Relevant bibliographies by topics / Ligand interaction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ligand interaction'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Ligand interaction"

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van Heerden, Tracey, and Eric van Steen. "Metal–support interaction on cobalt based FT catalysts – a DFT study of model inverse catalysts." Faraday Discussions 197 (2017): 87–99. http://dx.doi.org/10.1039/c6fd00201c.

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It is challenging to isolate the effect of metal–support interactions on catalyst reaction performance. In order to overcome this problem, inverse catalysts can be prepared in the laboratory and characterized and tested at relevant conditions. Inverse catalysts are catalysts where the precursor to the catalytically active phase is bonded to a support-like ligand. We can then view the metal–support interaction as a ligand interaction with the support acting as a supra-molecular ligand. Importantly, laboratory studies have shown that these ligands are still present after reduction of the catalyst. By varying the quantity of these ligands present on the surface, insight into the positive effect SMSI have during a reaction is gained. Here, we present a theoretical study of mono-dentate alumina support based ligands, adsorbed on cobalt surfaces. We find that the presence of the ligand may significantly affect the morphology of a cobalt crystallite. With Fischer–Tropsch synthesis in mind, the CO dissociation is used as a probe reaction, with the ligand assisting the dissociation, making it feasible to dissociate CO on the dense fcc Co(111) surface. The nature of the interaction between the ligand and the probe molecule is characterized, showing that the support-like ligands’ metal centre is directly interacting with the probe molecule.
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Piosik, Jacek, Kacper Wasielewski, Anna Woziwodzka, Wojciech Śledź, and Anna Gwizdek-Wiśniewska. "De-intercalation of ethidium bromide and propidium iodine from DNA in the presence of caffeine." Open Life Sciences 5, no. 1 (February 1, 2010): 59–66. http://dx.doi.org/10.2478/s11535-009-0077-2.

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AbstractCaffeine (CAF) is capable of interacting directly with several genotoxic aromatic ligands by stacking aggregation. Formation of such hetero-complexes may diminish pharmacological activity of these ligands, which is often related to its direct interaction with DNA. To check these interactions we performed three independent series of spectroscopic titrations for each ligand (ethidium bromide, EB, and propidium iodine, PI) according to the following setup: DNA with ligand, ligand with CAF and DNA-ligand mixture with CAF. We analyzed DNA-ligand and ligand-CAF mixtures numerically using well known models: McGhee-von Hippel model for ligand-DNA interactions and thermodynamic-statistical model of mixed association of caffeine with aromatic ligands developed by Zdunek et al. (2000). Based on these models we calculated association constants and concentrations of mixture components using a novel method developed here. Results are in good agreement with parameters calculated in separate experiments and demonstrate de-intercalation of EB and PI molecules from DNA caused by CAF.
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Karasev, Dmitry, Boris Sobolev, Alexey Lagunin, Dmitry Filimonov, and Vladimir Poroikov. "Prediction of Protein–ligand Interaction Based on Sequence Similarity and Ligand Structural Features." International Journal of Molecular Sciences 21, no. 21 (October 31, 2020): 8152. http://dx.doi.org/10.3390/ijms21218152.

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Computationally predicting the interaction of proteins and ligands presents three main directions: the search of new target proteins for ligands, the search of new ligands for targets, and predicting the interaction of new proteins and new ligands. We proposed an approach providing the fuzzy classification of protein sequences based on the ligand structural features to analyze the latter most complicated case. We tested our approach on five protein groups, which represented promised targets for drug-like ligands and differed in functional peculiarities. The training sets were built with the original procedure overcoming the data ambiguity. Our study showed the effective prediction of new targets for ligands with an average accuracy of 0.96. The prediction of new ligands for targets displayed the average accuracy 0.95; accuracy estimates were close to our previous results, comparable in accuracy to those of other methods or exceeded them. Using the fuzzy coefficients reflecting the target-to-ligand specificity, we provided predicting interactions for new proteins and new ligands; the obtained accuracy values from 0.89 to 0.99 were acceptable for such a sophisticated task. The protein kinase family case demonstrated the ability to account for subtle features of proteins and ligands required for the specificity of protein–ligand interaction.
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Ferreira de Freitas, Renato, and Matthieu Schapira. "A systematic analysis of atomic protein–ligand interactions in the PDB." MedChemComm 8, no. 10 (2017): 1970–81. http://dx.doi.org/10.1039/c7md00381a.

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We compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins representing 750 873 protein–ligand atomic interactions, and analyzed the frequency, geometry and the impact of each interaction type. The most frequent ligand–protein atom pairs can be clustered into seven interaction types.
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Marsh, Lorraine. "Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites." BioMed Research International 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/746980.

