Defining the Anti-Shine-Dalgarno Sequence Interaction and Quantifying Its Functional Role in Regulating Translation Efficiency | AIChE

Defining the Anti-Shine-Dalgarno Sequence Interaction and Quantifying Its Functional Role in Regulating Translation Efficiency

Authors 

Hockenberry, A. J. - Presenter, Northwestern University
Jewett, M. C., Northwestern University
Amaral, L. A., Northwestern University

Studies dating back to the 1970s established that binding between the anti-Shine-Dalgarno (aSD) sequence on prokaryotic ribosomes and mRNA helps to facilitate translation initiation. However, nearly all of these results are based on over-expressed recombinant protein constructs, so the importance of this sequence interaction over evolutionary time for endogenous genes remains difficult to quantify. Due to their large number and and varying contexts, endogenous genes provide a rich dataset that can shed light on ill defined parameters such as the precise location of aSD binding relative to the start codon that maximizes translation efficiency, the full extents of the aSD sequence, and the functional form of the relationship between aSD binding and translation efficiency. Here, we leverage genome-wide estimates of translation efficiency to determine these parameters and show that anti-Shine-Dalgarno sequence binding increases the translation of endogenous mRNAs on the order of 50%. Our findings highlight the non-linearity of this relationship, showing that translation efficiency is maximized for sequences with intermediate aSD binding strengths. These mechanistic insights are highly robust; we find nearly identical results in ribosome profiling datasets from 3 highly diverged bacteria, as well as independent genome-scale estimates of translation efficiency. Further, we show evidence that intermediate-strength binding sequences maximize translation efficiency in controlled experimental data using recombinant GFP expression. By analyzing the rules that shape and constrain the evolution of endogenous genes, our findings highlight the important role that systems biology and genome-scale analysis methods can play in guiding synthetic design.