Transcript levels do not faithfully predict protein levels, due to post-transcriptional regulation of gene expression mediated by RNA binding proteins (RBPs) and non-coding RNAs.
We developed a multivariate linear regression model integrating RBP levels and predicted
RBP-mRNA regulatory interactions from matched transcript and protein datasets. RBPs
significantly improved the accuracy in predicting protein abundance of a portion of the total
modeled mRNAs in three panels of tissues and cells and for different methods employed in
the detection of mRNA and protein. The presence of upstream translation initiation sites
(uTISs) at the mRNA 5’ untranslated regions was strongly associated with improvement in
predictive accuracy. On the basis of these observations, we propose that the recently discovered widespread uTISs in the human genome can be a previously unappreciated substrate of translational control mediated by RBP
