Evaluación in – silico de la estructura y función de la proteína hipotética B7FQK1 de phaeodactylum tricornutum

dc.contributor.advisorSánchez Calderón, Juan David
dc.contributor.authorNavarro Gómez, Sirlhey
dc.contributor.authorSuárez Gómez, Ana Milena
dc.coverage.spatialBarranquillaspa
dc.creator.emailnavarrogomez09@hotmail.comspa
dc.creator.emailany.suarez1@hotmail.comspa
dc.date.accessioned2020-01-22T20:48:52Z
dc.date.available2020-01-22T20:48:52Z
dc.date.created2019
dc.description.abstractPhaeodactylum tricornutum es una diatomea marina objeto de estudio durante los últimos años gracias a sus propiedades biológicas y su potencial biotecnológico. A partir de P. tricornutum se pueden obtener distintos componentes de alto valor como nutracéuticos, biocombustibles, cosméticos, productos farmacéuticos, etc. Esta microalga se encuentra dentro de las principales especies productoras de PUFAs (EPA y DHA), importantes en la industria farmacéutica y alimentaria debido a sus efectos positivos en la salud humana. P. tricornutum posee un genoma de aproximadamente 27. 4 megabases (Mb) y se estima que contiene 10, 402 genes. No obstante, existen regiones génicas con funcionalidad desconocida, lo que genera la necesidad de llevar a cabo análisis bioinformáticos que faciliten la comprensión del flujo de información desde los genes a las estructuras moleculares. Es por esto que, se buscó predecir computacionalmente la estructura de la proteína hipotética B7FQK1 de Phaeodactylum tricornutum y comprobar la función descrita, relacionada con la biosíntesis de ácidos grasos. La investigación se desarrolló en cuatro fases, la primera consistió en la evaluación in – silico de la estructura primaria, utilizando servidores y algoritmos como TMHMM, ConSurf, PROSITE, Pfam y BLAST. Posteriormente, se analizaron las características físico – químicas y perfiles de la secuencia de aminoácidos con las herramientas EXPASY – PROTPARAM y ProtScale respectivamente. En la tercera fase se predijo la estructura secundaria a partir de los resultados obtenidos de los servidores NPS@ y PSIPRED. Por último, se obtuvo la construcción del modelo 3D de la proteína mediante el servidor I – TASSER y se validó con la herramienta STRUCTURE ASSESSMENT de SWISS – MODEL. Se identificó un dominio FA_desaturasa 2 directamente relacionado con la función predicha. Con base en la evaluación computacional, se obtuvo la estructura secundaria y el modelo 3D, este último con un C – score de 1. 75 que indica un modelo de buena calidad. La predicción estructural y funcional de la proteína hipotética B7FQK1 permite profundizar en los conocimientos de las propiedades biológicas de la microalga y contribuye en la optimización de los procesos biotecnológicos.spa
dc.description.abstractPhaeodactylum tricornutum is a marine diatom that has been studied in recent years due to its biological properties and its biotechnological potential. From P. tricornutum different high value components can be obtained such as nutraceuticals, biofuels, cosmetics, pharmaceutical products, etc. This microalga is among the main producer species of PUFAs (EPA and DHA) important in the pharmaceutical and food industry due to its positive effects on human health. P. tricornutum has its sequenced genome, it has approximately 27.4 megabases (Mb) and it is estimated that it contains 10,402 genes. However, there are gene regions with unknown functionality, which generates the need to carry out bioinformatics analysis that facilitates the understanding of the flow of information from genes to molecular structures. That is why, we sought computationally to predict the structure of the hypothetical protein B7FQK1 of Phaeodactylum tricornutum and verify the function described, related to the biosynthesis of fatty acids. The research was developed in four phases, the first consisted in the in-silico evaluation of the primary structure, using servers and algorithms such as TMHMM, ConSurf, PROSITE, Pfam and BLAST. Subsequently, the physicochemical characteristics and profiles of the amino acids sequence were analyzed with the EXPASY - PROTPARAM and ProtScale tools respectively. In the third phase the secondary structure was predicted from the results obtained from the NPS @ and PSIPRED servers. Finally, the construction of the 3D model of the protein was obtained through the I - TASSER server and validated with the STRUCTURE ASSESSMENT of SWISS - MODEL tool. A FA_desaturase 2 domain directly related to the predicted function was identified. Based on the computational evaluation, the secondary structure and the 3D model were obtained, the latter with a C - score of 1.75 indicating a good quality model. The structural and functional prediction of the hypothetical protein B7FQK1 allows deepening the knowledge of the biological properties of the microalga and contributes in the optimization of biotechnological processes.Eng
