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dc.contributor.authorLohr, Thomas
dc.date.accessioned2022-05-30T16:17:26Z
dc.date.available2022-05-30T16:17:26Z
dc.date.submitted2021-09-01
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337617
dc.description.abstractDisordered proteins and regions are highly prevalent in the human proteome, and are often implicated in disease. However, methods to study these systems in detail are lacking, and the potential for thermodynamic and kinetic characterisation using experimental methods is limited. Molecular simulations and associated analysis methods have advanced to the point where investigating disordered proteins and their interactions with other (bio-)molecules on an atomistic scale is now possible. Amyloid-β 42 (Aβ42) is an aggregation-prone biomolecule implicated in Alzheimer’s disease, and recent work has shown that small molecules can inhibit the aggregation by dynamically binding to the monomeric form of this disordered protein. In this work I performed long-timescale simulations of Aβ42 with and without the addition of small molecules, and analysed the kinetics of the system using a neural network and a probabilistic state definition. Without a small molecule, the system occupies several states and transitions occur on the range of microseconds. With the small molecule 10074-G5, the dominant disordered state increases in population, and transitions out of this state become slower. Additionally, the conformational entropy of the protein backbone is increased, with the small molecule forming nanosecond-lifetime π-stacking interactions with aromatic side chains. These findings are consistent with nuclear magnetic resonance experiments, and indicate the possibility of designing molecules with high specificity. Another approach to targeting aggregation prone proteins such as Aβ42 consists of using specially engineered single-domain antibodies (sdAbs) with a modified complementarity determining region (CDR). These complementarity determining regions (CDRs) are often disordered and their dynamics are poorly understood. I performed enhanced sampling simulations of both an sdAb designed using a sequence-matching method as well as one developed with a structural approach to better understand their conformational space and provide information to improve selectivity and specificity of designed antibodies. These results show that it is possible to provide a comprehensive characterisation of the kinetics and thermodynamics of disordered proteins in terms of kinetic ensembles, which are defined by the structures and corresponding populations in their different states together with the transition rates between these states.
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectmolecular dynamics
dc.subjectconformational entropy
dc.subjectdisordered protein
dc.subjectmetadynamics
dc.subjectmarkov model
dc.subjectneural network
dc.titleKinetics of Disordered Proteins and their Interactions
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.date.updated2022-05-27T17:09:32Z
dc.identifier.doi10.17863/CAM.85024
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.contributor.orcidLohr, Thomas [0000-0003-2969-810X]
rioxxterms.typeThesis
cam.supervisorVendruscolo, Michele
cam.depositDate2022-05-27
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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