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Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorableΔG. Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorableΔGbecause of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total bindingΔGarising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo (MCMC) approach is presented. This method yields a metric that quantifies the influence on overall ligand affinity of ligand binding to multiple, distinct sites within a protein binding region. This metric is essentially a measure of dispersion in equilibrium ligand binding and depends on both the number of potential sites of interaction and the distribution of their individual predicted affinities. Analysis of test cases indicates that, for some ligand/protein pairs involving transporters and carrier proteins, diffuse binding contributes greatly to total affinity, whereas in other cases the influence is modest. This approach may be useful for studying situations where “nonspecific” interactions contribute to biological function.
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Kato, Koya, and George Chikenji. "1P266 Development of Ligand Based Virtual Screening considering protein-ligand interaction(22A. Bioinformatics: Structural genomics,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S150. http://dx.doi.org/10.2142/biophys.53.s150_1.

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KROGSDAM, Anne-M., Curt A. F. NIELSEN, Søren NEVE, Dorte HOLST, Torben HELLEDIE, Bo THOMSEN, Christian BENDIXEN, Susanne MANDRUP, and Karsten KRISTIANSEN. "Nuclear receptor corepressor-dependent repression of peroxisome-proliferator-activated receptor δ-mediated transactivation." Biochemical Journal 363, no. 1 (March 22, 2002): 157–65. http://dx.doi.org/10.1042/bj3630157.

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The nuclear receptor corepressor (NCoR) was isolated as a peroxisome-proliferator-activated receptor (PPAR) δ interacting protein using the yeast two-hybrid system. NCoR interacted strongly with the ligand-binding domain of PPARδ, whereas interactions with the ligand-binding domains of PPARγ and PPARα were significantly weaker. PPAR—NCoR interactions were antagonized by ligands in the two-hybrid system, but were ligand-insensitive in in vitro pull-down assays. Interaction between PPARδ and NCoR was unaffected by coexpression of retinoid X receptor (RXR) α. The PPARδ—RXRα heterodimer bound to an acyl-CoA oxidase (ACO)-type peroxisome-proliferator response element recruited a glutathione S-transferase—NCoR fusion protein in a ligand-independent manner. Contrasting with most other nuclear receptors, PPARδ was found to interact equally well with interaction domains I and II of NCoR. In transient transfection experiments, NCoR and the related silencing mediator for retinoid and thyroid hormone receptor (SMRT) were shown to exert a marked dose-dependent repression of ligand-induced PPARδ-mediated transactivation; in addition, transactivation induced by the cAMP-elevating agent forskolin was efficiently reduced to basal levels by NCoR as well as SMRT coexpression. Our results suggest that the transactivation potential of liganded PPARδ can be fine-tuned by interaction with NCoR and SMRT in a manner determined by the expression levels of corepressors and coactivators.
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Beshnova, Daria A., Joana Pereira, and Victor S. Lamzin. "Estimation of the protein–ligand interaction energy for model building and validation." Acta Crystallographica Section D Structural Biology 73, no. 3 (March 1, 2017): 195–202. http://dx.doi.org/10.1107/s2059798317003400.

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Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein–ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode. Here, a new approach, based on the estimation of the major energetic terms of protein–ligand interaction, is introduced for the automated identification of crystallographic ligands in the indicated binding site withARP/wARP. The applicability of the method to the validation of protein–ligand models from the Protein Data Bank is demonstrated by the detection of models that are `questionable' and the pinpointing of unfavourable interatomic contacts.
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Folkertsma, Simon, Paula I. van Noort, Arnold de Heer, Peter Carati, Ralph Brandt, Arie Visser, Gerrit Vriend, and Jacob de Vlieg. "The Use of in Vitro Peptide Binding Profiles and in Silico Ligand-Receptor Interaction Profiles to Describe Ligand-Induced Conformations of the Retinoid X Receptor α Ligand-Binding Domain." Molecular Endocrinology 21, no. 1 (January 1, 2007): 30–48. http://dx.doi.org/10.1210/me.2006-0072.