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10901/17781
dc.language.isospaspa
dc.relation.referencesAdl, S. M., Simpson, A. G. B., Farmer, M. A., Andersen, R. A., Anderson, O. R., Barta, J. R., … Taylor, M. F. J. R. (2005). The new higher level classification of eukaryotes with emphasis on the taxonomy of protists. Journal of Eukaryotic Microbiology, 52(5), 399–451. https://doi.org/10.1111/j.15507408.2005.00053.xspa
dc.relation.referencesAltschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D. J. (1990). Basic local alignment search tool. J. Mol. Biol., 215, 403–410spa
dc.relation.referencesAmbati, R. R., Gogisetty, D., Aswathanarayana, R. G., Ravi, S., Bikkina, P. N., Bo, L., & Yuepeng, S. (2018). Industrial potential of carotenoid pigments from microalgae: Current trends and future prospects. Critical Reviews in Food Science and Nutrition, 8398, 1–22. https://doi.org/10.1080/10408398.2018.1432561spa
dc.relation.referencesAtiku, H., Rmsr, M., Aa, A., & Aa, W. (2016). Harvesting of microalgae biomass from the phycoremediation process of greywater. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-016-7456-9spa
dc.relation.referencesAyodhya D. (2014). Bioremediation of wastewater by using microalgae : an experimental study, (August).spa
dc.relation.referencesBaker, D., & Sali, A. (2001). Protein structure prediction and structural genomics. Science, 294(5540), 93–96. https://doi.org/10.1126/science.1065659spa
dc.relation.referencesBaudouin-cornu, P., Schuerer, K., Marlie, P., & Thomas, D. (2004). Intimate Evolution of Proteins, 279(7), 5421–5428. https://doi.org/10.1074/jbc.M306415200spa
dc.relation.referencesBenkert, P., Biasini, M., & Schwede, T. (2011). Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics, 27(3), 343–350. https://doi.org/10.1093/bioinformatics/btq662spa
dc.relation.referencesBente Edvarsen, Wenche Eikrem, J.C Green, Robert A. Andersen, S. Y. M.-V. S. and L. K. (2000). Phylogenetic reconstructions of the Haptophyta inferred from 18s ribosomal DNA sequences and available morphological data. Phycologiaspa
dc.relation.referencesBerman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., … Bourne, P. E. (2000). The Protein Data Bank Helen. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235spa
dc.relation.referencesBorowitzka, M. A. (2013). High-value products from microalgae — their development and commercialisation. https://doi.org/10.1007/s10811-0139983-9spa
dc.relation.referencesBowler, C., Allen, A. E., Badger, J. H., Grimwood, J., Jabbari, K., Kuo, A., … Grigoriev, I. V. (2008). The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature, 456(7219), 239–244. https://doi.org/10.1038/nature07410spa
dc.relation.referencesBowler, C., Vardi, A., & Allen, A. E. (2010). Oceanographic and Biogeochemical Insights from Diatom Genomes, 333–367. https://doi.org/10.1146/annurevmarine-120308-081051spa
dc.relation.referencesBranco-Vieira, M., San Martin, S., Agurto, C., Freitas, M. A. V., Mata, T. M., Martins, A. A., & Caetano, N. (2018). Biochemical characterization of Phaeodactylum tricornutum for microalgae-based biorefinery. Energy Procedia, 153, 466–470. https://doi.org/10.1016/j.egypro.2018.10.079spa
dc.relation.referencesBrosnan, J. T., & Brosnan, M. E. (2012). Glutamate : a truly functional amino acid. https://doi.org/10.1007/s00726-012-1280-4spa
dc.relation.referencesBruce, A., Alexander, J., Julian, L., Martin, R., Keith, R., & Peter, W. (2002). Molecular Biology of the Cell (4th editio). New York.spa
dc.relation.referencesCañedo R, & J, A. (2004). Bioinformatica: en busca de los secreos moleculares de la vida, 12, 1–30spa
dc.relation.referencesCanto, R., & Baquero, F. (2008). Antibiotics and antibiotic resistance in water environments, 260–265. https://doi.org/10.1016/j.copbio.2008.05.006spa
dc.relation.referencesCardozo, K. H. M., Guaratini, T., Barros, M. P., Falcão, V. R., Tonon, A. P., Lopes, N. P., … Pinto, E. (2007). Metabolites from algae with economical impact. Comparative Biochemistry and Physiology - C Toxicology and Pharmacology, 146(1–2 SPEC. ISS.), 60–78. https://doi.org/10.1016/j.cbpc.2006.05.007spa
