dc.contributor.author | Hleap, Jose | |
dc.date.accessioned | 2017-02-02T18:55:38Z | |
dc.date.available | 2017-02-02T18:55:38Z | |
dc.date.issued | 2017-02-02T18:55:38Z | |
dc.identifier.uri | http://hdl.handle.net/10222/72669 | |
dc.description.abstract | Structural biology has been long concerned about the emergence of protein structures
and the convergence to particular folds. It can be said that protein structures are the
realization of genetic information given thermodynamical and biological constraints.
Given these properties, let’s refer to a structure as a phenotype. As such, protein
structures can be analysed as shapes within a geometric morphometrics framework,
and as a phenotype in a quantitative genetics framework. Here, I present a robust
way to analyse protein structures statistically in either evolutionary or molecular
dynamics sampling. I show how General Procrustes Analysis (GPA) can be applied
to aligned molecular dynamics snapshots, and provide evidence that the scaling component
of GPA is not applicable to protein structures. I also show how analysing
protein structures as shapes can give insights into dynamic and evolutionary patterns.
Analysing proteins as shapes also gives the possibility to apply known techniques to
assess modularity. Traditional techniques have dimensionality limitations. I show
how to overcome these limitations and propose a robust way to analyse protein structure
modularity. I show how a protein can be partitioned into biologically meaningful
clusters, which can be used for description, protein prediction, or analysis of protein
dynamics and evolution. The meaning of such modules is discussed further, and a
hierarchical model for protein structure modularity is proposed. Also, methods to
explore different kinds of modules at different kinds of hierarchy are explored.
Finally, given that protein structures are phenotypes, the potential response to
selection can be assessed by means of comparative quantitative genetics. I show that
traditional comparative approaches have a heavy computational burden, therefore
making the analysis infeasible. Nevertheless, similar approaches are developed to
efficiently and accurately generate the estimations when the phenotypic variance is
partitioned based on repeated measures, using a pooled-within covariance estimation. | en_US |
dc.language.iso | en | en_US |
dc.subject | Structural bioinformatics | en_US |
dc.subject | Geometric morphometrics | en_US |
dc.subject | Comparative quantitative genetics | en_US |
dc.title | COMPARATIVE QUANTITATIVE GENETICS OF PROTEIN STRUCTURES: A COMPOSITE APPROACH TO PROTEIN STRUCTURE EVOLUTION | en_US |
dc.date.defence | 2015-12-08 | |
dc.contributor.department | Department of Biochemistry & Molecular Biology | en_US |
dc.contributor.degree | Doctor of Philosophy | en_US |
dc.contributor.external-examiner | Dr. Jeff Thorne | en_US |
dc.contributor.graduate-coordinator | Dr. Jan K. Rainey | en_US |
dc.contributor.thesis-reader | Dr. David Byers | en_US |
dc.contributor.thesis-reader | Dr. Edward Susko | en_US |
dc.contributor.thesis-reader | Dr. Andrew Roger | en_US |
dc.contributor.thesis-supervisor | Dr. Christian Blouin | en_US |
dc.contributor.ethics-approval | Not Applicable | en_US |
dc.contributor.manuscripts | Yes | en_US |
dc.contributor.copyright-release | Yes | en_US |