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Abstract It is hypothesized that different ligand-induced conformational changes can explain the different interactions of nuclear receptors with regulatory proteins, resulting in specific biological activities. Understanding the mechanism of how ligands regulate cofactor interaction facilitates drug design. To investigate these ligand-induced conformational changes at the surface of proteins, we performed a time-resolved fluorescence resonance energy transfer assay with 52 different cofactor peptides measuring the ligand-induced cofactor recruitment to the retinoid X receptor-α (RXRα) in the presence of 11 compounds. Simultaneously we analyzed the binding modes of these compounds by molecular docking. An automated method converted the complex three-dimensional data of ligand-protein interactions into two-dimensional fingerprints, the so-called ligand-receptor interaction profiles. For a subset of compounds the conformational changes at the surface, as measured by peptide recruitment, correlate well with the calculated binding modes, suggesting that clustering of ligand-receptor interaction profiles is a very useful tool to discriminate compounds that may induce different conformations and possibly different effects in a cellular environment. In addition, we successfully combined ligand-receptor interaction profiles and peptide recruitment data to reveal structural elements that are possibly involved in the ligand-induced conformations. Interestingly, we could predict a possible binding mode of LG100754, a homodimer antagonist that showed no effect on peptide recruitment. Finally, the extensive analysis of the peptide recruitment profiles provided novel insight in the potential cellular effect of the compound; for the first time, we showed that in addition to the induction of coactivator peptide binding, all well-known RXRα agonists also induce binding of corepressor peptides to RXRα.
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Rifai, Yusnita. "SEARCH FOR GLIOMA DIRECT BINDING SITE OF ALKALOID USING PROTEIN-LIGAND ANT SYSTEM®." Asian Journal of Pharmaceutical and Clinical Research 11, no. 15 (October 3, 2018): 65. http://dx.doi.org/10.22159/ajpcr.2018.v11s3.30034.

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Objective: This research aims to know the best affinity and the best chemical conformation of anticancer compounds from alkaloid groups that have closed direction to Glioma-associated oncogene using protein-ligand ant system (PLANTS®). The interaction energy and hydrogen bond are included as evaluated targets.Methods: In this research, 27 ligands with root mean square deviation score at 1.614 Å and cyclopamine as native ligand are used. Meanwhile, staurosporinone acts as gliomas directed-binding-site-internal-control. Each ligand is docked in GLI with Protein Data Bank code 2GLI using two methods, GLI contains water and without water.Results: PLANTS® score for native ligand in the first and the second method is −73.9002 and −73.2700, respectively. Pancracristine, homoharringtonine, and sanguinarine showed PLANTS® score closed to the cyclopamine score result, but their hydrogen bond interaction differed from native ligan interaction. Evodiamine ligand has a good score and hydrogen bond to the same amino acid of protein GLI, which are GLU 175 and THR 173. This result indicated that evodiamine has the same identical mechanism as staurosporinone.Conclusion: The evodiamine is determined to have the same working mechanism as a GLI inhibitor.
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Dissertations / Theses on the topic "Ligand interaction"

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Maaß, Christian. "Exploring Metal-Ligand Interactions of Pyrrole Based Pincer Ligands." Doctoral thesis, Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2013. http://hdl.handle.net/11858/00-1735-0000-0022-5E31-1.

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Strömbergsson, Helena. "Chemogenomics: Models of Protein-Ligand Interaction Space." Doctoral thesis, Uppsala universitet, Centrum för bioinformatik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-89299.

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The large majority of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand recognition is thus central to drug discovery and design. Improved experimental techniques have resulted in an immense growth of drug target information. This has stimulated the development of chemogenomics and proteochemometrics (PCM) that take target information as well as ligand information into account to study the genomic effect of potential drugs. This thesis is concerned with modeling protein-ligand recognition, and the aim is to develop models that generalize to the entire protein-ligand space. To this end, protein-ligand interaction data has been extracted and manually curated from public databases, protein and ligand descriptors have been computed, and predictive models have been induced with machine-learning methods. An introduction to chemogenomics, machine learning, and PCM modeling is given in the thesis summary, which is followed by five research papers. Paper I shows that it is possible to induce interpretable models with a non-linear rule-based method, and paper II demonstrates that local descriptors of protein structure may be used to induce PCM models that cover proteins differing in sequence and fold. In paper III, such local descriptors are used to induce a PCM model on a large dataset that includes all major enzyme classes. This demonstrates that the local descriptors may be used to induce generalized models that span the entire known structural enzyme-ligand space. Paper IV describes a step towards proteome-wide PCM models, and shows that it is possible to predict high- and low-affinity complexes using a set of protein and ligand descriptors that do not require knowledge of 3D structure. Finally, paper V presents a method to visualize and compare protein-ligand chemogenomic subspaces, which may be used to predict unwanted cross-interactions of drugs with other proteins in the proteome.
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Zamuner, Stefano. "Ligand-receptor interaction modelling using PET imaging." Thesis, City University London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274523.

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Strömbergsson, Helena. "Chemogenomics : models of protein-ligand interaction space /." Uppsala : Acta Universitatis Upsaliensis, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-89299.