dc.relation.referencesChauton, M. S., Reitan, K. I., Norsker, N. H., Tveterås, R., & Kleivdal, H. T. (2015). A techno-economic analysis of industrial production of marine microalgae as a source of EPA and DHA-rich raw material for aquafeed: Research challenges and possibilities. Aquaculture, 436, 95–103. https://doi.org/10.1016/j.aquaculture.2014.10.038spa
dc.relation.referencesChen, C., & Evans, L. B. (1989). Phase Partitioning of Biomolecules : Solubilities of Amino Acids, 5(3), 111–118.spa
dc.relation.referencesCherubini, F. (2010). The biorefinery concept: Using biomass instead of oil for producing energy and chemicals. Energy Conversion and Management, 51(7), 1412–1421. https://doi.org/10.1016/j.enconman.2010.01.015spa
dc.relation.referencesCombet, C., Blanchet, C., Geourjon, C., & Deléage, G. (2000). NPS@: Network protein sequence analysis. Trends in Biochemical Sciences, 25(3), 147–150. https://doi.org/10.1016/S0968-0004(99)01540-6spa
dc.relation.referencesDavydov, R., Behrouzian, B., Smoukov, S., Stubbe, J., & Hoffman, B. M. (2005). Effect of Substrate on the Diiron ( III ) Site in Stearoyl Acyl Carrier Protein ∆ 9 Desaturase as Disclosed by Cryoreduction Electron Paramagnetic Resonance / Electron Nuclear Double Resonance Spectroscopy †, (Iii), 1309–1315.spa
dc.relation.referencesDeng, M., Zhang, K. U. I., Mehta, S., & Chen, T. (2003). Prediction of Protein Function Using Protein – Protein Interaction Data, 10(6), 947–960.spa
dc.relation.referencesDesbois, A. P., Lebl, T., Yan, L., & Smith, V. J. (2008). Isolation and structural characterisation of two antibacterial free fatty acids from the marine diatom , Phaeodactylum tricornutum, 755–764. https://doi.org/10.1007/s00253-0081714-9spa
dc.relation.referencesDolch, L. J., & Maréchal, E. (2015). Inventory of fatty acid desaturases in the pennate diatom Phaeodactylum tricornutum. Marine Drugs, 13(3), 1317–1339. https://doi.org/10.3390/md13031317spa
dc.relation.referencesDomergue, F., Abbadi, A., Ott, C., Zank, T. K., Zähringer, U., & Heinz, E. (2003). Acyl carriers used as substrates by the desaturases and elongases involved in very long-chain polyunsaturated fatty acids biosynthesis reconstituted in yeast. Journal of Biological Chemistry, 278(37), 35115–35126spa
dc.relation.referencesDomínguez, H. (2013). Algae as a source of biologically active ingredients for the formulation of functional foods and nutraceuticals. Functional Ingredients from Algae for Foods and Nutraceuticals, 1–19. https://doi.org/10.1533/9780857098689.1spa
dc.relation.referencesFigueiredo, P. S., Inada, A. C., Marcelino, G., Cardozo, C. M. L., Freitas, K. de C., Guimarães, R. de C. A., … Hiane, P. A. (2017). Fatty acids consumption: The role metabolic aspects involved in obesity and its associated disorders. Nutrients, 9(10), 1–32. https://doi.org/10.3390/nu9101158spa
dc.relation.referencesGalperin, M. Y., & Koonin, E. V. (2004). ―Conserved hypothetical‖ proteins: Prioritization of targets for experimental study. Nucleic Acids Research, 32(18), 5452–5463. https://doi.org/10.1093/nar/gkh885spa
dc.relation.referencesGantt E, C. S. (1965). The ultrastructure of Porphyridium cruentum. Journal of Experimental Psychology: General, 136(1), 23–42.spa
dc.relation.referencesGasteiger, E., Bairoch, A., Sanchez, J., Williams, K. L., Wilkins, M. R., Appel, R. D., & Hochstrasser, D. F. (2005). Protein Identification and Analysis Tools in the ExPASy Server, 112, 531–552.spa
dc.relation.referencesGerman-Báez, L. J., Valdez-Flores, M. A., Félix-Medina, J. V., NorzagarayValenzuela, C. D., Santos-Ballardo, D. U., Reyes-Moreno, C., … Valdez-Ortiz, A. (2017). Chemical composition and physicochemical properties of Phaeodactylum tricornutum microalgal residual biomass. Food Science and Technology International, 23(8), 681–689. https://doi.org/10.1177/1082013217717611spa
dc.relation.referencesGibbs, sarah p. (1992). The Evolution of Algal Chloroplasts. (Ralph A Lewin, Ed.) (Chapman &). new York and Londomspa
dc.relation.referencesGlaser, F., Pupko, T., Paz, I., Bell, R. E., Bechor-Shental, D., Martz, E., & Ben-Tal, N. (2003). ConSurf: Identification of Functional Regions in Proteins by Surface-Mapping of Phylogenetic Information Downloaded from. Bioinformatics Applications Note, 19(1), 163–164. https://doi.org/10.1093/bioinformatics/19.1.163spa
dc.relation.referencesGolding, G. B. (2003). DNA and the revolutions of molecular evolution, computational biology, and bioinformatics. Genome, 46(6), 930–935. https://doi.org/10.1139/g03-108spa
dc.relation.referencesHamilton, M. L., Warwick, J., Terry, A., Allen, M. J., Napier, A., & Sayanova, O. (2015). Towards the Industrial Production of Omega- 3 Long Chain Polyunsaturated Fatty Acids from a Genetically Modified Diatom Phaeodactylum tricornutum, 1–15. https://doi.org/10.1371/journal.pone.0144054spa
dc.relation.referencesHoffmann, M., Hornung, E., Busch, S., Kassner, N., Ternes, P., & Braus, G. H. (2007). A Small Membrane-peripheral Region Close to the Active Center Determines Regioselectivity of Membrane-bound Fatty Acid Desaturases from Aspergillus nidulans *, 282(37), 26666–26674. https://doi.org/10.1074/jbc.M705068200spa
dc.relation.referencesHuang, A., He, L., & Wang, G. (2011). Identification and characterization of microRNAs from Phaeodactylum tricornutum by high- throughput sequencing and bioinformatics analysis. BMC Genomics, 12(1), 337. https://doi.org/10.1186/1471-2164-12-337spa
dc.relation.referencesJabeen, A., Mohamedali, A., & Ranganathan, S. (2018a). Protocol for Protein Structure Modelling. Encyclopedia of Bioinformatics and Computational Biology, (September 2017), 252–272. https://doi.org/10.1016/B978-0-12809633-8.20477-9spa
dc.relation.referencesJabeen, A., Mohamedali, A., & Ranganathan, S. (2018b). Protocol for Protein Structure Modelling. Encyclopedia of Bioinformatics and Computational Biology, (September 2017), 252–272. https://doi.org/10.1016/B978-0-12809633-8.20477-9spa
dc.relation.referencesKamm, B., & Kamm, M. (2004). Principles of biorefineries. Applied Microbiology and Biotechnology, 64(2), 137–145. https://doi.org/10.1007/s00253-003-15377spa
dc.relation.referencesKent, M., Welladsen, H. M., Mangott, A., & Li, Y. (2015). Nutritional evaluation of Australian microalgae as potential human health supplements. PLoS ONE, 83 10(2), 1–14. https://doi.org/10.1371/journal.pone.0118985spa
dc.relation.referencesKhan, M. I., Shin, J. H., & Kim, J. D. (2018). The promising future of microalgae: Current status, challenges, and optimization of a sustainable and renewable industry for biofuels, feed, and other products. Microbial Cell Factories, 17(1), 1–21. https://doi.org/10.1186/s12934-018-0879-xspa
dc.relation.referencesKim, S. M., Jung, Y. J., Kwon, O. N., Cha, K. H., Um, B. H., Chung, D., & Pan, C. H. (2012). A potential commercial source of fucoxanthin extracted from the microalga Phaeodactylum tricornutum. Applied Biochemistry and Biotechnology, 166(7), 1843–1855. https://doi.org/10.1007/s12010-012-9602-2spa
dc.relation.referencesKoonin, E.V., Galperin, M. . (2003). Protein sequence motifs and domain databases In: Koonin, E.V., Galperin, M.Y. (Eds.), Sequence-evolution function: Computational Approaches in Comparative Genomics. Kluwer Academic Publishers.spa
dc.relation.referencesKrogh, A., Larsson, B., von Heijne, G., Sonnhammer, E. L. (2001). Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol., 305, 567–580.spa
dc.relation.referencesL. Tao, P. Zhang, C. Qin, S.Y. Chen, C. Zhang, Z. Chen, F. Zhu, S. Y., & Yang, Y.Q. Wei, Y. Z. C. (2015). Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tool. Advanced Drug Delivery Reviews. https://doi.org/10.1016/j.addr.2015.03.01spa
dc.relation.referencesLacapere, J. J. (2017). Membrane Protein Structure and Function Characterization. (Jean-Jacques Lacapere, Ed.). Humana Press. https://doi.org/10.1007/978-14939-7151-0spa
dc.relation.referencesLebeau, M. T., & Robert, J. (2003). Diatom cultivation and biotechnologically relevant products . Part I : Cultivation at various scales, 612–623. https://doi.org/10.1007/s00253-002-1176-4spa
dc.relation.referencesLee, S. Y., Cho, J. M., Chang, Y. K., & Oh, Y. K. (2017). Cell disruption and lipid extraction for microalgal biorefineries: A review. Bioresource Technology, 244, 1317–1328. https://doi.org/10.1016/j.biortech.2017.06.038spa
dc.relation.referencesLeliaert, F., Smith, D. R., Herron, M. D., Verbruggen, H., Delwiche, C. F., & Clerck, 84 O. De. (2012). Phylogeny and Molecular Evolution of the Green Algae, 1–46. https://doi.org/10.1080/07352689.2011.615705spa
dc.relation.referencesLevitan, O., Dinamarca, J., Zelzion, E., Lun, D. S., Guerra, L. T., Kyung, M., & Kim, J. (2015). Remodeling of intermediate metabolism in the diatom Phaeodactylum tricornutum under nitrogen stress, 112(2). https://doi.org/10.1073/pnas.1419818112spa
dc.relation.referencesLi, D., Xie, W., Hao, T., Cai, J., Zhou, T., & Balamurugan, S. (2018). Constitutive and Chloroplast Targeted Expression of Acetyl-CoA Carboxylase in Oleaginous Microalgae Elevates Fatty Acid Biosynthesis.spa
dc.relation.referencesLigeya Perezleo Solórzano, Ricardo Arencibia Jorge, Clara Conill González, Gudelia Achón Veloz, J. A. A. R. (2003). Impacto de la Bioinformática en las ciencias biomédicas, 11(ISSN 1024-9435).spa
