pathway might be an approach to infection control. Compounds that might inhibit the heme uptake pathway were selected via virtual screening.
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Mucs, Daniel. "Computational methods for prediction of protein-ligand interactions." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/computational-methods-for-prediction-of-proteinligand-interactions(33ad0b24-ef7b-4dff-8e28-597a2f34e079).html.
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This thesis contains three main sections. In the first section, we examine methodologies to discriminate Type II protein kinase inhibitors from the Type I inhibitors. We have studied the structure of 55 Type II kinase inhibitors and have notice specific descriptive geometric features. Using this information we have developed a pharmacophore and a shape based screening approach. We have found that these methods did not effectively discriminate between the two inhibitor types used independently, but when combined in a consecutive way – pharmacophore search first, then shape based screening, we have found a method that successfully filtered out all Type I molecules. The effect of protonation states and using different conformer generators were studied as well. This method was then tested on a freely available database of decoy molecules and again shown to be discriminative. In the second section of the thesis, we implement and assess swarm-based docking methods. We implement a repulsive particle swarm optimization (RPSO) based conformational search approach into Autodock 3.05. The performance of this approach with different parameters was then tested on a set of 51 protein ligand complexes. The effect of using different factoring for the cognitive, social and repulsive terms and the importance of the inertia weight were explored. We found that the RPSO method gives similar performance to the particle swarm optimization method. Compared to the genetic algorithm approach used in Autodock 3.05, our RPSO method gives better results in terms of finding lower energy conformations. In the final, third section we have implemented a Monte Carlo (MC) based conformer searching approach into Gaussian03. This enables high level quantum mechanics/molecular mechanics (QM/MM) potentials to be used in docking molecules in a protein active site. This program was tested on two Zn2+ ion-containing complexes, carbonic anhydrase II and cytidine deaminase. The effects of different QM region definitions were explored in both systems. A consecutive and a parallel docking approach were used to study the volume of the active site explored by the MC search algorithm. In case of the carbonic anhydrase II complex, we have used 1,2-difluorobenzene as a ligand to explore the favourable interactions within the binding site. With the cytidine deaminase complex, we have evaluated the ability of the approach to discriminate the native pose from other higher energy conformations during the exploration of the active site of the protein. We find from our initial calculations, that our program is able to perform a conformational search in both cases, and the effect of QM region definition is noticeable, especially in the description of the hydrophobic interactions within the carbonic anhydrase II system. Our approach is also able to find poses of the cytidine deaminase ligand within 1 Å of the native pose.
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Reynolds, Jonathan James. "Structure-based drug discovery against a novel antimalarial drug target, S-adenosylmethionine decarboxylase/ornithine decarboxylase." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/27172.
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Malaria is one of the most life-threatening diseases affecting mankind, with over 3 billion people being at risk of infection, with most of these people living in Africa, South America and Asia. As the malaria parasite is rapidly becoming resistant to many of the possible treatments on the market, it is of upmost importance to identify new possible drug targets and describe drugs against these that are inexpensive, easy to manufacture and have a long shelf-life in order to combat malaria. One such target is the polyamine pathway. The polyamines putrescine, spermidine, and spermine are crucial for cell differentiation and proliferation. Interference with polyamine biosynthesis by inhibition of the rate-limiting enzymes ornithine decarboxylase (ODC) and S-adenosylmethionine decarboxylase (AdoMetDC) has been discussed as a potential chemotherapy of cancer and parasitic infections. Usually, both enzymes are individually transcribed and highly regulated as monofunctional proteins. However, ODC and AdoMetDC from P. falciparum (PfODC and PfAdoMetDC, respectively) are found as a unique bifunctional protein (PfAdoMetDC/ODC) in the malaria parasite, making it an enticing target for new, selective antimalarial chemotherapies. In order to apply structure-based drug discovery strategies to design inhibitors for PfAdoMetDC/ODC, the atomic resolution structures of these proteins are needed. Each individual domain has had its structure proposed through homology modelling; however atomic resolution structures of these domains are not yet available. The