Suchergebnisse - "Simulaciones de dinámica molecular"

  1. 1
  2. 2

    Weitere Verfasser: University/Department: Universitat Pompeu Fabra. Departament de Medicina i Ciències de la Vida

    Thesis Advisors: Selent, Jana

    Quelle: TDX (Tesis Doctorals en Xarxa)

    Dateibeschreibung: application/pdf

  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13

    Dateibeschreibung: xxii, 147 páginas; application/pdf

    Relation: Schopf, J. W. Fossil evidence of Archaean life. Philos. Trans. R. Soc. B Biol. Sci. 361, 869–885 (2006).; Altermann, W. & Kazmierczak, J. Archean microfossils: A reappraisal of early life on Earth. Res. Microbiol. 154, 611–617 (2003).; Koch, R. Die Ätiologie der Milzbrand-Krankheit, begründet auf die Entwicklungsgeschichte des Bacillus Anthracis. Dtsch. Medizinische Wochenschrift 8, 283 (1882).; Fredricks, D. N. & Relman, D. A. Sequence-based identification of microbial pathogens: A reconsideration of Koch’s postulates. Clin. Microbiol. Rev. 9, 18–33 (1996).; Andrei, S., Valeanu, L., Chirvasuta, R. & Stefan, M.-G. New FDA approved antibacterial drugs: 2015-2017. FDA Approv. Antibact. Drugs 2015–2017.; Aminov, R. I. A brief history of the antibiotic era: Lessons learned and challenges for the future. Front. Microbiol. 1, (2010).; Ventola, C. L. [Review] The antibiotic resistance crisis: part 1: causes and threats. Pharm. Ther. 40, 277–83 (2015).; Peña, C. et al. Carbapenem-resistant Pseudomonas aeruginosa: Factors influencing multidrug-resistant acquisition in non-critically ill patients. Eur. J. Clin. Microbiol. Infect. Dis. 28, 519–522 (2009).; El Zowalaty, M. E. et al. Pseudomonas aeruginosa: Arsenal of resistance mechanisms, decades of changing resistance profiles, and future antimicrobial therapies. Fut. Microbiol. vol. 10 1683–1706 (2015).; Tacconelli, E. et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect. Dis. 18, 318–327 (2018).; Drugs Development & Approval Process. Novel Drug Approvals for 2017. U.S. Food Drug Adm. 1–36 (2017).; Drugs Development & Approval Process. Novel Drug Approvals for 2018. U.S. Food Drug Adm. 1–36 (2018).; Drugs Development & Approval Process. Through Innovation New Drug Therapy Approvals 2019. U.S. Food Drug Adm. 1–44 (2020).; Mullard, A. 2019 FDA drug approvals. Nat. Rev. Drug Discov. 19, 79–84 (2020).; Nealson, K. H., Platt, T. & Hastings, J. W. Cellular control of the synthesis and activity of the bacterial luminescent system. J. Bacteriol. 104, 313–22 (1970).; Kalia, V. C. Quorum Sensing vs Quorum Quenching: A Battle with No End in Sight. (Springer India, 2015).; Pérez-Pérez, M., Jorge, P., Pérez Rodríguez, G., Pereira, M. O. & Lourenço, A. Quorum sensing inhibition in Pseudomonas aeruginosa biofilms: new insights through network mining. Biofouling 33, 128–142 (2017).; Pesci, E. C., Pearson, J. P., Seed, P. C. & Iglewski, B. H. Regulation of las and rhl quorum sensing in Pseudomonas aeruginosa. J. Bacteriol. 179, 3127–32 (1997).; Dubern, J. F. & Diggle, S. P. Quorum sensing by 2-alkyl-4-quinolones in Pseudomonas aeruginosa and other bacterial species. Mol. Biosyst. 4, 882–888 (2008).; Lee, J. et al. A cell-cell communication signal integrates quorum sensing and stress response. Nat. Chem. Biol. 9, 339–343 (2013).; Schuster, M. & Peter Greenberg, E. A network of networks: Quorum-sensing gene regulation in Pseudomonas aeruginosa. Int. J. Med. Microbiol. 296, 73–81 (2006).; Lagarde, N., Zagury, J. F. & Montes, M. Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives. Journal of Chemical Information and Modeling vol. 55 1297–1307 (2015).; Sliwoski, G. R., Meiler, J. & Lowe, E. W. Computational Methods in Drug Discovery Prediction of protein structure and ensembles from limited experimental data View project Antibody modeling, Antibody design and Antigen-Antibody interactions View project. Comput. Methods Drug Discov. 66, 334–95 (2014).; Macalino, S. J. Y., Gosu, V., Hong, S. & Choi, S. Role of computer-aided drug design in modern drug discovery. Arc. of Pharmacal. Res. vol. 38 1686–1701 (2015).; Bajorath, J. Computer-aided drug discovery [ version 1; referees : 3 approved ] Referee Status : 4, 1–8 (2016).; Jorgensen, W. L. The Many Roles of Computation in Drug Discovery. Science vol. 303 1813–1818 (2004).; Aguayo-Ortiz, R. & Fernández-de Gortari, E. Overview of Computer-Aided Drug Design for Epigenetic Targets. in Epi-Informatics 21–52 (Academic Press, 2016).; Brown, N. et al. Big Data in Drug Discovery. in Progress in Medicinal Chemistry vol. 57 277–356 (Elsevier B.V., 2018).; Polanski, J. Big Data in Structure-Property Studies—From Definitions to Models. in 529–552 (2017).; Mendez, D. et al. ChEMBL: Towards direct deposition of bioassay data. Nucleic Acids Res. 47, D930–D940 (2019).; Kim, S. et al. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res. 47, D1102–D1109 (2019).; Wishart, D. S. et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).; Gutmanas, A. et al. PDBe: Protein data bank in Europe. Nucleic Acids Res. 42, (2014).; Treepong, P. et al. Global emergence of the widespread Pseudomonas aeruginosa ST235 clone. Clin. Microbiol. Infect. 24, 258–266 (2018).; Gellatly, S. L. & Hancock, R. E. W. Pseudomonas aeruginosa: New insights into pathogenesis and host defenses. Pathog. Dis. 67, 159–173 (2013).; Moradali, M. F., Ghods, S. & Rehm, B. H. A. Pseudomonas aeruginosa lifestyle: A paradigm for adaptation, survival, and persistence. Front. Cell. Infect. Microbiol. 7, (2017).; Frimmersdorf, E., Horatzek, S., Pelnikevich, A., Wiehlmann, L. & Schomburg, D. How Pseudomonas aeruginosa adapts to various environments: A metabolomic approach. Environ. Microbiol. 12, 1734–1747 (2010).; Migiyama, Y. et al. Pseudomonas aeruginosa bacteremia among immunocompetent and immunocompromised patients: Relation to initialantibiotic therapy and survival. Jpn. J. Infect. Dis. 69, 91–96 (2016).; Hauser, A. R. So Many Virulence Factors, So Little Time. Crit Care Med. 39, 2193–2194 (2012).; Olusegun, A. et al. We are IntechOpen , the world ’ s leading publisher of Open Access books Built by scientists , for scientists TOP 1 %. Intech i, 38 (2012).; Tacconelli, E., Carrara, E., Savoldi, A., Kattula, D. & Burkert, F. GLOBAL PRIORITY LIST OF ANTIBIOTIC-RESISTANT BACTERIA TO GUIDE RESEARCH, DISCOVERY, AND DEVELOPMENT OF NEW ANTIBIOTICS. http://www.cdc.gov/drugresistance/threat-report-2013/.; Centers for Disease Control and Prevention. Multi-drug resistant Pseudomonas aeruginosa infection. (2017).; Suetens, C. et al. Prevalence of healthcare-associated infections, estimated incidence and composite antimicrobial resistance index in acute care hospitals and long-term care facilities: Results from two european point prevalence surveys, 2016 to 2017. Eurosurveillance 23, 1–18 (2018).; Restrepo, M. I. et al. Burden and risk factors for Pseudomonas aeruginosa community-acquired pneumonia: A multinational point prevalence study of hospitalised patients. Eur. Respir. J. 52, (2018).; Instituto Nacional de Salud. Boletín epidemiológico semanal 07 de 2020. Boletín epidemiológico semanal (2020).; Tamber, S. & Hancock, R. E. W. Frontiers in Bioscience 8, s472-483, May 1, 2003] ON THE MECHANISM OF SOLUTE UPTAKE IN PSEUDOMONAS. 