Concepedia

The biochemical basis of microRNA targeting efficacy

Sean E. McGeary, Kathy S. Lin, Charlie Y. Shi, Thy Pham, Namita Bisaria, Gina M. Kelley, David P. Bartel

Science · 2019 · 1.6K citations · 50 references

Open access

TL;DR

MicroRNAs guide Argonaute‑mediated repression of mRNA targets, but limited affinity measurements hinder understanding and prediction of targeting efficacy. The study adapted RNA bind‑n‑seq to measure relative binding affinities of Argonaute‑miRNA complexes to all ≤12‑nt sequences. Using these affinity data, the authors built a biochemical model of miRNA repression and extended it to all miRNAs with a convolutional neural network. The model uncovered miRNA‑specific noncanonical sites, differential canonical affinities, a 100‑fold effect of flanking dinucleotides, and markedly improved cellular repression predictions, enabling quantitative integration of miRNAs into gene‑regulatory networks.

Abstract

MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.