dc.relation.referencesLiu, J., Sun, Z., Gerken, H., & Huang, J. (2014). Genetic engineering of the green alga Chlorella zofingiensis : a modified norflurazon-resistant phytoene desaturase gene as a dominant selectable marker. https://doi.org/10.1007/s00253-014-5593-yspa
dc.relation.referencesLu, J., & Deutsch, C. (2009). Electrostatics in the Ribosomal Tunnel Modulate Chain Elongation Rates Jianli. J Mol Biol, 384(1), 73–86. https://doi.org/10.1016/j.jmb.2008.08.089.Electrostaticsspa
dc.relation.referencesLubec, G., Afjehi-Sadat, L., Yang, J. W., & John, J. P. P. (2005). Searching for hypothetical proteins: Theory and practice based upon original data and literature. Progress in Neurobiology, 77(1–2), 90–127. https://doi.org/10.1016/j.pneurobio.2005.10.001spa
dc.relation.referencesMartino, A. De, Meichenin, A., Shi, J., Pan, K., & Bowler, C. (2007). Genetic and phenotypic characterization of Phaeodactylum tricornutum (Bacillariophyceae) accessions. Journal of Phycology, 43(5), 992–1009. https://doi.org/10.1111/j.1529-8817.2007.00384.xspa
dc.relation.referencesMcGuffin, L. J., Bryson, K., & Jones, D. T. (2000). The PSIPRED protein structure prediction server. Bioinformatics, 16(4), 404–405. https://doi.org/10.1093/bioinformatics/16.4.404spa
dc.relation.referencesMedipally, S. R., Yusoff, F. M., Banerjee, S., & Shariff, M. (2015). Microalgae as sustainable renewable energy feedstock for biofuel production. BioMed Research International, 2015. https://doi.org/10.1155/2015/519513spa
dc.relation.referencesMendes, A., Reis, A., & Vasconcelos, R. (2009). Crypthecodinium cohnii with emphasis on DHA production : a review, 199–214. https://doi.org/10.1007/s10811-008-9351-3spa
dc.relation.referencesMiao, X., Wu, Q., & Yang, C. (2004). Fast pyrolysis of microalgae to produce renewable fuels, 71, 855–863. https://doi.org/10.1016/j.jaap.2003.11.004spa
dc.relation.referencesMonroig, Ó., Llanos, R. De, Var, I., & Hontoria, F. (n.d.). Biosynthesis of Polyunsaturated Fatty Acids in Octopus vulgaris : Molecular Cloning and Functional Characterisation of a Stearoyl-CoA Desaturase and an Elongation of Very Long-Chain Fatty Acid 4 Protein. https://doi.org/10.3390/md15030082spa
dc.relation.referencesChen, C., & Evans, L. B. (1989). Phase Partitioning of Biomolecules : Solubilities of Amino Acids, 5(3), 111–118.Eng
dc.relation.referencesMurakami, Y., Tripathi, L. P., & Prathipati, P. (2017). ScienceDirect Network analysis and in silico prediction of protein – protein interactions with applications in drug discovery. Current Opinion in Structural Biology, 44, 134– 142. https://doi.org/10.1016/j.sbi.2017.02.005Eng
dc.relation.referencesNajmanovich, R. J. (2017). Evolutionary studies of ligand binding sites in proteins. Current Opinion in Structural Biology, 45, 85–90. https://doi.org/10.1016/j.sbi.2016.11.024Eng
dc.relation.referencesNeira, J. L., Florencio, F. J., & Muro-pastor, M. I. (2017). Biophysical Chemistry The isolated , twenty-three-residue-long , N-terminal region of the glutamine synthetase inactivating factor binds to its target. Biophysical Chemistry, 228(June), 1–9. https://doi.org/10.1016/j.bpc.2017.05.017Eng
dc.relation.referencesNicoletti, M. (2016). Microalgae Nutraceuticals. Foods, 5(3), 54. https://doi.org/10.3390/foods5030054Eng
dc.relation.referencesNorton, T. A., Melkonian, M., & Andersen, R. A. (1996). Algal biodiversity*. Phycologia, 35(4), 308–326. https://doi.org/10.2216/i0031-8884-35-4-308.1Eng
dc.relation.referencesObermayer, P. S. and B. (2005). Manufacturing Microalgae for Skin care, (16295351), 99–106.Eng
dc.relation.referencesOpen, A. A., Bryant, F. M., Munoz-azcarate, O., Kelly, A. A., Beaudoin, F., Kurup, S., & Eastmond, P. J. (2016). ACYL-ACYL CARRIER PROTEIN DESATURASE2 and 3 Are Responsible for Making Omega-7 Fatty Acids in the, 172(September), 154–162. https://doi.org/10.1104/pp.16.00836Eng
dc.relation.referencesPapers, J. (2003). Azide and Acetate Complexes plus two Iron-depleted Crystal Structures of the Di-iron Enzyme, 1–30.Eng
dc.relation.referencesPeng, K., Zheng, C., Xue, J., & Chen, X. (2014). Delta 5 fatty acid desaturase upregulates the synthesis of polyunsaturated fatty acids in marine diatom Phaeodactylum tricornutum Delta 5 Fatty Acid Desaturase Upregulates the Synthesis of Polyunsaturated Fatty Acids in the Marine Diatom Phaeodactylum tricornutum. https://doi.org/10.1021/jf5031086Eng
dc.relation.referencesPlane, P., Rotation, B., Angles, D., Plot, R., Prediction, A., Plot, H., & Prediction, P. (2014). Additional Bioinformatic Analyses Involving Protein Sequences (pp. 183–207). https://doi.org/10.1016/B978-0-12-410471-6.00008-6Eng