homology model of PfAdoMetDC/ODC has not yet been elucidated due to the interactions between the domains of the bifunctional protein not being fully understood. High levels of recombinant expression of the bifunctional protein have been either unsuccessful or resulted in the formation of insoluble proteins being produced. The purpose of this project is to optimise the recombinant expression of PfAdoMetDC/ODC, and the PfODC domain, to produce high yields of pure, soluble protein for subsequent atomic resolution structure determination. Ultimately, this will enable the utilisation of PfAdoMetDC/ODC in structure-based drug discovery strategies. Overexpression of P. falciparum proteins in E. coli is notoriously difficult, mainly due to the codon bias between the two species. Comparative studies were performed on four constructs of the PfAdoMetDC/ODC gene, containing either the wild-type, fully codon harmonised, or partially codon harmonised gene sequences to analyse the effect codon harmonisation had on protein expression and activity of both domains of PfAdoMetDC/ODC as well as on the monofunctional PfODC domain. Codon harmonisation did not improve the expression levels or the purity of recombinantly expressed PfAdoMetDC/ODC or the monofunctional PfODC domain. Truncated versions of both proteins, and contamination by the E. coli chaperone proteins DnaK and GroEL, were present in the protein samples even after purification by affinity chromatography. However, codon harmonisation improved the activity levels of the PfAdoMetDC domain, while decreasing the activity of the PfODC domain of PfAdoMetDC/ODC. Harmonisation of the monofunctional PfODC domain resulted in a decrease in the activity of the protein. In order to identify possible inhibitors of the PfODC domain of the bifunctional protein, a structure-based drug discovery study was initiated based on a homology model for PfODC. Four hundred compounds with known antimalarial activity were virtually screened against the PfODC homology model and the top two scoring compounds were selected for enzyme inhibition assays based on their predictive binding affinity against the enzyme, and two medium scoring compounds were selected as controls. Enzyme inhibition studies were performed on the bifunctional PfAdoMetDC/ODC to determine the effect the compounds had on both domains of the protein. Of the compounds assayed one of the compounds significantly reduced the activity levels of both domains of PfAdoMetDC/ODC. Additionally, one compound significantly reduced the activity level of the PfAdoMetDC domain of PfAdoMetDC/ODC. This work therefore contributes towards characterisation of the unique PfAdoMetDC/ODC in malaria parasites as a novel drug target.Dissertation (MSc)--University of Pretoria, 2012.
Biochemistry
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Al-Asri, Jamil [Verfasser]. "Controlling Hyperglycemia: Discovery of Novel Small α-Amylase Inhibitors Using Structure-Based Virtual Screening / Jamil Al-Asri." Berlin : Freie Universität Berlin, 2014. http://d-nb.info/106295016X/34.
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Bérenger, François. "Nouveaux logiciels pour la biologie structurale computationnelle et la chémoinformatique." Thesis, Paris, CNAM, 2016. http://www.theses.fr/2016CNAM1047/document.
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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-coeursThis 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
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Lindh, Martin. "Computational Modelling in Drug Discovery : Application of Structure-Based Drug Design, Conformal Prediction and Evaluation of Virtual Screening." Doctoral thesis, Uppsala universitet, Avdelningen för organisk farmaceutisk kemi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-328505.
Full textAbstract:
Structure-based drug design and virtual screening are areas of computational medicinal chemistry that use 3D models of target proteins. It is important to develop better methods in this field with the aim of increasing the speed and quality of early stage drug discovery. The first part of this thesis focuses on the application of structure-based drug design in the search for inhibitors for the protein 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR), one of the enzymes in the DOXP/MEP synthetic pathway. This pathway is found in many bacteria (such as Mycobacterium tuberculosis) and in the parasite Plasmodium falciparum. In order to evaluate and improve current virtual screening methods, a benchmarking data set was constructed using publically available high-throughput screening data. The exercise highlighted a number of problems with current data sets as well as with the use of publically available high-throughput screening data. We hope this work will help guide further development of well designed benchmarking data sets for virtual screening methods. Conformal prediction is a new method in the computer-aided drug design toolbox that gives the prediction range at a specified level of confidence for each compound. To demonstrate the versatility and applicability of this method we derived models of skin permeability using two different machine learning methods; random forest and support vector machines.
