Translating information from DNA to proteins is the most energetically expensive process a cellundertakes. The basic principles of translation are simple — free-floating ribosomes bind tomRNAs and translate the transcript one codon at a time, each of which is recognized by acorresponding tRNA. In recent years, our understanding of the molecular basis of translation hasimproved significantly. Advances in structural biology have provided a detailed view of how anindividual ribosome rests at a particular codon on an mRNA, recognizes a tRNA, makes peptidebonds, and then physically translocates to the next codon. Many factors, such as patterns ofcodon usage, mRNA structures, transcript abundances, protein domain-architectures, lengths ofgenes and untranslated regions (UTRs), and initiation and elongation rates have all been shownto modulate protein production. However, there exist two critical gaps in our understanding ofdynamics and evolution of translation. First, we lack a coherent view of how all the various factorsinvolved in translation interact with each other to regulate the global pace of protein synthesis ina cell. Second, we know little about how regulation of protein synthesis changes over time duringorganismal evolution and speciation. To address these critical gaps, we will develop a syntheticmodeling framework for transcription and translation, and parameterize it by generating high-throughput genomic datasets. We will employ this combined modeling/experimental approach tostudy the dynamics and regulation of protein synthesis in a panel of model organisms and evolvingpopulations. In the long-term, this hybrid approach will allow us to study how a cell modulatestranslation in different contexts, including viral infections and systemic diseases such as cancer.This framework will be particularly useful for elucidating the mechanisms of diseases that arisefrom synonymous mutations leading to opportunities for development of therapeutic interventionsto modify protein synthesis in a targeted manner.
|Effective start/end date||9/14/17 → 8/31/22|
- National Institutes of Health (NIH)