dc.relation.referencesPollastri, G., Martin, A. J. M., Mooney, C., & Vullo, A. (2007). Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information. BMC Bioinformatics, 8, 1–12. https://doi.org/10.1186/1471-2105-8-201Eng
dc.relation.referencesPrabowo, D. A., Hiraishi, O., & Suda, S. (2013). Diversity of Crypthecodinium SPP. (Dinophyceae) from Okinawa prefecture, Japan. Journal of Marine Science and Technology (Taiwan), 21(SUPPL), 181–191. https://doi.org/10.6119/JMST-013-1220-8Eng
dc.relation.referencesPulz, O., & Gross, W. (2004). Valuable products from biotechnology of microalgae. Applied Microbiology and Biotechnology, 65(6), 635–648. https://doi.org/10.1007/s00253-004-1647-xEng
dc.relation.referencesPunta, M., Coggill, P. C., Eberhardt, R. Y., Mistry, J., Tate, J., Boursnell, C., … Finn, R. D. (2012). The Pfam protein families database. Nucleic Acids Research, 40(D1), 290–301. https://doi.org/10.1093/nar/gkr1065Eng
dc.relation.referencesRaja, A., Vipin, C., & Aiyappan, A. (2013). Review Article Biological importance of Marine Algae- An overview, 2(5), 222–227.Eng
dc.relation.referencesRaposo, M. F. D. J., & De Morais, A. M. M. B. (2015a). Microalgae for the prevention of cardiovascular disease and stroke. Life Sciences, 125, 32–41. https://doi.org/10.1016/j.lfs.2014.09.018Eng
dc.relation.referencesRaposo, M. F. D. J., & De Morais, A. M. M. B. (2015b). Microalgae for the prevention of cardiovascular disease and stroke. Life Sciences, 125, 32–41. https://doi.org/10.1016/j.lfs.2014.09.018Eng
dc.relation.referencesRebolloso-Fuentes M.M , Navarro-Perez A, R.-M. J. . and G.-G. (2000). Biomass Nutrient Profiles of the Microalga. Journal of Food Biochemistry, 25(2001), 57– 76.Eng
dc.relation.referencesReijnders, M. J. M. F., van Heck, R. G. A., Lam, C. M. C., Scaife, M. A., dos Santos, V. A. P. M., Smith, A. G., & Schaap, P. J. (2014). Green genes: Bioinformatics and systems-biology innovations drive algal biotechnology. Trends in Biotechnology, 32(12), 617–626. https://doi.org/10.1016/j.tibtech.2014.10.003Eng
dc.relation.referencesRoberts, R. J. (2004). Identifying protein function - A call for community action. PLoS Biology, 2(3), 293–294. https://doi.org/10.1371/journal.pbio.0020042Eng
dc.relation.referencesRodolfi, L., Zittelli, G. C., Bassi, N., Padovani, G., Biondi, N., Bonini, G., & Tredici, M. R. (2009). Microalgae for oil: Strain selection, induction of lipid synthesis and outdoor mass cultivation in a low-cost photobioreactor. Biotechnology and Bioengineering, 102(1), 100–112. https://doi.org/10.1002/bit.22033Eng
dc.relation.referencesRost, B., & Liu, J. (2003). The Predict Protein server. Nucleic Acids Research, 31(13), 3300–3304. https://doi.org/10.1093/nar/gkg508Eng
dc.relation.referencesRost, B., Liu, J., Nair, R., Wrzeszczynski, K. O., & Ofran, Y. (2003). Automatic prediction of protein function. Cellular and Molecular Life Sciences, 60(12), 2637–2650. https://doi.org/10.1007/s00018-003-3114-8Eng
dc.relation.referencesRoy, A., Kucukural, A., & Zhang, Y. (2010). I-TASSER: A unified platform for automated protein structure and function prediction. Nature Protocols, 5(4), 725–738. https://doi.org/10.1038/nprot.2010.5Eng
dc.relation.referencesRoy, S., Chakraborty, H., Kumar, V., Behera, B. K., & Rana, R. S. (2018). In Silico Structural Studies and Molecular Docking Analysis of Delta6-desaturase in HUFA Biosynthetic Pathway. Animal Biotechnology, 29(3), 161–173. https://doi.org/10.1080/10495398.2017.1332639Eng
dc.relation.referencesRubin, G. M., Sheahan, L. C., Kenyon, G. L., DeMarini, D. M., Fuchs, E., Galas, D. J., … Ringe, D. (2002). Defining the mandate of proteomics in the postgenomics era: workshop report. Mol Cell Proteomics, 1(10), 763–780.Eng
dc.relation.referencesSathasivam, R., & Ki, J. S. (2018). A review of the biological activities of microalgal carotenoids and their potential use in healthcare and cosmetic industries. Marine Drugs, 16(1). https://doi.org/10.3390/md16010026Eng
dc.relation.referencesSchlessinger, A., Yachdav, G., & Rost, B. (2006). PROFbval : predict flexible and rigid residues in proteins, 22(7), 891–893. https://doi.org/10.1093/bioinformatics/btl032Eng
dc.relation.referencesSchwede, T., Kopp, J., Guex, N., & Peitsch, M. C. (2003). SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Research, 31(13), 3381–3385. https://doi.org/10.1093/nar/gkg520Eng
dc.relation.referencesScientific, T. (2012). Extinction Coefficients: A guide to understanding extinction coefficients, with emphasis on spectrophotometric determination of protein concentration (Vol. 0747, pp. 4–6).Eng
