Development of Diagnostics and Therapeutics for various neurodegenerative diseases
Title: Development of Diagnostics and Therapeutics for various neurodegenerative diseases
SNIC Project: 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:

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.