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dc.contributor.authorGarcia-Bonete, Maria-Jose
dc.date.accessioned2019-03-14T08:50:54Z
dc.date.available2019-03-14T08:50:54Z
dc.date.issued2019-03-14
dc.identifier.isbn978-91-7833-398-1
dc.identifier.isbn978-91-7833-399-8
dc.identifier.otherhttp://hdl.handle.net/2077/59179
dc.identifier.urihttp://hdl.handle.net/2077/59179
dc.description.abstractCell division and cell death (apoptosis) are two essential processes to maintain the specific number of cells in all multicellular organisms. In humans, the misregulation of these processes leads to severe diseases, such as cancer, and neurological, inflammatory or autoimmune diseases. Proteins are the most versatile macromolecules in all living organisms and are the orchestra directors of the majority of cellular processes. Their three-dimensional structure and the interaction with other molecules are essential for their correct biological function. This work focus on small human protein survivin which plays an important role in cell division and apoptosis, and has been extensively reported in clinical research. Our aim was to discover new interaction partners of survivin, and to study their specific binding and structure to better understand its function. We successfully used microarray peptide technology to determine new possible interaction partners and microscale thermophoresis to confirm these interactions. The direct interaction between the shugoshin-like protein family and survivin has been reported and highlights its importance in cell division. In addition, this thesis exhibits the powerful multivariate Bayesian inference approach for data analysis by focussing on addressing X-ray crystallography problems of experimental phasing for molecular structure determination. This approach has also been successfully applied to determine the binding curve and to calculate the interaction strength between two molecules, and avoids manual treatment and human subjective bias.sv
dc.language.isoengsv
dc.relation.haspartG. Katona, M.J. Garcia-Bonete and I. Lundholm. Estimating the difference between structure-factor amplitudes using multivariate Bayesian inference, Acta Cryst. A (2016) A72:406-411 ::doi::10.1107/S2053273316003430sv
dc.relation.haspartG. Gravina, C. Wasén, M.J. Garcia-Bonete, M. Turkkila, M.C. Erlandsson, S. Töyrä Silfverswärd, M. Brisslert, R. Pullerits, K.M. Andersson, G. Katona and M.I Bokarewa. Survivin in autoimmune disease, Autoimmunity Reviews (2017) 16:845-855 ::doi::10.1016/j.autrev.2017.05.016sv
dc.relation.haspartM.J. Garcia-Bonete, M. Jensen, C.V. Recktenwald, S. Rocha, V. Stadler, M. Bokarewa and G. Katona. Bayesian Analysis of MicroScale Thermophoresis Data to Quantify Affinity of Protein: Protein Interactions with Human Survivin, Scientific Reports ::doi::10.1038/s41598-017-17071-0sv
dc.relation.haspartM.J. Garcia-Bonete and G. Katona. Bayesian machine learning improves single wavelength anomalous difference phasing. [Manuscript] (2019)sv
dc.subjectsurvivinsv
dc.subjectcell cyclesv
dc.subjectapoptosissv
dc.subjectprotein interactionssv
dc.subjectX-ray crystallographysv
dc.subjectBayesian inferencesv
dc.titleStructural and Interaction Studies of the Human Protein Survivin.sv
dc.typeTextswe
dc.type.svepDoctoral thesiseng
dc.gup.mailmj.garcia.bonete@gmail.comsv
dc.gup.mailmaria-jose.garcia.bonete@gu.sesv
dc.type.degreeDoctor of Philosophysv
dc.gup.originUniversity of Gothenburg. Faculty of Sciencesv
dc.gup.departmentDepartment of Chemistry and Molecular Biology ; Institutionen för kemi och molekylärbiologisv
dc.gup.defenceplaceFredagen den 5 april 2019, kl. 09:30, Karl Isaksson, institutionen för kemi och molekylärbiologi, Medicinaregatan16sv
dc.gup.defencedate2019-04-05
dc.gup.dissdb-fakultetMNF


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