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Li, Jie [Verfasser], and Joachim [Akademischer Betreuer] Spatz. "Cell-ligand interaction study by immobilizing ligand on surface / Jie Li ; Betreuer: Joachim Spatz." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://d-nb.info/1177045443/34.

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Li, Jie [Verfasser], and Joachim P. [Akademischer Betreuer] Spatz. "Cell-ligand interaction study by immobilizing ligand on surface / Jie Li ; Betreuer: Joachim Spatz." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-258062.

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Lo, schiavo Valentina. "Control of ligand-receptor interaction by tuning molecular environment." Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX22109.

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L'adhésion cellulaire est un processus biologique fondamental contrôlé par des liaisons moléculaires spécifiques entre ligands et récepteurs attachés à des surfaces. La formation et la rupture de ces liens dépendent de facteurs cinétiques, mécaniques et structurelles. Le but de ce travail était d'observer comment l'interaction ICAM-1 - anti ICAM-1 pouvait être modifié en jouant i) sur la multivalence de molécules impliquées dans la liaison ii) sur la topographie de surface et iii) sur la mobilité des ligands. A cette fin, on a principalement utilisé une chambre à flux laminaire, complété par une détection de molécule unique par fluorescence.L'étude sur les effets de multivalence, utilisant des monomères et dimères d'ICAM-1, a été réalisée en absence et en présence d'une force mécanique, montrant la plus grande stabilité des liaisons divalentes. En outre, un renforcement avec la force et le temps a été trouvé et décrit avec une fonction à deux paramètres, montrant, pour les liaisons divalentes, un comportement intermédiaire entre rupture parallèles et successives des liaisons. La fréquence d'adhésion des liaisons monovalentes et bivalentes présente différentes valeurs causées par la différence de longueur de ces molécules.Les expériences d'adhésions ont été effectuées en variant la topographie du substrat pour les molécules étudiées. Une comparaison des cinétiques de liaisons sur ces surfaces ne montrent pas de différences soit dans la formation ou dans la rupture. Pour interpréter ces résultats, un modèle qui prend en compte la zone de contact réel devrait être construit à partir des images AFM des échantillons.Dans l'écoulement, le temps de contact entre les molécules est contrôlé par la convection de microsphères. Des résultats récents montrent qu'un minimum de temps est requis pour former la liaison (Robert et al. 2011). Pour tester cette prédiction, les ligands sont ancrés à une bicouche lipidique (SLB) pour étudier comment la diffusion peut modifier l'adhésion. Expérimentalement, les fréquences d'adhésion des liaisons ont montré un comportement similaire pour les SLB fixes et fluides. Toutefois, la simulation numérique prédit un effet sur la formation de la liaison, même lorsque la diffusion des ligands est faible. Il semblerait que la diffusion joue un rôle dans la dissociation de la liaison, réduisant fortement la valeur de koff pour une bicouche fluide. Cet effet peut être expliqué par la présence éventuelle de liaisons multiples dues à l'accumulation des ligands sur la surface de contact
Cell adhesion is a fundamental biological process mediated by specific molecular bonds formed by ligands and receptors attached to surfaces. Formation and rupture of these bonds depend on kinetic, mechanical and structural factors. The goal of this work was to observe how the ICAM-1 – anti ICAM-1 interaction can be modified by playing i) on the multivalency of molecules involved in the bond ii) on the topography of surface and iii) on the mobility of ligands. The main technique used for this purpose was the laminar flow chamber, completed by single-particle tracking in fluorescence.The study on multivalency effects, using monomeric and dimeric ICAM-1, was performed in absence and presence of mechanical force, showing the higher stability of divalent bonds. Also, a force- and time- strengthening dependence was found and described with a two-parameter function, showing, for divalent bonds, an intermediate behaviour between parallel and subsequent rupture of bonds. The adhesion frequency of monovalent and divalent bonds exhibit different values accounted by difference in length of these molecules.Adhesion experiments were performed varying the topography of the substrate for the investigated molecules. A comparison of bond kinetics on these surfaces did not show differences either in attachment or in rupture. To interpret these results, a model which takes into account the real contact area should be built from the AFM images of the samples.In the flow, the contact time between molecules is controlled by convection of microspheres. Recent results show that there is a minimal time required to form the bond (Robert et al. 2011). To test this prediction, ligands were anchored to supported lipid bilayer (SLB) to investigate how the diffusion can modify the adhesion. Experimentally, the adhesion frequencies of the bonds showed similar behaviour for fixed and fluid SLB. While, numerical simulation predicted an effect on bond formation even at low ligand diffusion. The diffusion seemed to play a role in bond dissociation, strongly reducing the value of koff for fluid bilayer. This effect can be explained by the possible presence of multiple bonds due to ligand accumulation on the contact area
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Plouvier, Bertrand. "Analogues thiazoliques de la nétropsine : interaction avec l'ADN et pouvoir cytotoxique." Lille 1, 1991. http://www.theses.fr/1991LIL10119.

