Computational engineering of co-substrate specificity in protein kinases

Romano, Valentina. Computational engineering of co-substrate specificity in protein kinases. 2016, Doctoral Thesis, University of Basel, Faculty of Science.

Available under License CC BY-NC-ND (Attribution-NonCommercial-NoDerivatives).


Official URL: http://edoc.unibas.ch/diss/DissB_11793

Downloads: Statistics Overview


Protein kinases are key regulators of most biochemical pathways and their involvement in different diseases is extensively documented. To identify the protein substrates of kinases is therefore of great importance for elucidating their functional role in the cell and to develop disease-specific therapies. However, the identification of specific kinase substrates is highly challenging due to the large number of protein kinases in cells, their substrate specificity overlap and the lack of absolute specificity of inhibitors. In the late 90s, Shokat and coworkers developed a protein engineering-based method addressing the question of identification of substrates of protein kinases. The approach was based on the mutagenesis of a specific residue to enlarge the ATP binding pocket of the target kinase to accommodate a chemically modified ATP as co-substrate, which would not bind to the native kinase. One of the challenges in applying this method to other kinases is to identify the optimal combination of kinase binding pocket mutations and ATP analogues such that the ATP analogue acts as specific co-substrate for the engineered kinase. Furthermore, the engineered kinases have to remain catalytically active.
This work aims to develop a computational protocol for the engineering of protein kinases. We predict which residues within the binding pocket of the target kinase could be mutated to change its co-substrate specificity from ATP to an ATP analogue. The protocol explores pairings of potential mutations and ATP analogues and can be used as prescreening test in the wider experiment for identifying specific substrates of protein kinases.
The protocol was tested on different tyrosine and serine/threonine protein kinases from the scientific literature where Shokat’s method was applied and experimental data were available. The method correlates well with published experimental data available for the tested protein kinases. Subsequently, we applied the computational protocol to the Mycobacterium tuberculosis protein kinase G, Mtb PknG. Mtb is a pathogenic bacterium and is the causative agent of tuberculosis. Tuberculosis is a widespread infectious disease which causes around two million deaths per year. PknG plays a key role in the survival of Mtb within the host organism. Since its specific downstream substrates as well as its mechanism of action are still unknown, PknG is an attractive target for our computational approach. Our protocol allowed us to design a number of pairs of PknG mutants and ATP analogues. The most promising pairs were tested in vitro, in our laboratory. All in vitro tests were performed by Mohamed-Ali Mahi. The most interesting pair was then used in follow-up ex vivo experiments, performed by the group of Prof. J. Pieters at Biozentrum.
Advisors:Schwede, Torsten and Tramontano, Anna
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (Schwede)
UniBasel Contributors:Romano, Valentina and Schwede, Torsten
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:11793
Thesis status:Complete
Number of Pages:1 Online-Ressource (109 Seiten)
Identification Number:
edoc DOI:
Last Modified:22 Jan 2018 15:52
Deposited On:27 Sep 2016 12:03

Repository Staff Only: item control page