Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794483
Title: Conformation prediction of synthetic ion channels : from structure prediction to structure classification
Author: Aguilar Sanjuan, Broncio
ISNI:       0000 0004 8499 9383
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2019
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Abstract:
The range of conformational states that determine the function of a protein is often unknown. This is the case for most proteins. In particular, for membrane proteins, this is more challenging, as their structures are often difficult to resolve. At present, the computational prediction of membrane protein structures has become a powerful means to overcome this limitation. However, determination of the meaningful conformations that we can relate to observable function is unclear overall. This research aims to provide a modelling framework to determine those conformational states that are relevant to ion conduction for peptide-assembled ion channels. We focus on the particular case of 'cWza Cysteine-mutant' ion channels. These are 'synthetic' ion channels made from the parallel symmetric assembly of eight α-helical peptides with identical amino-acid sequences. Their peptide sequences (cWza Cysteine-mutant) are redesigned versions of the outer-membrane sequence of the 'Wza' complex; the polysaccharide exporter in E. coli. Experiments measuring their conductive activity showed that these channels can either show a voltage-independent single-conductance or a voltage-dependent dual-conductance depending on their peptide sequence. A hypothesis is that a transition between two conformation states takes place for those channels with dual-conductance. While for those channels showing a single conductance, a single conformational state should only exist. However, three-dimensional crystal structures of these channels are unavailable. Based on a combination of techniques, we employed 'symmetric peptide-peptide docking', for prediction of atomic three-dimensional structures of all cWza Cysteine-mutant ion channels; a proposed method for 'conformational classification' of structures, via a description of their most probable inner Van der Waals pore dimensions; and 'molecular dynamics' simulations, to test the robustness of conformational classifications. Overall, we found that two groups of conformations were found for those modelled channels showing dual-conductance in experiments, while for the single-conductance channels, a single conformational group was found. Thus, our results show that observed changes in conductance of synthetic ion channels can indeed be explained by changes in conformation. While when a single conductance is observed, a single conformation exists. We also found that inter-chain atomic interactions such as Hydrogen-bonds and Knobs-Into-Holes played a role in the specificity of conformations and the evolution of their simulated dynamics. Further research needs to verify the validity of our procedure to other synthetic peptide-assembled ion channels that could be a target for de novo design or redesign. Additionally, we developed a molecular dynamics procedure to study with atomic-resolution the ion permeation process across peptide-assembled ion channels; embedded in a lipid-bilayer model and under applied voltage conditions. Details of the ion permeation mechanisms for individual channels are often difficult to resolve via experiments and computation. Application of our procedure to three different types of peptide-assembled ion channels revealed details of their very different ion permeation mechanisms, previously unknown. More time-intense simulations are needed to test the persistence of the identified permeation mechanisms.
Supervisor: Woolfson, Dek ; Liverpool, Tanniemola Sponsor: CONACyT
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.794483  DOI: Not available
Keywords: membrane proteins ; molecular dynamics ; docking ; peptide-petide interactions ; ion channels ; structure prediction ; Synthetic Biology
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