Evolutionary and structural signatures of protein-coding function : synonymous acceleration, read-through, and structural impact of mutations

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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Computational and Systems Biology Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Wolf, Maxim,Ph. D.(Maxim Y.)Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Manolis Kellis.

• Computational and Systems Biology Program.

Statement dc: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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127716

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127716