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La nétropsine est un antibiotique d'origine naturelle qui se lie spécifiquement aux bases Adénine et Thymine du petit sillon de l'ADN. Des modèles synthétiques où le motif N-méthylpyrrole de la nétropsine est remplacé par un thiazole différemment substitué ont été élaborés. Le mode de liaison à l'ADN de ces analogues de la nétropsine a été étudié par de nombreuses techniques physicochimiques (spectroscopie d'absorption UV, fluorescence, viscosimétrie, dichroïsme linéaire électrique) et de biologie moléculaire (footprinting) pour l'un d'entre eux. En prenant comme molécules de référence l'amsacrine et la bléomycine, substances antitumorales utilisées en clinique, des hybrides répondant au concept peptide à liaison spécifique-intercalant ont été réalisés: ils comprennent dans leur structure une partie pseudopeptidique thiazolique analogue de la nétropsine et l'élément intercalant constitutif de l'amsacrine ou de la bléomycine. Le mode d'interaction à l'ADN et l'activité biologique de tels hybrides ont été étudiés. Cette étude a permis d'élaborer dans un premier temps un composé (Thia-Nt) se liant dans le petit sillon à une séquence oligonucléotidique spécifique et dans un second temps de composés hybrides hybrides possédant une activité antitumorale en partie due au noyau acridinique intercalant de l'amsacrine qu'on leur a greffé
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Tran, Phong Lan Thao. "Quadruplexes de guanines : formation, stabilité et interaction." Thesis, Bordeaux 2, 2011. http://www.theses.fr/2011BOR21888/document.

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Les quadruplexes de guanines (G4) sont des structures non canonique d’acides nucléiques à quatre brins formées à partir de séquences ADN ou ARN riches en guanines. Ces structures reposant sur la formation et l’empilement de quartets de guanines sont très polymorphes, leur formation pourrait être envisagé dans de nombreux domaines d’application, aussi bien pour les biotechnologies que les nanotechnologies. L’étude de G4 tétramoléculaires modifiés présentée dans ce manuscrit a participé à la compréhension du mécanisme d’association de ces complexes. En particulier, nous avons montré que l’insertion de 8-méthyle-2’-déoxyguanosine à l’extrémité 5’ de la séquence favorise l’association et la stabilité du G4. Par ailleurs, l’étude de l’ADN en série L (image de l’ADN naturel dans un miroir) a montré la formation d’un G4 tétramoléculaire avec les mêmes propriétés que son énantiomère, à l’exception de sa chiralité, qui est inversée. L’étude a révélé également une auto-exclusion de deux énantiomères (forme D et forme L) démontrant un assemblage contrôlé des brins parallèles. Ce travail de thèse a aussi permis d’introduire un système simple et stable de visualisation de G4 tétramoléculaire antiparallèle, appelé “ADN synaptique”, sur une nanostructure d’ADN origami. In vivo, ces structures pourraient être impliquées de façon transitoire dans de nombreux processus biologiques, en particulier au niveau des télomères. Nous avons réalisé, au cours de cette thèse, une étude comparative de la structure et de la stabilité des séquences télomériques connues de différents organismes. Cette étude a permis d’enrichir les données nécessaires au développement d’un algorithme prédisant la stabilité de G4. Enfin, nous avons développé une méthode facile et peu coûteuse de criblage (G4-FID) sur plaques 96 puits permettant d’identifier l’interaction de ligands avec différentes séquences biologiques pertinentes. La stabilisation du G4 dans certaines régions du génome via des ligands spécifiques pourrait limiter la prolifération de cellules tumorales et est donc intéressante pour les thérapies anticancéreuses
Guanine quadruplexes (G4) are non-canonical four-stranded nucleic acid structures formed by guanine-rich DNA and RNA sequences. Theses polymorphic structures are built from the stacking of several G-quartets and could be involved in many fields, in biotechnology as well as in nanotechnology. The study of modified tetramolecular G4 presented in this manuscript participated to the understanding of tetramolecular G4 formation. Especially, we showed that the insertion of 8-methyl-2’-deoxyguanosine at the 5’-end of the sequence accelerate G4 formation and increase its stability. Besides, we demonstrate here that short guanine rich L-DNA strands (mirror image of natural DNA) form a tetramolecular G4 with the same properties than their enantiomer, but with opposite chirality. The study revealed also self-exclusion between two enantiomers (D- and L- form), showing the controlled parallel self-assembly of different G-rich strands. This work introduced also a simple and stable system to observe tetramolecular antiparallel G4 formation, called “synaptic DNA”, into a DNA origami nanostructure. In vivo, such structures appear to be implicated in genome dynamics, and especially at telomeres. During this thesis, we dedicated a study to the comparison of G4 folding and stability of known telomeric sequences from different organisms. The present study allowed enriching the dataset necessary to build and refine algorithms predicting G4 stability. Last but not least, we developed a G4 ligand screening method onto 96-well plates allowing the comparison of different biological relevant sequences. The G4 stabilisation by specific ligands in some genome regions may prevent cancer cell proliferation, making it an attractive target for anticancer therapy
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Kerkour, Abdelaziz. "Study of DNA G-quadruplex structures by Nuclear Magnetic Resonance (NMR)." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0292/document.

