Structural and Interaction Studies of the Human Protein Survivin.
Abstract
Cell 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.
Parts of work
G. 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/S2053273316003430 G. 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.2017.05.016 M.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/s41598-017-17071-0 M.J. Garcia-Bonete and G. Katona. Bayesian machine learning improves single wavelength anomalous difference phasing.
[Manuscript] (2019)
Degree
Doctor of Philosophy
University
University of Gothenburg. Faculty of Science
Institution
Department of Chemistry and Molecular Biology ; Institutionen för kemi och molekylärbiologi
Disputation
Fredagen den 5 april 2019, kl. 09:30, Karl Isaksson, institutionen för kemi och molekylärbiologi, Medicinaregatan16
Date of defence
2019-04-05
mj.garcia.bonete@gmail.com
maria-jose.garcia.bonete@gu.se
Date
2019-03-14Author
Garcia-Bonete, Maria-Jose
Keywords
survivin
cell cycle
apoptosis
protein interactions
X-ray crystallography
Bayesian inference
Publication type
Doctoral thesis
ISBN
978-91-7833-398-1
978-91-7833-399-8
Language
eng