472–483 (2003).; Baumgart, A. M. K., Molinari, M. A. & de Silveira, A. C. O. Prevalence of carbapenem resistant pseudomonas aeruginosa and acinetobacter baumannii in high complexity hospital. Brazilian J. Infect. Dis. 14, 433–436 (2010).; Colclough, A. L. et al. RND efflux pumps in Gram-negative bacteria; Regulation, structure and role in antibiotic resistance. Future Microbiol. 15, 143–157 (2020).; Oliver, A., Mulet, X., López-Causapé, C. & Juan, C. The increasing threat of Pseudomonas aeruginosa high-risk clones. Drug Resist. Updat. 21–22, 41–59 (2015).; Poole, K. Stress responses as determinants of antimicrobial resistance in Gram-negative bacteria. Trends Microbiol. 20, 227–234 (2012).; Fernández, L., Breidenstein, E. B. M. & Hancock, R. E. W. Creeping baselines and adaptive resistance to antibiotics. Drug Resist. Updat. 14, 1–21 (2011).; Fraud, S., Campigotto, A. J., Chen, Z. & Poole, K. MexCD-OprJ multidrug efflux system of Pseudomonas aeruginosa: Involvement in chlorhexidine resistance and induction by membrane-damaging agents dependent upon the AlgU stress response sigma factor. Antimicrob. Agents Chemother. 52, 4478–4482 (2008).; Lee, J. Y., Park, Y. K., Chung, E. S., Na, I. Y. & Ko, K. S. Evolved resistance to colistin and its loss due to genetic reversion in Pseudomonas aeruginosa. Sci. Rep. 6, 1–13 (2016).; Fujitani, S., Moffett, K. & Yu, V. Pseudomonas aeruginosa. Infect. Dis. Antimicrob. agents (2018).; Instituto Nacional de Vigilancia de Medicamentos y Alimentos INVIMA. NORMAS FARMACOLÓGICAS. (2020).; Bassetti, M., Vena, A., Croxatto, A., Righi, E. & Guery, B. How to manage Pseudomonas aeruginosa infections. Drugs Context 7, 1–18 (2018).; Public Health England. Antibiotic Awareness: Key Messages 2017 World Antibiotic Awareness Week European Antibiotic Awareness Day Antibiotic Guardian. www.facebook.com/PublicHealthEngland (2017).; Theuretzbacher, U. Antibiotic innovation for future public health needs. Clin. Microbiol. Infect. 23, 713–717 (2017).; Holden, M. T., Diggle, S. P. & Williams, P. Quorum Sensing. in Encyclopedia of Life Sciences (John Wiley & Sons, Ltd, 2007).; Miller, M. B. & Bassler, B. L. Quorum Sensing in Bacteria. Annu. Rev. Microbiol. 55, 165–199 (2001).; Bai Aswathanarayan, J. & Ravishankar Rai, V. Quorum-Sensing Systems in Pseudomonas. (2015).; O’Reilly, M. C. & Blackwell, H. E. Structure-Based Design and Biological Evaluation of Triphenyl Scaffold-Based Hybrid Compounds as Hydrolytically Stable Modulators of a LuxR-Type Quorum Sensing Receptor. ACS Infect. Dis. 2, 32–38 (2016).; Huang, H. et al. An integrated genomic regulatory network of virulence-related transcriptional factors in Pseudomonas aeruginosa. Nat. Commun. 10, (2019).; Lee, J. & Zhang, L. The hierarchy quorum sensing network in Pseudomonas aeruginosa. Protein Cell 6, 26–41 (2015).; Garcïa-Contreras, R. Is quorum sensing interference a viable alternative to treat Pseudomonas aeruginosa infections? Front. Microbiol. 7, 1–7 (2016).; Barr, H. L. et al. Pseudomonas aeruginosa quorum sensing molecules correlate with clinical status in cystic fibrosis. Eur. Respir. J. 46, 1046–1054 (2015).; Taha, M. N., Saafan, A. E., Ahmedy, A., El Gebaly, E. & Khairalla, A. S. Two novel synthetic peptides inhibit quorum sensing-dependent biofilm formation and some virulence factors in Pseudomonas aeruginosa PAO1. J. Microbiol. 57, 618–625 (2019).; Abinaya, M. & Gayathri, M. Inhibition of biofilm formation, quorum sensing activity and molecular docking study of isolated 3, 5, 7-Trihydroxyflavone from Alstonia scholaris leaf against P.aeruginosa. Bioorg. Chem. 87, 291–301 (2019).; Ćirić, A. D. et al. Natural products as biofilm formation antagonists and regulators of quorum sensing functions: A comprehensive review update and future trends. South African Journal of Botany vol. 120 65–80 (2019).; Soukarieh, F. et al. In silico and in vitro-guided identification of inhibitors of alkylquinolone-dependent quorum sensing in pseudomonas aeruginosa. Molecules 23, (2018).; Schütz, C. & Empting, M. Targeting the Pseudomonas quinolone signal quorum sensing system for the discovery of novel anti-infective pathoblockers. Beilstein Journal of Organic Chemistry vol. 14 2627–2645 (2018).; Sampathkumar, S. J., Srivastava, P., Ramachandran, S., Sivashanmugam, K. & Gothandam, K. M. Lutein: A potential antibiofilm and antiquorum sensing molecule from green microalga Chlorella pyrenoidosa. Microb. Pathog. 135, 103658 (2019).; Reina, J. C., Pérez-Victoria, I., Martín, J. & Llamas, I. A Quorum-Sensing Inhibitor Strain of Vibrio alginolyticus Blocks Qs-Controlled Phenotypes in Chromobacterium violaceum and Pseudomonas aeruginosa. Mar. Drugs 17, 494 (2019).; Teerapo, K., Roytrakul, S., Sistayanarain, A. & Kunthalert, D. A scorpion venom peptide derivative BmKn-22 with potent antibiofilm activity against Pseudomonas aeruginosa. PLoS One 14, (2019).; Gómez-Gómez, B. et al. Selenium and tellurium-based nanoparticles as interfering factors in quorum sensing-regulated processes: Violacein production and bacterial biofilm formation. Metallomics 11, 1104–1114 (2019).; Rutherford, S. T. & Bassler, B. L. Bacterial quorum sensing: Its role in virulence and possibilities for its control. Cold Spring Harb. Perspect. Med. 2, 1–25 (2012).; Soheili, V., Tajani, A. S., Ghodsi, R. & Bazzaz, B. S. F. Anti-PqsR compounds as next-generation antibacterial agents against Pseudomonas aeruginosa: A review. Eur. J. Med. Chem. 172, 26–35 (2019).; Maura, D., Hazan, R., Kitao, T., Ballok, A. E. & Rahme, L. G. Evidence for direct control of virulence and defense gene circuits by the pseudomonas aeruginosa quorum sensing regulator, MvfR. Sci. Rep. 6, 1–14 (2016).; Starkey, M. et al. Identification of Anti-virulence Compounds That Disrupt Quorum-Sensing Regulated Acute and Persistent Pathogenicity. PLoS Pathog. 10, (2014).; Wang, J. et al. Bacterial quorum-sensing signal IQS induces host cell apoptosis by targeting POT1-p53 signalling pathway. Cell. Microbiol. 21, e13076 (2019).; López Vallejo, F., Medina Franco, J. L. & Castillo, R. Diseño de fármacos asistido por computadora. Educ. Química 17, 452 (2006).; Saldívar-González, F., Prieto-Martínez, F. D. & Medina-Franco, J. L. Descubrimiento y desarrollo de fármacos: un enfoque computacional. Educ. Quim. 28, 51–58 (2017).; PROTEIN DATA BANK. PDB Statistics: PDB Data Distribution by Residue Count. https://www.rcsb.org/stats/distribution-residue-count (2020).; Meza Menchaca, T., Juárez-Portilla, C. & C. Zepeda, R. Past, Present, and Future of Molecular Docking. in Drug Discovery and Development - New Advances (IntechOpen, 2020).; Berry, M., Fielding, B. & Gamieldien, J. Practical Considerations in Virtual Screening and Molecular Docking. Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology: Algorithms and Software Tools (Elsevier Inc., 2015).; Prieto-Martínez, F. D., Arciniega, M. & Medina-Franco, J. L. Acoplamiento Molecular: Avances Recientes y Retos. TIP Rev. Espec. en Ciencias Químico-Biológicas 21, 65–87 (2018).; Hollingsworth, S. A. & Dror, R. O. Molecular Dynamics Simulation for All. Neuron 99, 1129–1143 (2018).; Azhagiya Singam, E. R., Tachachartvanich, P., La Merrill, M. A., Smith, M. T. & Durkin, K. A. Structural Dynamics of Agonist and Antagonist Binding to the Androgen Receptor. J. Phys. Chem. B 123, 7657–7666 (2019).; An, X. et al. How Does Agonist and Antagonist Binding Lead to Different Conformational Ensemble Equilibria of the κ-Opioid Receptor: Insight from Long-Time Gaussian Accelerated Molecular Dynamics Simulation. ACS Chem. Neurosci. 10, 1575–1584 (2019).; Wang, Y. T. & Chan, Y. H. Understanding the molecular basis of agonist/antagonist mechanism of human mu opioid receptor through gaussian accelerated molecular dynamics method. Sci. Rep. 7, 1–11 (2017).; Kolinski, M. & Filipek, S. Molecular Dynamics of μ Opioid Receptor Complexes with Agonists and Antagonists. Open Struct. Biol. J. 2, 8–20 (2008).; Wiltgen, M. & Tilz, G. P. Homology modelling: Eine übersicht über die methode am beispiel der strukturbestimmung vom diabetes antigen GAD 65. Wiener Medizinische Wochenschrift 159, 112–125 (2009).; Muhammed, M. T. & Aki-Yalcin, E. Homology modeling in drug discovery: Overview, current applications, and future perspectives. Chem. Biol. Drug Des. 93, 12–20 (2019).; Rodriguez, R., Chinea, G., Lopez, N., Pons, T. & Vriend, G. Homology modeling, model and software evaluation: Three related resources. Bioinformatics 14, 523–528 (1998).; Croll, T. I., Sammito, M. D., Kryshtafovych, A. & Read, R. J. Evaluation of template-based modeling in CASP13. Proteins Struct. Funct. Bioinforma. 87, 1113–1127 (2019).; Senior, A. W. et al. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins Struct. Funct. Bioinforma. 87, 1141–1148 (2019).; Liu, T., Lin, Y., Wen, X., Jorissen, R. N. & Gilson, M. K. BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35, 198–201 (2007).; Todeschini, R. & Consonni, V. Handbook of Molecular Descriptors. (John Wiley & Sons, 2008).; Sastry, M., Lowrie, J. F., Dixon, S. L. & Sherman, W. Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J. Chem. Inf. Model. 50, 771–784 (2010).; Bajusz, D., Rácz, A. & Héberger, K. Chemical Data Formats, Fingerprints, and Other Molecular Descriptions for Database Analysis and Searching. in Comprehensive Medicinal Chemistry III (2017).; Ruddigkeit, L., Van Deursen, R., Blum, L. C. & Reymond, J. L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model. 52, 2864–2875 (2012).; Choudhury, C. & Narahari Sastry, G. Pharmacophore Modelling and Screening: Concepts, Recent Developments and Applications in Rational Drug Design. Challenges Adv. Comput. Chem. Phys. 27, 25–53 (2019).; Qing, X. et al. Pharmacophore modeling: Advances, Limitations, And current utility in drug discovery. Journal of Receptor, Ligand and Channel Research vol. 7 81–92 (2014).; Liu, C. et al. Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds. Front. Cell. Infect. Microbiol. 10, 1–12 (2020).; Alpaydin, E. Machine Learning: The New AI. (The MIT Press, 2016).; Mayr, A. et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem. Sci. 9, 5441–5451 (2018).; Naveja, J. J. & Medina-Franco, J. L. Finding Constellations in Chemical Space Through Core Analysis. Front. Chem. 7, 1–10 (2019).; RDKit: Open-source cheminformatics. http://www.rdkit.org (2013).; MolVS: Molecule Validation and Standardization. https://molvs.readthedocs.io/en/latest/index.html (2016).; Lovering, F., Bikker, J. & Humblet, C. Escape from flatland: Increasing saturation as an approach to improving clinical success. J. Med. Chem. 52, 6752–6756 (2009).; Lovering, F. Escape from Flatland 2: Complexity and promiscuity. Medchemcomm 4, 515–519 (2013).; Clemons, P. A. et al. Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles. Proc. Natl. Acad. Sci. U. S. A. 107, 18787–18792 (2010).; Méndez-Lucio, O. & Medina-Franco, J. L. The many roles of molecular complexity in drug discovery. Drug Discovery Today (2017).; Bertz H., S. The First General Index of Molecular Complexity. J. Am. Chem. Soc. 103, 3599–3601 (1981).; Sander, T., Freyss, J., Von Korff, M. & Rufener, C. DataWarrior: An open-source program for chemistry aware data visualization and analysis. J. Chem. Inf. Model. 55, 460–473 (2015).; McKinney, W. Data Structures for Statistical Computing in Python. PROC. 9th PYTHON Sci. CONF. (SCIPY 2010) 51–56 (2010).; Hu, Y., Stumpfe, D. & Bajorath, J. Computational Exploration of Molecular Scaffolds in Medicinal Chemistry. Journal of Medicinal Chemistry (2016).; Stumpfe, D., Dimova, D. & Bajorath, J. Computational method for the systematic identification of analog series and key compounds representing series and their biological activity profiles. J. Med. Chem. 59, 7667–7676 (2016).; Zdrazil, B. & Guha, R. The Rise and Fall of a Scaffold: A Trend Analysis of Scaffolds in the Medicinal Chemistry Literature. J. Med. Chem. 61, 4688–4703 (2018).; Saldívar-González, F. I., Naveja, J. J., Palomino-Hernández, O. & Medina-Franco, J. L. Getting SMARt in drug discovery: Chemoinformatics approaches for mining structure-multiple activity relationships. RSC Advances vol. 7 632–641 (2017).; Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).; Golbamaki, A., Franchi, A. M. & Gini, G. The Maximum Common Substructure (MCS) Search as a New Tool for SAR and QSAR. in Advances in QSAR Modeling. Challenges and Advances in Computational Chemistry and Physics 149–165 (2017).; Xu, Y. J. & Johnson, M. Using molecular equivalence numbers to visually explore structural features that distinguish chemical libraries. J. Chem. Inf. Comput. Sci. 42, 912–926 (2002).; Maggiora, G. M. On outliers and activity cliffs - Why QSAR often disappoints. Journal of Chemical Information and Modeling vol. 46 1535 (2006).; Cruz-Monteagudo, M. et al. Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde? Drug Discov. Today 19, 1069–1080 (2014).; Hu, Y. & Bajorath, J. Activity profile relationships between structurally similar promiscuous compounds. Eur. J. Med. Chem. 69, 393–398 (2013).; Medina-Franco, J. L. Activity cliffs: Facts or artifacts? Chem. Biol. Drug Des. 81, 553–556 (2013).; Medina-Franco, J. L. et al. Characterization of activity landscapes using 2D and 3D similarity methods: Consensus activity cliffs. J. Chem. Inf. Model. (2009).; Sud, M. MayaChemTools: An Open Source Package for Computational Drug Discovery. J. Chem. Inf. Model. 56, 2292–2297 (2016).; Saldívar-González, F. I., Lenci, E., Trabocchi, A. & Medina-Franco, J. L. Exploring the chemical space and the bioactivity profile of lactams: a chemoinformatic study. RSC Adv. 9, 27105–27116 (2019).; Firdaus Begam, B., Begam, B. F. & Kumar, J. S. Visualization of Chemical Space Using Principal Component Analysis. World Appl. Sci. J. 29, 53–59 (2014).; Karlov, D. S., Sosnin, S., Tetko, I. V. & Fedorov, M. V. Chemical space exploration guided by deep neural networks. RSC Adv. 9, 5151–5157 (2019).; De La Vega De León, A. & Bajorath, J. Chemical space visualization: Transforming multidimensional chemical spaces into similarity-based molecular networks. Future Med. Chem. 8, (2016).; Pettersen, E. F. et al. UCSF Chimera?A visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).; Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).; Wu, S., Skolnick, J. & Zhang, Y. Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol. 5, 1–10 (2007).; Ovchinnikov, S., Park, H., Kim, D. E., DiMaio, F. & Baker, D. Protein structure prediction using Rosetta in CASP12. Proteins Struct. Funct. Bioinforma. 86, 113–121 (2018).; Meier, A. & Söding, J. Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling. PLoS Comput. Biol. 11, (2015).; Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis Lawrence. Nat. Protoc. 10, 845–858 (2016).; Waterhouse, A. et al. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).; Shapovalov, M. V. & Dunbrack Jr., R. L. A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions Maxim. 19, 844–858 (2012).; Gasteiger, J. & Marsili, M. A new model for calculating atomic charges in molecules. Tetrahedron Lett. 19, 3181–3184 (1978).; Halgren, T. a. Merck Molecular Force Field. J. Comput. Chem. 17, 490–519 (1996).; Trott, O. & Olson, A. J. Software news and update AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).; Salentin, S., Schreiber, S., Haupt, V. J., Adasme, M. F. & Schroeder, M. PLIP: Fully automated protein-ligand interaction profiler. Nucleic Acids Res. 43, W443–W447 (2015).; Hunter, J. D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 9, 90–95 (2007).; Bergdorf, M. et al. Desmond / GPU Performance as of November 2016. 2015, 1–11 (2016).; Humphrey, W., Dalke, A. & Schulten, K. VMD -- Visual Molecular Dynamics. J. Mol. Graph. 14, 33–38 (1996).; Gowers, R. et al. MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations. Proc. 15th Python Sci. Conf. 98–105 (2016).; Seyler, S. L., Kumar, A., Thorpe, M. F. & Beckstein, O. Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways. PLoS Comput. Biol. 11, 1–36 (2015).; Chemical Computing Group ULC. Molecular Operating Environment (MOE). (2020).; Ali, M. PyCaret: An open source, low-code machine learning library in Python. (2020).; O’Reilly, M. C. et al. Structural and Biochemical Studies of Non-native Agonists of the LasR Quorum-Sensing Receptor Reveal an L3 Loop “Out” Conformation for LasR. Cell Chem. Biol. 25, 1128-1139.e3 (2018).; Wermuth, C. G. The Practice of Medicinal Chemistry, 3rd Ed. 982 (2011).; Hao, M., Bryant, S. H. & Wang, Y. Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem. J. Cheminform. 8, 37 (2016).; Boursier, M. E., Combs, J. B. & Blackwell, H. E. N -Acyl l -Homocysteine Thiolactones Are Potent and Stable Synthetic Modulators of the RhlR Quorum Sensing Receptor in Pseudomonas aeruginosa. ACS Chem. Biol. 14, 186–191 (2019).; Eibergen, N. R., Moore, J. D., Mattmann, M. E. & Blackwell, H. E. Potent and Selective Modulation of the RhlR Quorum Sensing Receptor by Using Non-native Ligands: An Emerging Target for Virulence Control in Pseudomonas aeruginosa. Chembiochem 16, 2348–56 (2015).; Ahumedo Monterrosa, M., Galindo, J. F., Vergara Lorduy, J., Alí-Torres, J. & Vivas-Reyes, R. The role of LasR active site amino acids in the interaction with the Acyl Homoserine Lactones (AHLs) analogues: A computational study. J. Mol. Graph. Model. 86, 113–124 (2019).; Bateman, A. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).; Cavasotto, C. N. & Phatak, S. S. Homology modeling in drug discovery: current trends and applications. Drug Discovery Today vol. 14 676–683 (2009).; Kim, T. et al. Structural insights into the molecular mechanism of Escherichia coli SdiA, a quorum-sensing receptor. Acta Crystallogr. Sect. D Biol. Crystallogr. 70, 694–707 (2014).; Nguyen, Y. et al. Structural and mechanistic roles of novel chemical ligands on the SdiA quorum-sensing transcription regulator. MBio 6, 1–10 (2015).; Lintz, M. J., Oinuma, K. I., Wysoczynski, C. L., Greenberg, E. P. & Churchill, M. E. A. Crystal structure of QscR, a Pseudomonas aeruginosa quorum sensing signal receptor. Proc. Natl. Acad. Sci. U. S. A. 108, 15763–15768 (2011).; Paczkowski, J. E. et al. An Autoinducer Analogue Reveals an Alternative Mode of Ligand Binding for the LasR Quorum-Sensing Receptor. ACS Chem. Biol. 14, 378–389 (2019).; Ramachandran, G. N., Ramakrishnan, C. & Sasisekharan, V. Stereochemistry of polypeptide chain configurations. J. Mol. Biol. 7, 95–99 (1963).; Studer, G. et al. QMEANDisCo—distance constraints applied on model quality estimation. Bioinformatics 36, 1765–1771 (2020).; Wallner, B. & Elofsson, A. Can correct protein models be identified? Protein Sci. 12, 1073–1086 (2003).; Chowdhury, N. & Bagchi, A. Identification of ligand binding activity and DNA recognition by RhlR protein from opportunistic pathogen Pseudomonas aeruginosa—a molecular dynamic simulation approach. J. Mol. Recognit. 31, 1–7 (2018).; Kim, H. et al. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. Biotechnol. Bioprocess Eng. 25, 895–930 (2020).; Yang, M. et al. Machine Learning Models Based on Molecular Fingerprints and an Extreme Gradient Boosting Method Lead to the Discovery of JAK2 Inhibitors. J. Chem. Inf. Model. 59, 5002–5012 (2019).; Fernández, A. et al. Learning from Imbalanced Data Sets. Learning from Imbalanced Data Sets (Springer International Publishing, 2018).; Chawla, N. V. Data Mining for Imbalanced Datasets: An Overview. in Data Mining and Knowledge Discovery Handbook 853–867 (Springer-Verlag, 2006).; Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002).; Ji, X., Tong, W., Liu, Z. & Shi, T. Five-Feature Model for Developing the Classifier for Synergistic vs. Antagonistic Drug Combinations Built by XGBoost. Front. Genet. 10, 600 (2019).; Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining vols 13-17-August-2016 785–794 (Association for Computing Machinery, 2016).; Vass, M. et al. Molecular interaction fingerprint approaches for GPCR drug discovery. Curr. Opin. Pharmacol. 30, 59–68 (2016).; Velázquez-Libera, J. L., Rossino, G., Navarro-Retamal, C., Collina, S. & Caballero, J. Docking, Interaction Fingerprint, and Three-Dimensional Quantitative Structure–Activity Relationship (3D-QSAR) of Sigma1 Receptor Ligands, Analogs of the Neuroprotective Agent RC-33. Front. Chem. 7, (2019).; Khan, M. F., Tang, H., Lyles, J. T. & Pineau, R. Antibacterial Properties of Medicinal Plants From Pakistan Against Multidrug-Resistant ESKAPE. 9, 1–17 (2018).; Atef, N. M., Shanab, S. M., Negm, S. I. & Abbas, Y. A. Evaluation of antimicrobial activity of some plant extracts against antibiotic susceptible and resistant bacterial strains causing wound infection. 9, (2019).; Aparicio, R. & Velasco, J. Composición química y actividad antibacteriana del aceite esencial de los frutos de una nueva especie del género. 158–174 (2017).; https://repositorio.unal.edu.co/handle/unal/80376; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