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Mazuela, Aragón Javier. "Design and screening of biaryl phosphite-based ligand libraries for asymmetric reduction and c-c and c-x bond forming reactions." Doctoral thesis, Universitat Rovira i Virgili, 2012. http://hdl.handle.net/10803/96665.
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Durant els últims anys, els compostos fosfit han demostrat ser lligands eficients en diverses reaccions catalitzades por metalls de transició. En aquest context, hem desemvolupat diverses lligandoteques fosfit per la seva aplicació en reaccions per obtenit productes enantiomericament purs. Més concretament hem estudiat:
(a) La síntesis i aplicació de 9 lligandoteques fosfit-nitrogen en l’hidrogenació d’olefines mínimament funcionalizades catalitzada per iridi i les reaccions de substitució al•lílica i Heck catalitzades per pal•ladi. Aquests lligands s’han dissenyat mitjançant variacions sistemàtiques de diversos paràmetres del lligand. En tots els casos s’han obtingut activitats i selectivitats excel•lents (ee’s superiors als 99%) per un ampli rang de substrats. Els resultats competeixen favorablement amb els publicats prèviament en la bibliografia.
(b) L’aplicació de diversos tipus de lligandoteques fosfit en la hidrofomilació de vinilarens, olefines heterocícliques i enol esters terminals catalitzada per rodi obtenint resultats prometedors (ee’ de fins el 76%).During the last years, phosphite-containing compounds have proved to be efficient ligands for several metal-catalyzed transformations. In this context, we have developed several phosphite-containing ligand libraries for their application in reactions leading to enantiomerically pure products. More concretely we have studied: (a) the synthesis and screening of 9 phosphite-nitrogen ligand libraries in the Ir-catalyzed hydrogenation of minimally functionalized olefins, Pd-catalyzed allylic substitution and Heck reactions. These ligand libraries have been designed by systematic modification of several ligand parameters. In all cases excellent activities, regio- and enantioselectivities (ee’s up to >99%) have been obtained for a broad range of substrates. These results compete favorably with those reported previously in the literature. (b) the screening of several types of phosphite containing ligand libraries in the Rh-catalyzed hydroformylation of vinylarenes, heterocyclic olefins and 1,1’-terminal enol esters obtaining promising results (ee’s up to 76%).
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Keränen, Henrik. "Advances in Ligand Binding Predictions using Molecular Dynamics Simulations." Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230777.
Full textAbstract:
Biochemical processes all involve associations and dissociations of chemical entities. Understanding these is of substantial importance for many modern pharmaceutical applications. In this thesis, longstanding problems with regard to ligand binding are treated with computational methods, applied to proteins of key pharmaceutical importance. Homology modeling, docking, molecular dynamics simulations and free-energy calculations are used here for quantitative characterization of ligand binding to proteins. By combining computational tools, valuable contributions have been made for pharmaceutically relevant areas: a neglected tropical disease, an ion channel anti-drug-target, and GPCR drug-targets. We report three compounds inhibiting cruzain, the main cysteine protease of the protozoa causing Chagas’ disease. The compounds were found through an extensive virtual screening study and validated with experimental enzymatic assays. The compounds inhibit the enzyme in the μM-range and are therefore valuable in further lead optimization studies. A high-resolution crystal structure of the BRICHOS domain is reported, together with molecular dynamics simulations and hydrogen-deuterium exchange mass spectrometry studies. This work revealed a plausible mechanism for how the chaperone activity of the domain may operate. Rationalization of structure-activity relationships for a set of analogous blockers of the hERG potassium channel is given. A homology model of the ion channel was used for docking compounds and molecular dynamics simulations together with the linear interaction energy method employed for calculating the binding free-energies. The three-dimensional coordinates of two GPCRs, 5HT1B and 5HT2B, were derived from homology modeling and evaluated in the GPCR Dock 2013 assessment. Our models were in good correlation with the experimental structures and all of them placed among the top quarter of all models assessed. Finally, a computational method, based on molecular dynamics free-energy calculations, for performing alanine scanning was validated with the A2A adenosine receptor bound to either agonist or antagonist. The calculated binding free-energies were found to be in good agreement with experimental data and the method was subsequently extended to non-alanine mutations. With extensive experimental mutation data, this scheme is a valuable tool for quantitative understanding of ligand binding and can ultimately be used for structure-based drug design.
