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Title: Development of novel strategies for template-based protein structure prediction
Author: Mezulis, Stefans
ISNI:       0000 0004 6346 9973
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
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The most successful methods for predicting the structure of a protein from its sequence rely on identifying homologous sequences with a known structure and building a model from these structures. A key component of these homology modelling pipelines is a model combination method, responsible for combining homologous structures into a coherent whole. Presented in this thesis is poing2, a model combination method using physics-, knowledge- and template-based constraints to assemble proteins using information from known structures. By combining intrinsic bond length, angle and torsional constraints with long- and short-range information extracted from template structures, poing2 assembles simplified protein models using molecular dynamics algorithms. Compared to the widely-used model combination tool MODELLER, poing2 is able to assemble models of approximately equal quality. When supplied only with poor quality templates or templates that do not cover the majority of the query sequence, poing2 significantly outperforms MODELLER. Additionally presented in this work is PhyreStorm, a tool for quickly and accurately aligning the three-dimensional structure of a query protein with the Protein Data Bank (PDB). The PhyreStorm web server provides comprehensive, current and rapid structural comparisons to the protein data bank, providing researchers with another tool from which a range of biological insights may be drawn. By partitioning the PDB into clusters of similar structures and performing an initial alignment to the representatives of each cluster, PhyreStorm is able to quickly determine which structures should be excluded from the alignment. For a benchmarking set of 100 proteins of diverse structure, PhyreStorm is capable of finding over 90% of all high-scoring structures in the PDB, and over 80% of all structures of moderate alignment score.
Supervisor: Sternberg, Michael Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral