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@PHDTHESIS{Raghavan:990890,
author = {Raghavan, Bharath},
othercontributors = {Carloni, Paolo and Spehr, Marc},
title = {{H}igh performance computing-based {QM}/{MM} simulations
for drug design: application to the non-invasive diagnosis
of {IDH}1-associated glioma},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-07554},
pages = {1 Online-Ressource : Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2024},
abstract = {Structure-based drug design and molecular dynamics (MD),
are routinely used to speed up the pre-clinical stages of
the drug discovery process by predicting ligand-target
poses. Nevertheless, current MD methods (based on classical
physics), may encounter difficulties in describing a large
range of targets, including many metal-containing proteins,
and enzyme transition states. This greatly limits the
applicability of MD to drug discovery. A quantum mechanical
(QM) treatment may be required to accurately investigate
these targets. However, this is very expensive and
infeasible to implement in current drug design pipelines.
Here I show that the density functional theory-based
multiscale Quantum Mechanical/Molecule Mechanical (QM/MM)
MD, as implemented in the recently introduced MiMiC
framework with high performance computing (HPC), has a great
potential to contribute towards such efforts. A
quantum-based HPC virtual screening (QHPC--VS) protocol,
integrating MiMiC-QM/MM MD, is formulated and refined here.
This includes the development of the MiMiCPy software
package for seamless conversion of MM outputs to QM/MM files
of large biomolecules. My protocol is used to study the
therapeutically important Isocitrate Dehydrogenase 1 enzyme.
Very high performance and scaling of MiMiC was achieved for
this application, demonstrating its potential for QM/MM
simulations of biology. The mutant (R132H) isoform of IDH1
has been noted as a potential predictive biomarker for
glioma, introducing a strong rationale to develop
non-invasive imaging methods of this marker like positron
emission tomogrpahy (PET). This involves targeting the
mutant IDH1 active site with selective PET radioactive
ligands or radiotracers. However, due to the nature of the
protein, such selective ligands have not been proposed in
the literature so far. Furthermore, classical MD simulations
of the complicated active site lead to extreme distortion of
the structure, making application of computational
techniques dificult. The QHPC--VS pipeline allowed for
accurately simulating the mut-IDH1 active site at the
quantum level, and suggest ligands that would function as
radioactive tracers for PET. Our simulations predict 15 such
small molecules, out of which is fluorothymidine, an already
well-known PET radiotracer precursor for various other
cancers. This discovery has the potential to greatly
accelerate the development of non-invasive diagnosis of
glioma.},
cin = {137810 / 130000},
ddc = {530},
cid = {$I:(DE-82)137810_20140620$ / $I:(DE-82)130000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2024-07554},
url = {https://publications.rwth-aachen.de/record/990890},
}
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