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Author
Querol Mercadé, Anna
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Abstract
Glioblastoma multiforme (GBM) is the most lethal and common primary brain tumor. Despite great advances in the medical field there is still no effective treatment for this type of cancer, which remains largely incurable. Despite surgery, chemo- and radiotherapy, elimination of the tumor is impossible, so it has a high rate of recurrence. Glioblastoma cancer stem cells play a key role in the formation mainte-nance and recurrence of GBM tumors. Thus, cancer stem cells are a very relevant target for new ther-apeutic approaches. The transmembrane glycoprotein CD133 is a well-known biomarker of cancer stem cells (CSC) in various tumor cells including GBM. However, the CD133 receptor is not only present in CSCs but also in hematopoietic stem cells and other healthy tissues. A promising approach to avoid side effects and damage in targeting therapeutics to the target cells, in this case CSCs, using antibodies that are only activated at the tumor site.
The aim of the present project is to perform the computational in-silico design of a masking method to generate protease-activated antibodies. This design is easily transferable across several antibody spec-ificities. In this project, the work focuses on the use of single-chain variable antibody fragments (scfvs), in particular a scFv targeting CD133. To render this activable antibodies the N-terminus of the scFv will be extended with a protease recognition sequence and covalently tethered on the opposite site of the antigen binding region. The peptide linker will cover the hypervariable regions, thereby preventing antigen recognition by steric hindrance.
To achieve this strategy the 3D structure of the scFv antiCD133 has been obtained using Homology and Ab Initio modeling tools. Landing site candidates upon which to perform the covalent reaction with the lock have been identified thanks to structural analysis. The stability of the models generated have been evaluated by molecular dynamics simulation with an approximation to physiological conditions.
The project has been carried out in collaboration between the Protein Targeted Therapeutics Laboratory research unit from the Group of Material Engineering (GMAT) and the Molecular Modelling unit from the Laboratory of Biochemistry (GQBB).
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