Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2019 / Cataloged from the PDF of thesis. / Includes bibliographical references (pages 87-90). / In this thesis I observe evolutionary signatures in coding regions to: (1) understand the sources of highly mutable coding regions in mammals; (2) to elucidate a new candidate function for a stop codon readthrough candidate gene, BRI3BP; and (3) to show how rapid sequence-based structure approximations can help predict the structural impact of amino-acid changes. (1) First, I searched for deviations from the evolutionary signatures of coding regions to recognize synonymous acceleration elements (SAEs) in protein coding genes. I showed that these are driven by an increased mutation rate, which persists in the human lineage, in otherwise evolutionarily-constrained protein-coding regions, providing an important resource to better characterize protein-coding constraint in mammals and within humans. (2) Second, I combined evolutionary signatures at the protein-coding and protein-folding level to characterize the functional implication of stop-codon readthrough in BRI3BP. I showed that this readthrough region has conserved spaced hydrophobic residues that pattern match to the -terminal helix forming a coiled-coil-like domain. This change alters BRI3BP function from pro-growth to pro-apoptotic, similarly to VEGF-A. This suggests that readthrough-triggered apoptosis may represent a general mechanism for limiting growth of cells with aberrant ribosomal termination. (3) Third, I used rapid protein-structure approximation of burial of residues based on protein sequence to predict the structural impact of amino acid alterations. I show that the prediction can be improved over using exclusively the hydrophobicity change of the residue. Overall my work demonstrates how evolutionary and structural signatures can be used to predict highly mutational gene regions, readthrough function and structural impact of mutation. / by Maxim Wolf. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/127716 |
| Date | January 2019 |
| Creators | Wolf, Maxim,Ph. D.(Maxim Y.)Massachusetts Institute of Technology. |
| Contributors | Manolis Kellis., Massachusetts Institute of Technology. Computational and Systems Biology Program., Massachusetts Institute of Technology. Computational and Systems Biology Program |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 90 pages, application/pdf |
| Rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided., http://dspace.mit.edu/handle/1721.1/7582 |
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