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Specificity and nonspecificity in RNA–protein interactions

Key Points

  • Mammalian cells encode tens of thousands of RNA species and more than 1,000 proteins that interact with them. Many of these proteins can bind to multiple RNAs, and any given RNA can interact with many proteins, giving rise to highly complex networks of cellular RNA–protein interactions.

  • New approaches based on high-throughput sequencing technologies have been developed to quantitatively measure the interaction of proteins simultaneously with large numbers of RNAs.

  • These approaches have revealed that specificity in RNA–protein interactions represents a continuum from low-affinity to high-affinity RNA substrate variants. This continuum is quantitatively described by affinity distributions and comprehensive binding models.

  • Affinity distributions for RNA-binding proteins (RBPs) that are considered specific RNA binders do not differ fundamentally from affinity distributions for nonspecific RBPs, indicating that even the latter have inherent binding specificity. However, physiological targets of specific proteins fall into the high-affinity range of the affinity distribution, whereas physiological targets of nonspecific proteins do not.

  • The biological specificity of RBPs is affected by RNA structure, other proteins, RNA and protein concentrations, and the kinetics of reactions that precede or follow the RNA–protein binding steps.

  • Mechanisms have evolved to amplify or compensate for inherent specificities of RNA-binding domains. These include changes in the size of the RNA-binding site of proteins, the combination of multiple RNA-binding domains in a single RBP and the coordinated binding of multiple RBPs.

Abstract

To fully understand the regulation of gene expression, it is critical to quantitatively define whether and how RNA-binding proteins (RBPs) discriminate between alternative binding sites in RNAs. Here, we describe new methods that measure protein binding to large numbers of RNA variants, and ways to analyse and interpret data obtained by these approaches, including affinity distributions and free energy landscapes. We discuss how the new methodologies and the associated concepts enable the development of inclusive, quantitative models for RNA–protein interactions that transcend the traditional binary classification of RBPs as either specific or nonspecific.

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Figure 1: The major classes of eukaryotic RNAs.
Figure 2: RBP affinity distributions.
Figure 3: RBP binding models.
Figure 4: Free energy landscapes of RNA–protein interactions.
Figure 5: Mechanisms to increase or decrease the intrinsic specificity of RBPs.

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Acknowledgements

The authors thank the members of the Jankowsky and the Harris groups for discussions on the subject of this Review. Work in the authors' laboratories is supported by the US National Institute of General Medical Sciences.

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Correspondence to Eckhard Jankowsky or Michael E. Harris.

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Glossary

Nucleotidyltransferases

Enzymes that catalyse the transfer of a phosphorylated nucleoside from one compound to another.

Charged tRNAs

tRNA molecules that are chemically bonded by 2′ or 3′ aminoacyl linkages to their cognate amino acids.

Competing endogenous RNAs

(ceRNAs). RNAs that regulate other RNA transcripts by competing for shared micro RNAs.

Equilibrium binding free energy

The Gibbs free energy (ΔG), typically measured in units of kcal per mol, for an equilibrium binding reaction that is related to the equilibrium dissociation constant, Kd.

Sequence space

The set of all possible nucleotide sequence combinations of N length defined by 4N.

Hidden Markov models

Probabilistic models used in molecular biology to describe the binding specificity of a protein or ligand derived from a set of bound sequences assuming a Markov process with unobserved (hidden) states.

Neural network analyses

Any of a family of pattern-recognition algorithms that use approximate nonlinear functions using sets of adaptive weights.

Decision tree-guided approaches

Any of a family of statistical classifying methods for sorting data according to attributes (for example, nodes) that form a hierarchy encoded as a tree.

Higher-order Bayesian networks

A type of statistical model that represents a set of random variables and their conditional dependencies expressing the quantitative strength of the connections between variables.

Equilibrium binding affinity

A quantitative description of the energetic strength of the interaction between two molecules, typically expressed by the dissociation constant, Kd.

Association rate constants

The second-order rate constants, with unit Mol−1s−1, that describe the binding (association) of two molecules in solution.

Dissociation rate constants

The first-order rate constants, with unit s−1, that describe the dissociation of a complex between two molecules in solution.

A-form helix

A right-handed double helix formed by nucleic acids, primarily RNA, with characteristic numbers of base pairs per turn, a deep major groove and a shallow minor groove.

Maximal specificity

An optimal mode of molecular recognition resulting in the largest difference in binding free energy between cognate and non-cognate ligands.

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Jankowsky, E., Harris, M. Specificity and nonspecificity in RNA–protein interactions. Nat Rev Mol Cell Biol 16, 533–544 (2015). https://doi.org/10.1038/nrm4032

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