Assistant Professor, Department of Bioengineering
Host: John Hunt
Title: Ribosome dynamics captured by deep sequencing and deep learning
Abstract: Synonymous codon choice can have dramatic effects on ribosome speed, RNA stability, and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs, exposing a need for models of translation that capture the full range of empirically observed variation. Previously, we showed that deep sequencing of ribosome-protected mRNA fragments reveals not only the position of each ribosome but also, unexpectedly, its particular stage of the elongation cycle. This provides a new way to study the detailed kinetics of translation and a new probe with which to identify sequence elements that affect each step in the elongation cycle. More recently, we modeled variation in translation elongation using a feedforward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. We applied our model to design synonymous variants of a fluorescent protein in yeast and concluded that control of translation elongation alone is sufficient to produce large, quantitative differences in protein output.