Development of Diagnostics and Therapeutics for various neurodegenerative diseases
Title: |
Development of Diagnostics and Therapeutics for various neurodegenerative diseases |
DNr: |
SNIC 2021/23-40 |
Project Type: |
SNIC Small Storage |
Principal Investigator: |
Murugan Natarajan Arul <murugan@kth.se> |
Affiliation: |
Kungliga Tekniska högskolan |
Duration: |
2021-02-01 – 2022-02-01 |
Classification: |
10407 |
Homepage: |
https://www.kth.se/profile/murugan |
Keywords: |
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Abstract
In this project, with the use of force-field, quantum mechanics and machine learning based
approaches, we aim to design drugs and diagnostic agents for various neurodegenerative
diseases such as Alzheimer's and Parkinson's. Due to the increased the average lifespan,
the elder population in Sweden tend develop various aging related diseases. Among these
the so called conformational diseases referred as Alzheimer's (AD) and Parkinson's (PD) are
caused by the aggregation of proteins such as amyloid beta protein, microtubules binding tau
proteins in intra-compartmental and outside neurons. Since there are no effective molecular
reagents found to reverse the aggregation, there are no treatment methods available for
these diseases yet. Also the approaches for the early diagnosis of these diseases are also
not available yet. So, it is of primary importance to develop effective diagnostic and treatment
methods for AD and PD. In this proposal, we aim to contribute to this subject through use of
integrated computational methods involving force-field, quantum mechanics and data driven
approaches. We will use molecular docking, molecular dynamics, ab initio molecular
dynamics and hybrid QM/MM response calculations. The main objective of the project is to
identify molecules with favourable drug-like properties which means the lead compounds
should have optimal binding affinity, binding specificity, pharmacodynamic (Absorption,
distribution, metabolism, excretion and toxicity) and pharmacokinetic (solubility and
bioavailability) properties. These properties in turn depend on the free energy of differences.
For example, the binding affinity itself depends on the difference in free energies of the ligand
in protein-like environment to solvent-like environment. It can be estimated computationally
using different approaches including molecular docking, implicit simulation free energy
calculations approach (MM-GBSA or MM-PBSA) and quantum mechanics fragmentation
scheme. In order to rank the compounds as per experimental data, we need to estimate the
binding free energies within a few kcal/mol accuracy which is a major challenge in
computing. So along with studying various protein-ligand interactions, a major part of the
project also deals with the method development for the reliable estimation of binding free
energies. Once the methods are validated by testing against different available datasets of
binding affinities, they can be used for prediction purpose. In particular, here the targets
associated with various neurodegenerative diseases will be mainly focused. Below we list the
relevant diagnostic and drug targets for neurodegenerative diseases. In the case of AD and
PD, monoamine oxidase-B, monoamine oxidase-A, Acetyl choline esterase, Butyl choline
esterase, Beta-secretase, gamma-secretase amyloid and tau fibrils are suitable as
biomarkers and drug targets. Currently, we have studied the small molecules interaction with
various targets such as BACE, MAO-B, MAO-A amyloid and tau fibril and QM fragmentation
and MM-GBSA approaches were found useful for ranking compounds. So, these approaches
will be used for designing novel small molecular compounds for drug/diagnostic purposes for
these targets. The ranking of compounds using machine learning approaches is in progress.