Abstract
This thesis explores the relationships between enzyme mutations and their impact on catalytic function. This is considered from two angles: firstly, in cases where missense mutations lead to pathological processes in humans, and secondly, from a contrasting perspective where mutations confer benefits and are harnessed for the engineering and optimization of enzymes.Through the integration of genomic and proteomic data, two enzymes emerged that are correlated with the toxicity of α-synuclein. The P5B-ATPase ATP13A2 and the phosphatase Synaptojanin-1 (Synj-1) were independently identified to be implicated in neurodegenerative diseases through various mutations.
In Chapter 3, I have modeled ATP13A2, focusing on elucidating details on the active site composition, conformation, and the role of specific amino acids in the catalytic reaction. This is needed to be able to quantitatively investigate the effect of mutations near the active site of the protein, during the different conformational states. I show the binding mode of the ATP substrate in the presence of one and two Mg2+ cations, in the E1 conformational state leading to E1P. The Molecular Dynamics simulations and QM/MM potential energy scans give strong evidence that ATP13A2 completes the autophosphorylation reaction with two Mg2+ ions in the active site. I show that without Arg686 the barrier height of the reaction is considerably higher while Lys859 is crucial for stabilizing the reactant state. Additionally, upon the analysis of the Molecular Dynamics trajectories, several binding pockets are identified, which is likely where the ATP13A2 cargo binds.
In Chapter 4, a method for the classification of enzyme variants is proposed, based on the predicted effect on the catalytic rate, coming from the mutations. This method is based on Molecular Dynamics simulations of the variants at/around the rate-limiting step and integration with Machine Learning algorithms. I look at variants that are similar to wild type Galactose Oxidase and variants with significant structural differences (> 10 mutations). Some of the variants are modeled with non-native substrates to create a model that can classify variants that convert a diverse substrate range. This approach achieves excellent classification accuracy and high precision and recall with the current dataset.
In Chapter 5, structural exploration is conducted on the 5-phosphatase domain of Synaptojanin-1 (Synj-1). The 5-phosphatase domain is modeled embedded in a membrane, to gain insights into its substrate interaction. This modeling work can inform the design of inhibitors for disorders in which Synj-1 is overexpressed.
The thesis concludes by introducing a new method for calculating electron transfer rates. This method can be applied in the investigation of electron transfer in a biological context involving an enzyme mutation.
Overall, this thesis aims to contribute to a deeper understanding of the structural and functional implications of missense mutations in several specific cases, using traditional physics-based computational approaches and to also test the integration of these methods with Machine Learning, in the context of enzyme optimization, particularly when limited experimental data is available.
| Date of Award | 1 Jul 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Edina Rosta (Supervisor), Marco Klaehn (Supervisor) & Hao Fan (Supervisor) |