  14. 14
  15. 15

    Dateibeschreibung: xx, 123 páginas; application/pdf

    Relation: Abiko, L. A. (2015). Estudo da dinâmica funcional dos domínios regulatórios do trocador de Na + /Ca 2+ de Drosophila melanogaster por Ressonância Magnética Nuclear em Solução. UNIVERSIDADE DE SÃO PAULO.; Abiko, L. A., Vitale, P. M., Favaro, D. C., Hauk, P., Li, D.-W., Yuan, J., … Brüschweiler, R. (2016). Model for the allosteric regulation of the Na + /Ca 2+ exchanger NCX. Proteins, 84(5), 580–590. https://doi.org/10.1002/prot.25003; Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindah, E. (2015). Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1–2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001; Allnér, O., Foloppe, N., & Nilsson, L. (2015). Motions and entropies in proteins as seen in NMR relaxation experiments and molecular dynamics simulations. Journal of Physical Chemistry B, 119(3), 1114–1128. https://doi.org/10.1021/jp506609g; Bagur, R., & Hajnóczky, G. (2017). Intracellular Ca2+ Sensing: Its Role in Calcium Homeostasis and Signaling. Molecular Cell, 66(6), 780–788. https://doi.org/10.1016/j.molcel.2017.05.028; Baig, M. H., Sudhakar, D. R., Kalaiarasan, P., Subbarao, N., Wadhawa, G., Lohani, M., … Khan, A. U. (2014). Insight into the effect of inhibitor resistant S130G mutant on physicochemical properties of SHV type beta-lactamase: A molecular dynamics study. PLoS ONE, 9(12), 1–19. https://doi.org/10.1371/journal.pone.0112456; Banci, L. (2003). Molecular dynamics simulations of metalloproteins. Current Opinion in Chemical Biology, 7(1), 143–149. https://doi.org/10.1016/S1367-5931(02)00014-5; Bax, A., & Grishaev, A. (2005). Weak alignment NMR: A hawk-eyed view of biomolecular structure. Current Opinion in Structural Biology, 15(5), 563–570. https://doi.org/10.1016/j.sbi.2005.08.006; Berridge, M J, Bootman, M. D., & Lipp, P. (1998). Molecular biology: Calcium - a life and death signal. Nature, 395(October), 645–648. Retrieved from http://dx.doi.org/10.1038/27094; Berridge, M J, Lipp, P., & Bootman, M. D. (2000). The versatility and universality of calcium signalling. Nature Reviews. Molecular Cell Biology, 1(1), 11–21. https://doi.org/10.1038/35036035; Berridge, Michael J, Bootman, M. D., & Roderick, H. L. (2003). Calcium signalling: dynamics, homeostasis and remodelling. Nature Reviews. Molecular Cell Biology, 4(7), 517–529. https://doi.org/10.1038/nrm1155; Besserer, G. M., Ottolia, M., Nicoll, D. a, Chaptal, V., Cascio, D., Philipson, K. D., & Abramson, J. (2007). The second Ca2+-binding domain of the Na+ Ca2+ exchanger is essential for regulation: crystal structures and mutational analysis. Proceedings of the National Academy of Sciences of the United States of America, 104(47), 18467–18472. https://doi.org/10.1073/pnas.0707417104; Blaustein, M. P., & Lederer, W. J. (1999). Sodium/calcium exchange: its physiological implications. Physiological Reviews, 79(3), 763–854.; Boivin, S., Kozak, S., & Meijers, R. (2013). Optimization of protein purification and characterization using Thermofluor screens. Protein Expression and Purification, 91(2), 192–206. https://doi.org/10.1016/j.pep.2013.08.002; Boyman, L., Mikhasenko, H., Hiller, R., & Khananshvili, D. (2009). Kinetic and equilibrium properties of regulatory calcium sensors of NCX1 protein. Journal of Biological Chemistry, 284(10), 6185–6191. https://doi.org/10.1074/jbc.M809012200; Bronner, F. (2001). Extracellular and intracellular regulation of calcium homeostasis. TheScientificWorldJournal, 1, 919–925. https://doi.org/10.1100/tsw.2001.489; Carafoli, Ernest, Malmström, K., Sigel, E., & Crompton, M. (1976). THE REGULATION OF INTRACELLULAR CALCIUM. Clinical Endocrinology, 5(s1), s49–s59. https://doi.org/10.1111/j.1365-2265.1976.tb03815.x; Carafoli, Ernesto. (1984). Intracellular calcium. General Pharmacology: The Vascular System, 15(5), 439. https://doi.org/10.1016/0306-3623(84)90065-X; Cavanagh, J., Fairbrother, W. J., Palmer III, A. G., Rance, M., & Skelton, N. J. (2007). Protein NMR Spectroscopy: Principles and Practice. In Protein NMR Spectroscopy (Vol. 2nd). Retrieved from http://www.amazon.fr/Protein-NMR-Spectroscopy-PrinciplesPractice/dp/012164491X; Chaptal, V., Besserer, G. M., Ottolia, M., Nicoll, D. a., Cascio, D., Philipson, K. D., & Abramson, J. (2007). How Does Regulatory Ca2+ Regulate the Na+-Ca2+ Exchanger? Channels, 1(6), 397–399. https://doi.org/10.4161/chan.1.6.5640; Chen, K., & Tjandra, N. (2011). The Use of Residual Dipolar Coupling in Studying Proteins by NMR. In NMR of Proteins and Small Biomolecules (pp. 47–67). https://doi.org/10.1007/128_2011_215; De La Torre, J. G., Huertas, M. L., & Carrasco, B. (2000). HYDRONMR: Prediction of NMR Relaxation of Globular Proteins from Atomic-Level Structures and Hydrodynamic Calculations. Journal of Magnetic Resonance, 147(1), 138–146. https://doi.org/10.1006/jmre.2000.2170; Delaglio, F., Grzesiek, S., Vuister, G. W., Zhu, G., Pfeifer, J., & Bax, A. (1995). NMRPipe: A multidimensional spectral processing system based on UNIX pipes. Journal of Biomolecular NMR, 6(3), 277–293. https://doi.org/10.1007/BF00197809; Dick, F. (1994). Acid cleavage/deprotection in Fmoc/tBu solid-phase peptide synthesis. Methods in Molecular Biology (Clifton, N.J.), 35, 63–72. https://doi.org/10.1385/0-89603- 273-6:63; Dosset, P., Hus, J. C., Blackledge, M., & Marion, D. (2000). Efficient analysis of macromolecular rotational diffusion from heteronuclear relaxation data. Journal of Biomolecular NMR, 16(1), 23–28. https://doi.org/10.1023/A:1008305808620; Dyck, C., Maxwell, K., Buchko, J., Trac, M., Omelchenko, A., Hnatowich, M., & Hryshko, L. V. (1998). Structure-Function Analysis of CALX1.1, a Na + -Ca 2+ Exchanger from Drosophila. Journal of Biological Chemistry, 273(21), 12981–12987. https://doi.org/10.1074/jbc.273.21.12981; Dyck, C., Omelchenko, A., Elias, C. L., Quednau, B. D., Philipson, K. D., Hnatowich, M., & Hryshko, L. V. (1999). Ionic Regulatory Properties of Brain and Kidney Splice Variants of the Ncx1 Na+–Ca2+ Exchanger. The Journal of General Physiology, 114(5), 701–711. https://doi.org/10.1085/jgp.114.5.701; Favier, A., & Brutscher, B. (2019). NMRlib: user-friendly pulse sequence tools for Bruker NMR spectrometers. Journal of Biomolecular NMR, 73(5), 199–211. https://doi.org/10.1007/s10858-019-00249-1; Gáspári, Z., & Perczel, A. (2010). Protein Dynamics as Reported by NMR. Annual Reports on NMR Spectroscopy, 71(C), 35–75. https://doi.org/10.1016/B978-0-08-089054- 8.00002-2; Giladi, M., Bohbot, H., Buki, T., Schulze, D. H., Hiller, R., & Khananshvili, D. (2012). Dynamic features of allosteric Ca 2+ sensor in tissue-specific NCX variants. Cell Calcium, 51(6), 478–485. https://doi.org/10.1016/j.ceca.2012.04.007; Giladi, M., Boyman, L., Mikhasenko, H., Hiller, R., & Khananshvili, D. (2010). Essential role of the CBD1-CBD2 linker in slow dissociation of Ca 2+ from the regulatory twodomain tandem of NCX1. Journal of Biological Chemistry, 285(36), 28117–28125. https://doi.org/10.1074/jbc.M110.127001; Giladi, M., Sasson, Y., Fang, X., Hiller, R., Buki, T., Wang, Y. X., … Khananshvili, D. (2012). A common CA2+-driven interdomain module governs eukaryotic NCX regulation. PLoS ONE, 7(6). https://doi.org/10.1371/journal.pone.0039985; Gill, M. L., & Palmer, A. G. (2014). Local isotropic diffusion approximation for coupled internal and overall molecular motions in NMR spin relaxation. Journal of Physical Chemistry B, 118(38), 