dc.relation.referencesSerif, M., Dubois, G., Finoux, A., Teste, M., Jallet, D., & Daboussi, F. (n.d.). Onestep generation of multiple gene knock-outs in genome editing. Nature Communications, (2018), 1–10. https://doi.org/10.1038/s41467-018-06378-9Eng
dc.relation.referencesShapiro, L., & Harris, T. (2008). The rough guide to in silico function prediction, or how to use sequence and structure information to predict protein function. PLoS Computational Biology, 4(10). https://doi.org/10.1371/journal.pcbi.1000160Eng
dc.relation.referencesShen, P., Wang, H., Pan, Y., Meng, Y., & Wu, P. (2016). Identification of Characteristic Fatty Acids to Quantify Triacylglycerols in Microalgae, 7(February). https://doi.org/10.3389/fpls.2016.00162Eng
dc.relation.referencesSigrist, C. J. A., Cerutti, L., De Castro, E., Langendijk-Genevaux, P. S., Bulliard, V., Bairoch, A., & Hulo, N. (2009). PROSITE, a protein domain database for functional characterization and annotation. Nucleic Acids Research, 38(SUPPL.1), 161–166. https://doi.org/10.1093/nar/gkp885Eng
dc.relation.referencesSilva Benavides, A. M., Torzillo, G., Kopecký, J., & Masojídek, J. (2013). Productivity and biochemical composition of Phaeodactylum tricornutum (Bacillariophyceae) cultures grown outdoors in tubular photobioreactors and open ponds. Biomass and Bioenergy, 54(0), 115–122. https://doi.org/10.1016/j.biombioe.2013.03.016Spa
dc.relation.referencesSingh, D., Carlson, R., Fell, D., & Poolman, M. (2015). Modelling metabolism of the diatom Phaeodactylum tricornutum. Biochemical Society Transactions, 43(6), 1182–1186. https://doi.org/10.1042/BST20150152Eng
dc.relation.referencesSingh, S., Arora, R. R., Singh, M., & Khosla, S. (2016). Eicosapentaenoic acid versus docosahexaenoic acid as options for vascular risk prevention: A fish story. American Journal of Therapeutics, 23(3), e905–e910. https://doi.org/10.1097/MJT.0000000000000165Eng
dc.relation.referencesSouza, A. De, Requi, R. D., Fernandes, L., Jose, H., Rossetto, S., Domitrovic, T., & Palhano, F. L. (2017). Protein charge distribution in proteomes and its impact on translation, 1–21.Eng
dc.relation.referencesSpolaore, P., Joannis-cassan, C., Duran, E., Isambert, A., Génie, L. De, & Paris, E. C. (2006). Commercial Applications of Microalgae, 101(2), 87–96. https://doi.org/10.1263/jbb.101.87Eng
dc.relation.referencesSrinuanpan, S., Chawpraknoi, A., Chantarit, S., & Prasertsan, P. (2018). A rapid method for harvesting and immobilization of oleaginous microalgae using pellet-forming filamentous fungi and the application in phytoremediation of secondary effluent. International Journal of Phytoremediation, 20(10), 1017– 1024. https://doi.org/10.1080/15226514.2018.1452187Eng
dc.relation.referencesStarr C., Taggart R ., Evers C., S. L. (2011). Biology: The Unity and Diversity of Life. (CENGAGE Learning Custom Publishing, Ed.) (12th ed.).Eng
dc.relation.referencesStone, T. A., Schiller, N., Workewych, N., Heijne, G. Von, & Deber, C. M. (2016). Hydrophobic clusters raise the threshold hydrophilicity for insertion of transmembrane sequences in vivo Hydrophobic clusters raise the threshold hydrophilicity for insertion of transmembrane sequences in vivo. Biochemistry, 1–33. https://doi.org/10.1021/acs.biochem.6b00650 90 Suda, S., AtEng
dc.relation.referencesSuda, S., Atsumi, M., & Miyashita, H. (2002). Taxonomic characterization of a marine Nannochloropsis species, N. oceanica sp. nov. (Eustigmatophyceae). Phycologia, 41(3), 273–279. https://doi.org/10.2216/i0031-8884-41-3-273.1Eng
dc.relation.referencesTonon, T., Harvey, D., Larson, T. R., & Graham, I. A. (2002). Long chain polyunsaturated fatty acid production and partitioning to triacylglycerols in four microalgae, 61, 15–24.Eng
dc.relation.referencesTsui, C. K. M., Marshall, W., Yokoyama, R., Honda, D., Lippmeier, J. C., Craven, K. D., … Berbee, M. L. (2009). Labyrinthulomycetes phylogeny and its implications for the evolutionary loss of chloroplasts and gain of ectoplasmic gliding. Molecular Phylogenetics and Evolution, 50(1), 129–140. https://doi.org/10.1016/j.ympev.2008.09.027Eng
dc.relation.referencesTusnady, G.E., Simon, I. (2001). The HMMTOP transmembrane topology prediction server. Bioinformatics, 17, 849–850.Eng
dc.relation.referencesUr Rehman Hafeez , BariInam, Ali Anwar, and M. H. (2017). A Bayesian Approach for Estimating Protein-Protein Interactions by Integrating Structural and NonStructural Biological Data. Molecular BioSystems, 1, 1–11. https://doi.org/10.1039/C7MB00484BEng
dc.relation.referencesVia, A., & Helmer-citterich, M. (2004). A structural study for the optimisation of functional motifs encoded in protein sequences, 12, 1–12.Eng
