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Computational Approaches for Predicting Drug Targets

Adeyelu, Tolulope Tosin; (2020) Computational Approaches for Predicting Drug Targets. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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This thesis reports the development of several computational approaches to predict human disease proteins and to assess their value as drug targets, using in-house domain functional families (CATH FunFams). CATH-FunFams comprise evolutionary related protein domains with high structural and functional similarity. External resources were used to identify proteins associated with disease and their genetic variations. These were then mapped to the CATH-FunFams together with information on drugs bound to any relatives within the FunFam. A number of novel approaches were then used to predict the proteins likely to be driving disease and to assess whether drugs could be repurposed within the FunFams for targeting these putative driver proteins. The first work chapter of this thesis reports the mapping of drugs to CATHFunFams to identify druggable FunFams based on statistical overrepresentation of drug targets within the FunFam. 81 druggable CATH-FunFams were identified and the dispersion of their relatives on a human protein interaction network was analysed to assess their propensity to be associated with side effects. In the second work chapter, putative drug targets for bladder cancer were identified using a novel computational protocol that expands a set of known bladder cancer genes with genes highly expressed in bladder cancer and highly associated with known bladder cancer genes in a human protein interaction network. 35 new bladder cancer targets were identified in druggable FunFams, for some of which FDA approved drugs could be repurposed from other protein domains in the FunFam. In the final work chapter, protein kinases and kinase inhibitors were analysed. These are an important class of human drug targets. A novel classification protocol was applied to give a comprehensive classification of the kinases which was benchmarked and compared with other widely used kinase classifications. Druginformation from ChEMBL was mapped to the Kinase-FunFams and analyses of protein network characteristics of the kinase relatives in each FunFam used to identify those families likely to be associated with side effects.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Computational Approaches for Predicting Drug Targets
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10090406
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