| KEYWORD |
Virtual screening for novel inhibitors of MAP tau aggregation in Alzheimer's disease
keywords ALZHEIMER'S DISEASE, COMPUTATIONAL DRUG DESIGN, HYPERPHOSPHORYLATION, MAP TAU, MICROTUBULES, TAUOPATHY
Reference persons JACEK ADAM TUSZYNSKI
External reference persons Prof. Marco Luppi, Universita degli studi di Bologna
Prof. Maral Aminpour, Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
Research Groups 28- biomedica
Thesis type COMPUTATIONAL
Description Chronic and progressive neurodegenerative diseases (NDDs) such as Alzheimer’s, Parkinson’s, and various dementias pose a critical challenge to the healthcare system because an aging population is rapidly increasing the number of people at risk, yet there is no effective disease-modifying therapy. Lack of solutions for NDDs is due to a complex molecular mechanism that still needs to find a real causative effect. Many attempts to find drugs that can inhibit pathological aggregation of proteins, a typical hallmark in NDDs, had little success to date. Nevertheless, a common process observed at a cellular level that leads to NDDs is the disruption of the structural dynamics of microtubules (MTs), following an abnormal dysregulation of the MT- associated protein tau (MAPtau). The drug design strategy will use an iterative process involving several different computer modeling techniques to be carried out as follows: 1) selection and initial 3D homology modeling of the drug targets, starting from the human tubulin sequences and the templates from the Protein Data bank (PDB) for tubulin and MAPtau; 2) equilibration and refinement of homology models; 3) docking, screening, ranking and re-design of suitable compounds to act as inhibitors; 4) refined modeling of drug-target interactions, specific for the proteins. In the docking procedure, the binding orientation of each compound and its binding affinity for the target protein are then calculated. Algorithms, such as AUTODOCK, are able to identify small molecules that can fit into the ligand binding sites on proteins of known structure and have been used successfully to identify novel protein-ligand interactions. These compounds are further filtered with post-screening methods such as detailed MD calculations. These techniques will allow us to probe new or pre-existing drug-binding sites with positive controls and novel drugs to identify new important interactions. Next, chemi-informatic searches for similar compounds to the top hits and their docking to the targets will allow the identification of possible hits. They will be ranked according to MD simulations for their mode of binding and binding energy estimates. These results will be the basis for commercial acquisition of the top ranked compounds as only publicly available databases will be screened in view of the limited time and budget available.
Required skills basic understanding of computational modelling: docking, molecular dynamics, chemi-informatics
Deadline 11/11/2022
PROPONI LA TUA CANDIDATURA