11120–11128. https://doi.org/10.1021/jp506580c; Guvench, O., & MacKerell, A. D. (2008). Comparison of Protein Force Fields for Molecular Dynamics Simulations. In Methods in Molecular Biology (Vol. 443, pp. 63–88). https://doi.org/10.1007/978-1-59745-177-2_4; Halty-deLeon, L., Hansson, B. S., & Wicher, D. (2018). The Drosophila melanogaster Na+/Ca2+ Exchanger CALX Controls the Ca2+ Level in Olfactory Sensory Neurons at Rest and After Odorant Receptor Activation. Frontiers in Cellular Neuroscience, 12(July), 1–9. https://doi.org/10.3389/fncel.2018.00186; Hendus-Altenburger, R., Wang, X., Sjøgaard-Frich, L. M., Pedraz-Cuesta, E., Sheftic, S. R., Bendsøe, A. H., … Peti, W. (2019). Molecular basis for the binding and selective dephosphorylation of Na+/H+ exchanger 1 by calcineurin. Nature Communications, 10(1), 1–13. https://doi.org/10.1038/s41467-019-11391-7; Hilge, M. (2012). Ca2+ Regulation of Ion Transport in the Na+/Ca2+ Exchanger. Journal of Biological Chemistry, 287(38), 31641–31649. https://doi.org/10.1074/jbc.R112.353573; Hilge, Mark. (2013). Ca2+ Regulation in the Na+/Ca2+ Exchanger Features a Dual Electrostatic Switch Mechanism. In L. Annunziato (Ed.), Sodium Calcium Exchange: A Growing Spectrum of Pathophysiological Implications SE - 3 (pp. 27–33). https://doi.org/10.1007/978-1-4614-4756-6_3; Hilge, Mark, Aelen, J., Foarce, A., Perrakis, A., & Vuister, G. W. (2009). Ca2+ regulation in the Na+/Ca2+ exchanger features a dual electrostatic switch mechanism. Proceedings of the …, 106(34), 14333. https://doi.org/10.1073/pnas.0902171106; Hilge, Mark, Aelen, J., & Vuister, G. W. (2006). Ca2+ Regulation in the Na+/Ca2+ Exchanger Involves Two Markedly Different Ca2+ Sensors. Molecular Cell, 22(1), 15–25. https://doi.org/10.1016/j.molcel.2006.03.008; Hilgemann, D. W., Collins, A., & Matsuoka, S. (1992). Steady-state and dynamic properties of cardiac sodium-calcium exchange: Secondary modulation by cytoplasmic calcium and ATP. Journal of General Physiology, 100(6), 933–961. https://doi.org/10.1085/jgp.100.6.933; Hilgemann, D. W., Nicoll, D. A., & Philipson, K. D. (1991). Charge movement during Na+ translocation by native and cloned cardiac Na+/Ca2+ exchanger. Nature, Vol. 352, pp. 715–718. https://doi.org/10.1038/352715a0; Hore, P. J. (1995). Nuclear Magnetic Resonance (First). New York, NY: Oxford University Press Inc.; Hryshko, L. V., Matsuoka, S., Nicoll, D. A., Weiss, J. N., Schwarz, E. M., Benzer, S., & Philipson, K. D. (1996). Anomalous regulation of the Drosophila Na(+)-Ca2+ exchanger by Ca2+. The Journal of General Physiology, 108(1), 67–74. https://doi.org/10.1085/jgp.108.1.67; Hu, W., & Wang, L. (2006). Residual Dipolar Couplings: Measurements and Applications to Biomolecular Studies. Annual Reports on NMR Spectroscopy, 58, 231–303. https://doi.org/10.1016/S0066-4103(05)58005-0 Ishima, R., & Torchia, D. (2000). Protein dynamics from NMR. Nature Structural Biology, 7(9), 740–743. https://doi.org/10.1038/78963; Johnson, E., Bruschweiler-Li, L., Showalter, S. a, Vuister, G. W., Zhang, F., & Brüschweiler, R. (2008). Structure and dynamics of Ca2+-binding domain 1 of the Na+/Ca2+ exchanger in the presence and in the absence of Ca2+. Journal of Molecular Biology, 377, 945–955. https://doi.org/10.1016/j.jmb.2008.01.046; Johnson, E., Brüschweiler, R., & Showalter, S. A. (2008). A multifaceted approach to the interpretation of NMR order parameters: a case study of a dynamic alpha-helix. The Journal of Physical Chemistry. B, 112(19), 6203–6210. https://doi.org/10.1021/jp711160t; Karplus, M., & Kuriyan, J. (2005). Molecular dynamics and protein function. Proceedings of the National Academy of Sciences of the United States of America, 102(19), 6679– 6685. https://doi.org/10.1073/pnas.0408930102; Kay, L. E. (1998). Protein dynamics from NMR. Nature Structural Biology, 5(7), 513–517. https://doi.org/10.1038/755; Keeler, J. (2011). Understanding NMR Spectroscopy. John Wiley & Sons. .; Kempf, J. G., & Loria, J. P. (2002). Protein dynamics from solution NMR: Theory and applications. Cell Biochemistry and Biophysics, 37(3), 187–211. https://doi.org/10.1385/CBB:37:3:187; Khananshvili, D. (2014). Sodium-calcium exchangers (NCX): Molecular hallmarks underlying the tissue-specific and systemic functions. Pflugers Archiv European Journal of Physiology, 466, 43–60. https://doi.org/10.1007/s00424-013-1405-y; Khananshvili, D. (2020). Basic and editing mechanisms underlying ion transport and regulation in NCX variants. Cell Calcium, 85(November 2019), 102131. https://doi.org/10.1016/j.ceca.2019.102131; Kleckner, I. R., & Foster, M. P. (2011). An introduction to NMR-based approaches for measuring protein dynamics. Biochimica et Biophysica Acta - Proteins and Proteomics, 1814(8), 942–968. https://doi.org/10.1016/j.bbapap.2010.10.012; Kozak, S., Lercher, L., Karanth, M. N., Meijers, R., Carlomagno, T., & Boivin, S. (2016). Optimization of protein samples for NMR using thermal shift assays. Journal of Biomolecular NMR, 64(4), 281–289. https://doi.org/10.1007/s10858-016-0027-z; Kramer, F., Deshmukh, M. V., Kessler, H., & Glaser, S. J. (2004). Residual dipolar coupling constants: An elementary derivation of key equations. Concepts in Magnetic Resonance Part A: Bridging Education and Research, 21(1), 10–21. https://doi.org/10.1002/cmr.a.20003; Kummerlowe, G., Schmitt, S., & Luy, B. (2010). Cross-Fitting of Residual Dipolar Couplings. The Open Spectroscopy Journal, 4(1), 16–27. https://doi.org/10.2174/1874383801004010016; Leach, A. R. (2001). Molecular modelling: Principles and applications-Prentice Hall (2nd ed.). Pearson- Prentice Hall; Lemkul, J. (2018). From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package [Article v1.0]. Living Journal of Computational Molecular Science, 1(1), 1–53. https://doi.org/10.33011/livecoms.1.1.5068 Li, D. W., & Brüschweiler, R. (2010). NMR-based protein potentials. Angewandte Chemie - International Edition, 49(38), 6778–6780. https://doi.org/10.1002/anie.201001898; Li, Z., Nicoll, D. A., Collins, A., Hilgemann, D. W., Filoteo, A. G., Penniston, J. T., … Philipson, K. D. (1991). Identification of a peptide inhibitor of the cardiac sarcolemmal Na+-Ca2+ exchanger. Journal of Biological Chemistry, 266(2), 1014–1020.; Liao, J., Li, H., Zeng, W., Sauer, D. B., Belmares, R., & Jiang, Y. (2012). Structural Insight into the Ion-Exchange Mechanism of the Sodium/Calcium Exchanger. Science, 335(curve 1), 686–690. https://doi.org/10.1126/science.1215759; Liao, Jun, Marinelli, F., Lee, C., Huang, Y., Faraldo-Gómez, J. D., & Jiang, Y. (2016). Mechanism of extracellular ion exchange and binding-site occlusion in a sodium/calcium exchanger. Nature Structural and Molecular Biology, 23(6), 590–599. https://doi.org/10.1038/nsmb.3230; Libreros, G. A. (2018). L , D-transpeptidases de Mycobacterium tuberculosis : Estudo das interações com antibióticos β -lactâmicos e triagem de fragmentos. Universidade Estadual Paulista, Julio de Mesquita Filho (UNESP).; Lipari, G., & Szabo, A. (1982a). Model-Free Approach to the Interpretation of Nuclear Magnetic Resonance Relaxation in Macromolecules. 1. Theory and Range of Validity. Journal of the American Chemical Society, 104(17), 4546–4559. https://doi.org/10.1021/ja00381a009; Lipari, G., & Szabo, A. (1982b). Model-Free Approach to the Interpretation of Nuclear Magnetic Resonance Relaxation in Macromolecules. 