Biophysical mechanisms of proteomic and fitness effects of synonymous substitutions

NIH RePORTER · NIH · R01 · $322,977 · view on reporter.nih.gov ↗

Abstract

Project Summary/Abstract The overarching goal of the proposed research is to elucidate the mechanisms by which synonymous mutations affect the abundance and turnover of synthesized proteins in cytoplasm with ensuing effect on fitness. We determined that synonymous substitutions in the folA gene encoding the essential E. coli enzyme Dihydrofolate Reductase (DHFR) have broad effects: synonymous substitutions of rare codons to frequent ones at the 5' end of the nucleotide sequence cause significant drop of the amount of mRNA produced while the ones near the 3'- terminus result in depletion of soluble, active DHFR protein. We will broadly investigate the relationship between DHFR folding thermodynamics and kinetics and its post-translation abundance in cytoplasm for a broad coverage of sequence space including both missense and synonymous mutations. We will focus on the role that protein quality control (specific proteases and chaperones) play in amplifying or mitigating fitness effects of synonymous mutations and determine the evolutionary paths by which E. coli recovers the fitness losses incurred by specific synonymous mutations. Lessons from the DHFR study will be used as guidance for a broader exploration on the level of complete genomes of several organisms from E. coli to human with the aim to link conservation of specific synonymous codons to the position-specific aspects of protein folding pathways. To this end we will apply our recent algorithm for structure- based predictions of protein folding pathways to predict location of rare conserved codons based on only the three-dimensional structure of the protein. Altogether these studies will provide deep mechanistic insights into the origin of fitness effects of synonymous substitutions and their evolutionary consequences. It will help to discern the forces of evolutionary selection from sequence analysis data. Further, it will enable to identify the causal relationship between mutations and disease phenotype – an unmet medical need in the era of data driven approaches to development of new therapeutics.

Key facts

NIH application ID
9962428
Project number
5R01GM124044-04
Recipient
HARVARD UNIVERSITY
Principal Investigator
EUGENE I SHAKHNOVICH
Activity code
R01
Funding institute
NIH
Fiscal year
2020
Award amount
$322,977
Award type
5
Project period
2017-09-19 → 2021-06-30