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Virtual screening, molecular dynamics simulatons and chemoinformatics in pharmacology
The Human Genome Project completed in 2003 has uncovered over 90% of the human genome. Since then, more and more omics data have become available. The exponential growth in the amount of biological data prompts progressive developments in computational methods for data analysis to solve various biol...
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| Format: | Doctoral Thesis |
| Language: | English |
| Published: |
Philipps-Universität Marburg
2023
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| Online Access: | PDF Full Text |
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| Summary: | The Human Genome Project completed in 2003 has uncovered over 90% of the human genome. Since then, more and more omics data have become available. The exponential growth in the amount of biological data prompts progressive developments in computational methods for data analysis to solve various biological and pharmaceutical problems. One of the most revolutionary works is the development of AlphaFold, an open-access database that uses an artificial intelligence system to accurately predict protein structure from amino acid sequences. These structures allow scientists to better study and understand newly identified proteins and their mechanisms which can accelerate the drug discovery process.
This thesis focuses on the application of different computational methods in attempts to solve different pharmacological problems. Chapter 1 introduces readers to the contents of this thesis and outlines the work done. The next chapter describes the basic concepts embodied in this thesis, including concepts of molecular dynamics and metadynamics simulations, virtual screenings, and chemoinformatics methods, priming the readers for the subsequent chapters.
The first content chapter of the thesis illustrates the application of homology modelling, whole protein docking, molecular dynamics and metadynamics simulations to unravel pharmacological questions associated with β adrenergic receptors, specifically on selectivity, efficacy, and potency of the β receptor ligands. Proteins are dynamic objects that move and change with different ligands, effectors, and conditions. However, most structural data of protein only captured snapshots of the protein. This chapter highlights the importance of the dynamics of protein and shows that molecular dynamics simulation is an especially useful method in studying and predicting the dynamic effects of different ligands and effectors on the receptors.
The next content chapter showcases the application of large-scale virtual screening to discover potential therapeutic compounds for factor IX. Structure-based screening of large-scale compound libraries has become a common practice in early drug discovery process to identify lead and probe compounds. This method is especially useful for novel pockets or targets where little to nothing is known about the binder or pocket. Factor IX is a protein necessary for the coagulation of blood and has been suggested as a target for anti-thrombotic therapy. In this part of the thesis, we targeted a novel allosteric pocket of factor IX, that was discovered by our collaborator, using a docking approach, in an attempt to find inhibitors of this protein and optimise the hit compounds as in the drug discovery process.
The last content chapter of this thesis is on the generation of a targeted combinatorial virtual library. Virtual compound libraries are frequently used in the process of virtual screening. We generated a virtual di-peptide mimic library that can potentially mimic the action of di-peptides on protein and attempted validation of compounds in our library by docking to WD repeat-containing protein 5 (WDR5). Multiple chemoinformatics approaches were showcased in this chapter, from the comparison of two- and three-dimensional structures of compounds to computing various chemical properties that are important for small-molecule drugs. |
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| DOI: | 10.17192/z2023.0486 |