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Les G-quadruplexes (G4) sont des structures d'acides nucléiques non-canoniques formées par des séquences riches en Guanines (G) principalement localisées dans les telomères et les régions promotrices des oncogènes. Elles sont constituées de l'empilement de plusieurs tétrades de G en présence de cations. En utilisant la spectroscopie par RMN, nous avons caractérisé l'interaction entre le ligand TAP et le G4 télomérique humain constituée de la séquence d(AG3(T2AG3)3). CD et RMN 1D 1H ont été utilisés pour suivre l'interaction entre les deux partenaires. RMN 2D a été utilisé pour attribuer sans ambiguïté toutes les résonances de 1H dans le complexe et d'explorer le site d'interaction. Un modèle illustrant l'interaction de TAP avec 22AG au niveau des sillons et boucles a été généré. Une autre partie de ce travail consiste en l'étude du G4 tétra-moléculaire formé par TG4T et son interaction avec des ligands G4 par la RMN dans les cellules. Des spectres 1H-15N HMQC ont été effectués à l'intérieur de Xenopus laevis et les lysats des cellules HeLa et comparés avec ceux observés dans les conditions in vitro ce qui a montré une bonne stabilité de G4 à l'intérieur de la cellule. En outre, l'interaction de d [TG4T]4 avec des ligands spécifiques de G4 présentant trois différents modes d'interaction a également été étudiée. Le ligand 360A a montré un comportement prometteur. Enfin, dans la dernière partie, différentes séquences de promoteur Kras ont été criblés par RMN pour sélectionner des candidats pour la détermination de structure haute résolution. Deux séquences différentes ont été sélectionnées et caractérisées par spectroscopie CD. La stabilisation des structures G4 formées par ces séquences en interaction avec différents ligands a également été étudiée. Une titration RMN 1D 1H entre 22RT et le ligand Braco19 a montré un comportement intéressant de k-ras G4 par la formation d'espèces intermédiaires lors de l'addition de Braco19
G-quadruplexes (G4) are non-canonical nucleic acid structures formed by G-rich sequences mainly localized in telomeres and promoter regions of oncogenes. They are built from the stacking of several G-quartets in the presence of cations. Using NMR spectroscopy, we have characterized the interaction between the TAP ligand and the human telomeric G4 formed by the sequence d(AG3(T2AG3)3). CD and 1D 1H NMR spectroscopy were used to follow the interaction between the two partners. 2D NMR was used to assign unambiguously all 1H resonances in the complex and to explore the binding site. A model depicting the interaction of TAP with 22AG in grooves and loops was generated. Another part of this work consists in the study of tetramolecular G4 formed by TG4T and its interaction with G4 ligands by in-cell NMR. 1H-15N HMQC spectra were performed inside Xenopus laevis and HeLa cell lysates compared to those observed in vitro conditions showing a good stability of G4 inside the cell. Furthermore, the interaction of d[TG4T]4 with three G4 specific ligands presenting different mode of interaction was also investigated. The ligand 360A showed a promising behavior. Finally, in the last part, different sequences of Kras promoter were screened by NMR to select good candidates for high resolution structure determination. Two different sequences were selected and characterized by CD spectroscopy. The stabilization of G4 structures formed by these sequences in interaction with different ligands was also investigated. A 1D 1H NMR titration between Braco19 and 22RT showed an interesting behavior of k-ras G4 by the formation of intermediate species upon the addition of Braco19
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Books on the topic "Ligand interaction"

1

Eble, Johannes A., and Klaus Kühn. Integrin-Ligand Interaction. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6.

