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Title: Predicting and characterising protein-protein complexes
Author: Moal, I. H.
ISNI:       0000 0004 2729 0239
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2011
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Macromolecular interactions play a key role in all life processes. The construction and annotation of protein interaction networks is pivotal for the understanding of these processes, and how their perturbation leads to disease. However the extent of the human interactome and the limitations of the experimental techniques which can be brought to bear upon it necessitate theoretical approaches. Presented here are computational investigations into the interactions between biological macromolecules, focusing on the structural prediction of interactions, docking, and their kinetic and thermodynamic characterisation via empirical functions. Firstly, the use of normal modes in docking is investigated. Vibrational analysis of proteins are shown to indicate the motions which proteins are intrinsically disposed to undertake, and the use of this information to model flexible deformations upon protein-protein binding is evaluated. Subsequently SwarmDock, a docking algorithm which models flexibility as a linear combination of normal modes, is presented and benchmarked on a wide variety of test cases. This algorithm utilises state of the art energy functions and metaheuristics to navigate the free energy landscape. Information derived from Langevin dynamics simulations of encounter complex formation in the crowded cytosolic environment can be incorporated into SwarmDock and enhances its performance. Finally, a benchmark of binding free energies derived from the literature is presented. For this benchmark, a large number of molecular descriptors are derived. Machine learning methods are then applied to these in order to derive empirical binding free energy, association rate and dissociation rate functions which take account of the conformational changes which occur upon complexation.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available