dc.relation.referencesVillanova, V., Fortunato, A. E., Singh, D., Bo, D. D., Conte, M., Obata, T., … Finazzi, G. (2017). Investigating mixotrophic metabolism in the model diatom Phaeodactylum tricornutum.Eng
dc.relation.referencesVrieling, E. G., Beelen, T. P. M., van Santen, R. A., & Gieskes, W. W. C. (1999). Diatom silicon biomineralization as an inspirational source of new approaches to silica production. Progress in Industrial Microbiology, 35(C), 39–51. https://doi.org/10.1016/S0079-6352(99)80096-4Eng
dc.relation.referencesWainright PO, Hinkle G, Sogin ML, S. S. (2000). Monophyletic Origins of the Metazoa : An Evolutionary Link with Fungi.Eng
dc.relation.referencesWang, X., Liu, Y., Wei, W., Zhou, X., & Yuan, W. (2017). Enrichment of long-chain polyunsaturated fatty acids by coordinated expression of multiple metabolic nodes in the oleaginous microalga Phaeodactylum tricornutum. https://doi.org/10.1021/acs.jafc.7b02397Eng
dc.relation.referencesWilkins, M. R., Gasteiger, E., Bairoch, A., Sanchez, J., Williams, K. L., Appel, R. D., & Hochstrasser, D. F. (2005). Protein Identification and Analysis Tools in the ExPASy Server, 112, 531–552.Eng
dc.relation.referencesWilson, C. A., Kreychman, J., & Gerstein, M. (2000). Assessing annotation transfer for genomics: Quantifying the relations between protein sequence, structure and function through traditional and probabilistic scores. Journal of Molecular Biology, 297(1), 233–249. https://doi.org/10.1006/jmbi.2000.3550Eng
dc.relation.referencesXue, W., Liu, F., Sun, Z., & Zhou, Z. (2016). A ∆ -9 Fatty Acid Desaturase Gene in the Microalga Myrmecia incisa Reisigl : Cloning and Functional Analysis. https://doi.org/10.3390/ijms17071143Eng
dc.relation.referencesYang, J., Wang, Y., Zhang, Y., Arbor, A., & Arbor, A. (2017). ResQ: An approach to unified estimation of B-factor and residue-specific error in protein structure prediction. J Mol Biol, 428(4), 693–701. https://doi.org/10.1016/j.jmb.2015.09.024.ResQEng
dc.relation.referencesYang, M., Lin, X., Liu, X., Zhang, J., & Ge, F. (2018). Genome Annotation of a Model Diatom Phaeodactylum tricornutum Using an Integrated Proteogenomic Pipeline. Molecular Plant, 11(10), 1292–1307. https://doi.org/10.1016/j.molp.2018.08.005Eng
dc.relation.referencesYang, Y., Du, L., Hosokawa, M., Miyashita, K., Kokubun, Y., Arai, H., & Taroda, H. (2017). Fatty Acid and Lipid Class Composition of the Microalga Phaeodactylum tricornutum, 368(4), 363–368.Eng
dc.relation.referencesZaslavskaia, L. A., Casey Lippmeier, J., Kroth, P. G., Grossman, A. R., & Apt, K. E. (2000). Transformation of the diatom Phaeodactylum tricornutum (Bacillariophyceae) with a variety of selectable marker and reporter genes. Journal of Phycology, 36(2), 379–386. https://doi.org/10.1046/j.1529- 8817.2000.99164.xEng
dc.relation.referencesZhang, Y. (2008). I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, 9, 1–8. https://doi.org/10.1186/1471-2105-9-40Eng
dc.relation.referencesZhang, Y., & Skolnick, J. (2005). TM-align: A protein structure alignment algorithm based on the TM-score. Nucleic Acids Research, 33(7), 2302–2309. https://doi.org/10.1093/nar/gki524Eng
dc.relation.referencesZou, L. G., Chen, J. W., Zheng, D. L., Balamurugan, S., Li, D. W., Yang, W. D., & Liu, J. S. (2018). High ‑ efficiency promoter ‑ driven coordinated regulation of multiple metabolic nodes elevates lipid accumulation in the model microalga Phaeodactylum tricornutum. Microbial Cell Factories, 1–8. https://doi.org/10.1186/s12934-018-0906-yEng
dc.relation.referencesZulu N.N., Zienkiewicz K., Vollheyde K., F. I. . (2018). Current trends to comprehend lipid metabolism in diatoms. Progress in Lipid Research, 70(December 2017), 1–16. https://doi.org/10.1016/j.plipres.2018.03.001Eng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.subjectÁcidos grasosspa
dc.subjectEnergía biomasicaspa
dc.subjectMicroalgasspa
dc.subject.lembBiotecnología -- Investigacionesspa
dc.subject.lembÁcidos grasos -- Investigacionesspa
dc.subject.lembPhaeodactylum tricornutumspa
dc.subject.proposalIn – silicospa
dc.subject.proposalÁcidos grasosspa
dc.subject.proposalPUFAsspa
dc.subject.proposalPhaeodactylum tricornutumspa
dc.subject.subjectenglishIn - silicospa
dc.subject.subjectenglishfatty acidsspa
dc.subject.subjectenglishPUFAsspa
dc.subject.subjectenglishPhaeodactylum tricornutumspa
dc.titleEvaluación in – silico de la estructura y función de la proteína hipotética B7FQK1 de phaeodactylum tricornutumspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.localTesis de Maestríaspa

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