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Roterman-Konieczna, Irena, ed. Identification of Ligand Binding Site and Protein-Protein Interaction Area. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5285-6.

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Kozik, Andrzej. Thiamine-protein interaction: Chemical mechanism of ligand-binding and bioanalytical application of thiamine-binding proteins from seeds. Kraków: Nakł. Uniwersytetu Jagiellońskiego, 1996.

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Russo, Nino, and Dennis R. Salahub, eds. Metal-Ligand Interactions. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0155-1.

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Guschlbauer, Wilhelm, and Wolfram Saenger, eds. DNA—Ligand Interactions. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4684-5383-6.

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Gohlke, Holger, ed. Protein-Ligand Interactions. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645947.

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Williams, Mark A., and Tina Daviter, eds. Protein-Ligand Interactions. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-398-5.

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Russo, Nino, Dennis R. Salahub, and Malgorzata Witko, eds. Metal-Ligand Interactions. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0191-5.

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Nienhaus, G. Ulrich. Protein-Ligand Interactions. New Jersey: Humana Press, 2005. http://dx.doi.org/10.1385/1592599125.

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Daviter, Tina, Christopher M. Johnson, Stephen H. McLaughlin, and Mark A. Williams, eds. Protein-Ligand Interactions. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1197-5.

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Book chapters on the topic "Ligand interaction"

1

Jennissen, Herbert P. "Hydrophobic Interaction Chromatography." In Protein-Ligand Interactions, 81–99. Totowa, NJ: Humana Press, 2005. http://dx.doi.org/10.1007/978-1-59259-912-7_5.

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Eble, Johannes A. "Integrins—A Versatile and Old Family of Cell Adhesion Molecules." In Integrin-Ligand Interaction, 1–40. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6_1.

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Gimond, Clotilde, and Arnoud Sonnenberg. "Activation States of Integrins." In Integrin-Ligand Interaction, 219–40. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6_10.

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David, Frank S., Andreas Kern, and Eugene E. Marcantonio. "Post-Ligand Binding Events: Outside-In Signaling Through the Integrins." In Integrin-Ligand Interaction, 241–51. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6_11.

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Gullberg, Donald, and Peter Ekblom. "Integrins During Development." In Integrin-Ligand Interaction, 253–67. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6_12.

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Kühn, Klaus. "Extracellular Matrix Constituents as Integrin Ligands." In Integrin-Ligand Interaction, 41–83. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6_2.

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Tangemann, Kirsten, and Jürgen Engel. "Binding Studies of Integrins with Their Respective Ligands." In Integrin-Ligand Interaction, 85–100. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6_3.

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Pfaff, Martin. "Recognition Sites of RGD-Dependent Integrins." In Integrin-Ligand Interaction, 101–21. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6_4.

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Eble, Johannes A. "The Ligand Recognition Motifs of α4-Integrins and Leukocyte Integrins." In Integrin-Ligand Interaction, 123–39. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6_5.

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Kühn, Klaus. "Conformation-Dependent Recognition Sites." In Integrin-Ligand Interaction, 141–55. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4064-6_6.

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Conference papers on the topic "Ligand interaction"

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Kanlikilicer, Pinar, Nilay Budeyri, Berna Sariyar Akbulut, Amable Hortacsu, and Elif Ozkirimli Olmez. "Dynamic analysis of ß lactamase ligand interaction." In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130306.

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Liao, Zhirui, Ronghui You, Xiaodi Huang, Xiaojun Yao, Tao Huang, and Shanfeng Zhu. "DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983365.

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Maggi, Norbert, Patrizio Arrigo, and Carmelina Ruggiero. "SNP analysis of Rac1 For personalized ligand interaction." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5626750.

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Wang, Caihua, Juan Liu, Fei Luo, Yafang Tan, Zixin Deng, and Qian-Nan Hu. "Pairwise input neural network for target-ligand interaction prediction." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999129.

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DE STEFANO, LUCA, LUCIA ROTIROTI, IVO RENDINA, LUIGI MORETTI, VIVIANA SCOGNAMIGLIO, MOSÈ ROSSI, and SABATO D'AURIA. "PROTEIN-LIGAND INTERACTION DETECTION BY POROUS SILICON OPTICAL SENSOR." In Proceedings of the 10th Italian Conference. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812833532_0011.

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Silva, Victor Hugo Malamace da, and Glaucio Braga Ferreira. "Chemical interaction study between xanthate ligand and lead (II) using NBO, EDA and QTAIM analysis." In VIII Simpósio de Estrutura Eletrônica e Dinâmica Molecular. Universidade de Brasília, 2020. http://dx.doi.org/10.21826/viiiseedmol2020159.