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Ramamoorthy, Divya. "Design of Novel Inhibitors for Infectious Diseases using Structure-based Drug Design: Virtual Screening, Homology Modeling and Molecular Dynamics." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4393.
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The main aim of the study in this thesis was to use structure-based protocols to design new drugs for enzymes, DXS and DXR in the non mevalonate pathway. Another aim of this study was to identify the dimer interface in E.coli FabH as an allosteric binding site for designing new class of anti-infective drugs. We have attempted to identify potential inhibitors for DXS by docking the NCI Diversity set compounds, compound libraries available from GSK-MMV and St. Jude's Children's research center. FabH dimer interface has been identified as a potential target using SiteMap, Alanine mutagenesis and docking studies.
The first chapter gives an overview of the computational methods. The next two chapters briefly introduce the biological targets in the author's study. Chapter two explains the importance of non-mevalonate pathway in microbes. Different enzymes in the non-mevalonate pathway are discussed and the importance of terpenoids in biological processes and also the use of terpenoids as drugs have been extensively discussed in this chapter. The crystal structures available for DXS and DXR are also discussed. Chapter three brings out the importance of FabH as an anti-infective target. Crystal structure of FabH E.coli is discussed and the importance of FabH as a dimer has been discussed in this chapter.
Chapter 3 describes the methods, homology models generated, and analysis from docking studies. The homology models for PvDXS and PvDXR have been used in this study to identify potential inhibitors. Domain swapping and the structural organization of PvDXS before and after domain swaping are discussed. Identification of domain swaping in PvDXS using entropy changes has been extensively discussed.
Chapter 4 focuses on FabH (Fatty Acid Biosynthesis, enzyme H also referred to as β-ketoacyl-ACP-synthase III) dimer interface as an allosteric target. SiteMap analysis and MD simulations on the FabH monomer and dimer structures revealed the dimer interface as a binding region. Further analyses were done by mutagenesis studies on the Phe87 residue, a key residue at the dimer interface region and validating the results using docking studies. NCI Diversity Set compounds were docked at the dimer interface of FabH, which revealed that compounds NSC91529 and NSC19803 docked best at the dimer interface region with the phenyl ring of both the compounds
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Salentin, Sebastian. "In Silico Identification of Novel Cancer Drugs with 3D Interaction Profiling." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-226435.
Full textAbstract:
Cancer is a leading cause of death worldwide. Development of new cancer drugs is increasingly costly and time-consuming. By exploiting massive amounts of biological data, computational repositioning proposes new uses for old drugs to reduce these development hurdles. A promising approach is the systematic analysis of structural data for identification of shared binding pockets and modes of action.
In this thesis, I developed the Protein-Ligand Interaction Profiler (PLIP), which characterizes and indexes protein-ligand interactions to enable comparative analyses and searching in all available structures. Following, I applied PLIP to identify new treatment options in cancer: the heat shock protein Hsp27 confers resistance to drugs in cancer cells and is therefore an attractive target with a postulated drug binding site. Starting from Hsp27, I used PLIP to define an interaction profile to screen all structures from the Protein Data Bank (PDB). The top prediction was experimentally validated in vitro. It inhibits Hsp27 and significantly reduces resistance of multiple myeloma cells against the chemotherapeutic agent bortezomib.
Besides computational repositioning, PLIP is used in docking, binding mode analysis, quantification of interactions and many other applications as evidenced by over 12,000 users so far. PLIP is provided to the community online and as open source.