2. Analysis of Experimental Results. Journal of the American Chemical Society, 104(17), 4559–4570. https://doi.org/10.1021/ja00381a010; Losonczi, J. A., Andrec, M., Fischer, M. W. F., & Prestegard, J. H. (1999). Order Matrix Analysis of Residual Dipolar Couplings Using Singular Value Decomposition. Journal of Magnetic Resonance, 138(2), 334–342. https://doi.org/10.1006/jmre.1999.1754; Lümmen, P. (2013). Calcium Channels as Molecular Target Sites of Novel Insecticides. In Advances in Insect Physiology (Vol. 44, pp. 287–347). https://doi.org/10.1016/B978-0-12- 394389-7.00005-3; Lytton, J. (2007). Na+/Ca2+ exchangers: Three mammalian gene families control Ca2+ transport. Biochemical Journal, 406(3), 365–382. https://doi.org/10.1042/BJ20070619 Martín-Santamaría, S. (Ed.). (2017). Computational Tools for Chemical Biology. https://doi.org/10.1039/9781788010139; Massi, F., Johnson, E., Wang, C., Rance, M., & Palmer, A. G. (2004). NMR R 1 ρ Rotating-Frame Relaxation with Weak Radio Frequency Fields. Journal of the American Chemical Society, 126(7), 2247–2256. https://doi.org/10.1021/ja038721w; Matsuoka, S., Nicoll, D. A., He, Z., & Philipson, K. D. (1997). Regulation of the Cardiac Na+-Ca2+ exchanger by the endogenous XIP region. Journal of General Physiology, 109(2), 273–286. https://doi.org/10.1085/jgp.109.2.273; McFadzean, I., & Gibson, A. (2002). The developing relationship between receptoroperated and store-operated calcium channels in smooth muscle. British Journal of Pharmacology, 135(1), 1–13. https://doi.org/10.1038/sj.bjp.0704468; Miroux, B., & Walker, J. E. (1996). Over-production of Proteins inEscherichia coli: Mutant Hosts that Allow Synthesis of some Membrane Proteins and Globular Proteins at High Levels. Journal of Molecular Biology, 260(3), 289–298. https://doi.org/10.1006/jmbi.1996.0399; Molinaro, P., Pannaccione, A., Sisalli, M. J., Secondo, A., Cuomo, O., Sirabella, R., … Annunziato, L. (2015). A new cell-penetrating peptide that blocks the autoinhibitory XIP domain of NCX1 and enhances antiporter activity. Molecular Therapy : The Journal of the American Society of Gene Therapy, 23(3), 465–476. https://doi.org/10.1038/mt.2014.231; Morgon, N. H., & Coutinho, K. R. (2007). Métodos de química teórica e modelagem molecular. Editora Livraria da Física.; Morin, S. (2011). A practical guide to protein dynamics from 15N spin relaxation in solution. Progress in Nuclear Magnetic Resonance Spectroscopy, 59(3), 245–262. https://doi.org/10.1016/j.pnmrs.2010.12.003; Nicoll, D. A., Ottolia, M., Goldhaber, J. I., & Philipson, K. D. (2013). 20 years from NCX purification and cloning: milestones. Advances in Experimental Medicine and Biology, 961, 17–23. https://doi.org/10.1007/978-1-4614-4756-6_2; Nicoll, D. A., Sawaya, M. R., Kwon, S., Cascio, D., Philipson, K. D., & Abramson, J. (2006). The crystal structure of the primary Ca2+ sensor of the Na +/Ca2+ exchanger reveals a novel Ca2+ binding motif. Journal of Biological Chemistry, 281(31), 21577– 21581. https://doi.org/10.1074/jbc.C600117200; Olsson, M. H. M., SØndergaard, C. R., Rostkowski, M., & Jensen, J. H. (2011). PROPKA3: Consistent treatment of internal and surface residues in empirical p K a predictions. Journal of Chemical Theory and Computation, 7(2), 525–537. https://doi.org/10.1021/ct100578z; Omelchenko, a, Dyck, C., Hnatowich, M., Buchko, J., Nicoll, D. a, Philipson, K. D., & Hryshko, L. V. (1998). Functional differences in ionic regulation between alternatively spliced isoforms of the Na+-Ca2+ exchanger from Drosophila melanogaster. The Journal of General Physiology, 111(May), 691–702. https://doi.org/10.1085/jgp.111.5.691; On, C., Marshall, C. R., Chen, N., Moyes, C. D., & Tibbits, G. F. (2008). Gene Structure Evolution of the Na+-Ca2+ Exchanger (NCX) Family. BMC Evolutionary Biology, 8(1), 127. https://doi.org/10.1186/1471-2148-8-127; Ottolia, M., Nicoll, D. A., & Philipson, K. D. (2009). Roles of two Ca2+-binding domains in regulation of the cardiac Na+-Ca2+ exchanger. Journal of Biological Chemistry, 284(47), 32735–32741. https://doi.org/10.1074/jbc.M109.055434; Palmer, A. G. (1997). Probing molecular motion by NMR. Current Opinion in Structural Biology, 7(5), 732–737. https://doi.org/10.1016/S0959-440X(97)80085-1; Palmer, A. G. (2004). NMR characterization of the dynamics of biomacromolecules. Chemical Reviews, 104(8), 3623–3640. https://doi.org/10.1021/cr030413t; Palmer, A. G., Williams, J., & McDermott, A. (1996). Nuclear magnetic resonance studies of biopolymer dynamics. Journal of Physical Chemistry, 100(31), 13293–13310. https://doi.org/10.1021/jp9606117; Parekh, A. B., & Putney, J. W. (2005). Store-Operated Calcium Channels. Physiological Reviews, 85(2), 757–810. https://doi.org/10.1152/physrev.00057.2003; Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera - A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084; Philipson, Keneth D., Nicoll, D. A., Ottolia, M., Quednau, B. D., Reuter, H., John, S., & Qiu, Z. (2006). The Na+/Ca2+ Exchange Molecule. Annals of the New York Academy of Sciences, 976(1), 1–10. https://doi.org/10.1111/j.1749-6632.2002.tb04708.x; Philipson, Kenneth D., & Nicoll, D. A. (2000). Sodium-Calcium Exchange: A Molecular Perspective. Annual Review of Physiology, 62(1), 111–133. https://doi.org/10.1146/annurev.physiol.62.1.111; Reeves, J. P. (1998). Na+/Ca2+ exchange and cellular Ca2+ homeostasis. Journal of Bioenergetics and Biomembranes, 30(2), 151–160. https://doi.org/10.1023/A:1020569224915; Reeves, J. P., & Condrescu, M. (2008). Ionic regulation of the cardiac sodium-calcium exchanger. Channels, 2(5), 322–328. https://doi.org/10.4161/chan.2.5.6897; Ren, X., & Philipson, K. D. (2013). The topology of the cardiac Na+/Ca2+ exchanger, NCX1. Journal of Molecular and Cellular Cardiology, 57(1), 68–71. https://doi.org/10.1016/j.yjmcc.2013.01.010; Rule, G. S., & Hitchens, T. K. (2006). Fundamentals of Protein NMR Spectroscopy. In Focus on Structural Biology: Vol. 5. https://doi.org/10.1007/1-4020-3500-4; Salinas, R. K., Bruschweiler-Li, L., Johnson, E., & Brus̈chweiler, R. (2011). Ca 2+ binding alters the interdomain flexibility between the two cytoplasmic calcium-binding domains in the Na +/Ca 2+ exchanger. Journal of Biological Chemistry, 286(37), 32123–32131. https://doi.org/10.1074/jbc.M111.249268; Sass, J., Cordier, F., Hoffmann, A., Rogowski, M., Cousin, A., Omichinski, J. G., … Grzesiek, S. (1999). Purple membrane induced alignment of biological macromolecules in the magnetic field. Journal of the American Chemical Society, 121(10), 2047–2055. https://doi.org/10.1021/ja983887w; Schwarz, E. M., & Benzer, S. (1997). Calx, a Na-Ca exchanger gene of Drosophila melanogaster. Proceedings of the National Academy of Sciences of the United States of America, 94(19), 10249–10254. https://doi.org/10.1073/pnas.94.19.10249; Scopes, R. K. (1974). Measurement of protein by spectrophotometry at 205 nm. Analytical Biochemistry, 59(1), 277–282. https://doi.org/10.1016/0003-2697(74)90034-7; Sebastián Yagüe, Á., Pascua García, A., Sebastían, F., Aguirre, J., León, E., Bajic, D., & Baú, D. (2014). Bioinformática con Ñ (1st ed.; A. Sebastián & A. Pascual-García, Eds.). https://doi.org/10.5281/zenodo.1065032; Sharma, V., & O’Halloran, D. M. (2014). Recent structural and functional insights into the family of sodium calcium exchangers. Genesis, 52(2), 93–109. https://doi.org/10.1002/dvg.22735; Shen, Y., & Bax, A. (2013). Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. Journal of Biomolecular NMR, 56(3), 227–241. https://doi.org/10.1007/s10858-013-9741-y; Singh, R. K., Blossom, B. M., Russo, D. A., Van Oort, B., Croce, R., Jensen, P. E., … Bjerrum, M. J. (2019). Thermal unfolding and refolding of a lytic polysaccharide monooxygenase from: Thermoascus aurantiacus. RSC Advances, 9(51), 29734–29742. https://doi.org/10.1039/c9ra05920b; Skora, L., Mestan, J., Fabbro, D., Jahnke, W., & Grzesiek, S. (2013). NMR reveals the allosteric opening and closing of Abelson tyrosine kinase by ATP-site and myristoyl pocket inhibitors. Proceedings of the National Academy of Sciences of the United States of America, 110(47). https://doi.org/10.1073/pnas.1314712110; Song, Y., Dimaio, F., Wang, R. Y. R., Kim, D., Miles, C., Brunette, T., … Baker, D. (2013). High-resolution comparative modeling with RosettaCM. Structure, 21(10), 1735–1742. https://doi.org/10.1016/j.str.2013.08.005; Stabelini, T. C. (2018). Estudos estruturais de fragmentos do trocador de Na+/Ca2+ por RMN em solução (Biblioteca Digital de Teses e Dissertações da Universidade de São Paulo). https://doi.org/10.11606/D.46.2018.tde-11122018-091550; Strickland, M., & Tjandra, N. (2018). Residual dipolar coupling for conformational and dynamic studies. Modern Magnetic Resonance, 419–434. https://doi.org/10.1007/978-3- 319-28388-3_86; Terpe, K. (2006). Overview of bacterial expression systems for heterologous protein production: From molecular and biochemical fundamentals to commercial systems. Applied Microbiology and Biotechnology, 72(2), 211–222. https://doi.org/10.1007/s00253- 006-0465-8; Tolman, J. R., & Ruan, K. (2006). NMR residual dipolar couplings as probes of biomolecular dynamics. Chemical Reviews, 106(5), 1720–1736. https://doi.org/10.1021/cr040429z; Verkhratsky, A., Trebak, M., Perocchi, F., Khananshvili, D., & Sekler, I. (2018). Crosslink between calcium and sodium signalling. Experimental Physiology, 103(2), 157–169. https://doi.org/10.1113/EP086534; Vranken, W. F., Boucher, W., Stevens, T. J., Fogh, R. H., Pajon, A., Llinas, M., … Laue, E. D. (2005). The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins, 59(4), 687–696. https://doi.org/10.1002/prot.20449; Wang, T., Xu, H., Oberwinkler, J., Gu, Y., Hardie, R. C., & Montell, C. (2005). Light activation, adaptation, and cell survival functions of the Na + /Ca 2+ exchanger CalX. Neuron, 45(3), 367–378. https://doi.org/10.1016/j.neuron.2004.12.046; William Studier, F., Rosenberg, A. H., Dunn, J. J., & Dubendorff, J. W. (1990). Use of T7 RNA polymerase to direct expression of cloned genes. Methods in Enzymology, 185(C), 60–89. https://doi.org/10.1016/0076-6879(90)85008-C; Wu, M., Le, H. D., Wang, M., Yurkov, V., Omelchenko, A., Hnatowich, M., … Zheng, L. (2010). Crystal structures of progressive Ca2+ binding states of the Ca2+ sensor Ca2+ binding domain 1 (CBD1) from the CALX Na+/Ca2+ exchanger reveal incremental conformational transitions. Journal of Biological Chemistry, 285(4), 2554–2561. https://doi.org/10.1074/jbc.M109.059162; Wu, M., Tong, S., Gonzalez, J., Jayaraman, V., Spudich, J. L., & Zheng, L. (2011). Structural Basis of the Ca 2+ Inhibitory Mechanism of Drosophila Na +/Ca 2+ Exchanger CALX and Its Modification by Alternative Splicing. Structure, 19(10), 1509–1517. https://doi.org/10.1016/j.str.2011.07.008; Wu, M., Wang, M., Nix, J., Hryshko, L. V., & Zheng, L. (2009). Crystal Structure of CBD2 from the Drosophila Na+/Ca2+ Exchanger: Diversity of Ca2+ Regulation and Its Alternative Splicing Modification. Journal of Molecular Biology, 387(1), 104–112. https://doi.org/10.1016/j.jmb.2009.01.045; Wüthrich, K. (1986). NMR of Proteins and Nucleic Acids. In A Wiley-Interscience Publication. Retrieved from https://books.google.com.br/books?id=zfBqAAAAMAAJ; Yuan, J., Yuan, C., Xie, M., Yu, L., Bruschweiler-Li, L., & Bruschweiler, R. (2018). The Intracellular Loop of the Na+/Ca2+ Exchanger Contains an “awareness Ribbon” Shaped Two-helix Bundle Domain. Biochemistry, 1. https://doi.org/10.1021/acs.biochem.8b00300; Zheng, L., Wu, M., & Tong, S. (2013). Structural Studies of the Ca2+ Regulatory Domain of Drosophila Na+/Ca2+ Exchanger CALX. In L. Annunziato (Ed.), Sodium Calcium Exchange: A Growing Spectrum of Pathophysiological Implications (pp. 55–63). https://doi.org/10.1007/978-1-4614-4756-6_6; Abiko, L. A., Vitale, P. M., Favaro, D. C., Hauk, P., Li, D.-W., Yuan, J., … Brüschweiler, R. (2016). Model for the allosteric regulation of the Na + /Ca 2+ exchanger NCX. Proteins: Structure, Function, and Bioinformatics, 84(5), 580–590. https://doi.org/10.1002/prot.25003; Boivin, S., Kozak, S., & Meijers, R. (2013). Optimization of protein purification and characterization using Thermofluor screens. Protein Expression and Purification, 91(2), 192–206. https://doi.org/10.1016/j.pep.2013.08.002.; Delaglio, F., Grzesiek, S., Vuister, G. W., Zhu, G., Pfeifer, J., & Bax, A. (1995). NMRPipe: A multidimensional spectral processing system based on UNIX pipes. Journal of Biomolecular NMR, 6(3), 277–293. https://doi.org/10.1007/BF00197809.; Hendus-Altenburger, R., Wang, X., Sjøgaard-Frich, L. M., Pedraz-Cuesta, E., Sheftic, S. R., Bendsøe, A. H., … Peti, W. (2019). Molecular basis for the binding and selective dephosphorylation of Na+/H+ exchanger 1 by calcineurin. Nature Communications, 10(1), 1–13. https://doi.org/10.1038/s41467-019-11391-7.; Kozak, S., Lercher, L., Karanth, M. N., Meijers, R., Carlomagno, T., & Boivin, S. (2016). Optimization of protein samples for NMR using thermal shift assays. Journal of Biomolecular NMR, 64(4), 281–289. https://doi.org/10.1007/s10858-016-0027-z.; Miroux, B., & Walker, J. E. (1996). Over-production of Proteins inEscherichia coli: Mutant Hosts that Allow Synthesis of some Membrane Proteins and Globular Proteins at High Levels. Journal of Molecular Biology, 260(3), 289–298. https://doi.org/10.1006/jmbi.1996.0399.; Scopes, R. K. (1974). Measurement of protein by spectrophotometry at 205 nm. Analytical Biochemistry, 59(1), 277–282. https://doi.org/10.1016/0003-2697(74)90034-7.; Singh, R. K., Blossom, B. M., Russo, D. A., Van Oort, B., Croce, R., Jensen, P. E., … Bjerrum, M. J. (2019). Thermal unfolding and refolding of a lytic polysaccharide monooxygenase from: Thermoascus aurantiacus. RSC Advances, 9(51), 29734–29742. https://doi.org/10.1039/c9ra05920b.; Terpe, K. (2006). Overview of bacterial expression systems for heterologous protein production: From molecular and biochemical fundamentals to commercial systems. Applied Microbiology and Biotechnology, 72(2), 211–222. https://doi.org/10.1007/s00253- 006-0465-8.; Vranken, W. F., Boucher, W., Stevens, T. J., Fogh, R. H., Pajon, A., Llinas, M., … Laue, E. D. (2005). The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins, 59(4), 687–696. https://doi.org/10.1002/prot.20449.; William Studier, F., Rosenberg, A. H., Dunn, J. J., & Dubendorff, J. W. (1990). Use of T7 RNA polymerase to direct expression of cloned genes. Methods in Enzymology, 185(C), 60–89. https://doi.org/10.1016/0076-6879(90)85008-C.; https://repositorio.unal.edu.co/handle/unal/80816; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https:/repositorio.una.edu.co

    Verfügbarkeit: https://repositorio.unal.edu.co/handle/unal/80816
    https:/repositorio.una.edu.co

  16. 16
  17. 17
  18. 18

    Quelle: Docta Complutense
    instname
    E-Prints Complutense. Archivo Institucional de la UCM
    E-Prints Complutense: Archivo Institucional de la UCM
    Universidad Complutense de Madrid

    Dateibeschreibung: application/pdf

  19. 19
  20. 20