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Abstract:
As a useful flotation agent, the xanthate ligand, O-alkyldithiocarbonates, has been used by different countries by its easy and inexpensive synthesis. More recently papers explored many different applications using this ligand within a complex of several metals cation. In order to study the proprieties of the lead (II) complex with such ligand, the object of this work is to provide a better understanding of the Pb-S bond using different theoretical approaches as NBO, EDA and QTAIM analysis and the influence caused by the different alkyl groups of the ligand. By an optimized structure, the NBO showed that the Pb-S is mainly composed by p orbital of the lead and by the p lone pair of the sulfur atom. The calculation with different alkyl groups highlights that the presence of a larger hydrocarbon chain provides a higher contribution of the s orbital of the lead atom to the interaction. Through the EDA analysis, the interaction between ligand and metal has the predominance of an electrostatic character. The size of the alkyl group has an impact on the value of both covalent and electrostatic character, making the interaction more covalent, due to a higher presence of an electronic density on sulfur atom. This density can be evaluated by the topological study of the QTAIM analysis, which enhances the fact that the charge over the sulfur atom gets higher when using a larger alkyl group for the xanthate ligand.
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Hu, Fan, Jiaxin Jiang, and Peng Yin. "Interpretable Prediction of Protein-Ligand Interaction by Convolutional Neural Network." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8982989.

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Th. Gomti, Devi. "Role of vibrational spectroscopy in characterization of drug-ligand interaction." In Asian Spectroscopy Conference 2020. Institute of Advanced Studies, Nanyang Technological University, 2020. http://dx.doi.org/10.32655/asc_8-10_dec2020.71.

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Liu, Xiyuan, Ru Zhang, Chandrababu Rejeeth, Sohel Rana, Deepanjali D. Gurav, and Kun Qian. "A Label-Free Electrochemical Biosensor Based on Ligand-Receptor Interaction." In 2018 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO). IEEE, 2018. http://dx.doi.org/10.1109/3m-nano.2018.8552168.

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Jiang, Jiaxin, Fan Hu, Muchun Zhu, and Peng Yin. "A Multi-Task Deep Model for Protein-Ligand Interaction Prediction." In 2019 4th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2019. http://dx.doi.org/10.1109/iciibms46890.2019.8991464.

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Reports on the topic "Ligand interaction"

1

Weinberg, Andrew D. Tumor Specific CD4+ T-Cell Costimulation Through a Novel Receptor/Ligand Interaction. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada374764.

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Weinberg, Andrew D. Tumor Specific CD4+ T-Cell Costimulation Through a Novel Receptor Ligand Interaction. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada359629.

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Weber, George F. Contribution of the Receptor/Ligand Interaction Between CD44 and Osteopontin to Formation of Breast Cancer Metastases. Fort Belvoir, VA: Defense Technical Information Center, July 1999. http://dx.doi.org/10.21236/ada384133.

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Krane, Ian M. Mouse Models of the Neu Ligand Interaction With its Receptor in Mammary Gland Tumorigenesis and Development. Fort Belvoir, VA: Defense Technical Information Center, February 1997. http://dx.doi.org/10.21236/ada326468.

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Leder, Philip, and Ian Krane. Mouse Models of the Neu Ligand Interaction with its Receptor in Mammary Gland Tumorigenesis and Development. Fort Belvoir, VA: Defense Technical Information Center, June 1995. http://dx.doi.org/10.21236/ada297167.

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Weber, Georg. Contribution of the Receptor/Ligand Interaction Between CD44 and Osteopontin to Formation of Breast Cancer Metastases. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada393323.

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Ma, Buyong, and Ruth Nussinov. Computational Study of Cytolytic Peptides: Monomeric-Oligomeric Structures and Ligand Interactions. Fort Belvoir, VA: Defense Technical Information Center, November 2005. http://dx.doi.org/10.21236/ada444931.

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Greenwald, Rebecca. The Role of B7 Ligand Interactions During an In Vivo Mucosal Immune Response. Fort Belvoir, VA: Defense Technical Information Center, June 1998. http://dx.doi.org/10.21236/ad1011831.

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Royer, Lacey. Cul3 Ubiquitin Ligase and Ctb73 Protein Interactions. Portland State University Library, January 2014. http://dx.doi.org/10.15760/honors.48.

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Fagan, Patricia A. NMR studies of DNA oligomers and their interactions with minor groove binding ligands. Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/373863.

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