A Quantitative and Predictive Model for RNA Binding by Human Pumilio Proteins

Jarmoskaite, Inga; Denny, Sarah K.; Vaidyanathan, Pavanapuresan P.; Becker, Winston R.; Andreasson, Johan O. L.; Layton, Curtis J.; Kappel, Kalli; Shivashankar, Varun; Sreenivasan, Raashi; Das, Rhiju; Greenleaf, William J.; Herschlag, Daniel

doi:10.1016/j.molcel.2019.04.012

Cited by 66 publications

(159 citation statements)

References 85 publications

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“…To quantify the stabilities of RNA structures in our library, we used the RNA-MaP platform to measure PUM2 equilibrium binding to the oligonucleotide library. The data were fit to a single-site binding model with an additional binding term for non-specific PUM2 association with a PUM2/RNA complex (27). Representative binding curves for varying RNA stabilities are shown in Fig.…”

Section: Validation Of a High-throughput Assay For Rna Structural Stamentioning

confidence: 99%

“…The third condition is that the RBP must have known sequence specificity to pinpoint the RNA residues whose accessibility determines affinity and to account for all potential binding sites. Our recently developed thermodynamic model of PUM2 specificity allowed us to identify sequences in which the designed consensus or mutant sites were the only sites predicted to bind with significant affinity (SI Materials and Methods) (27). Alternative sites that are more accessible than the designed consensus site could make the structure effects appear weaker.…”

Section: Validation Of a High-throughput Assay For Rna Structural Stamentioning

confidence: 99%

“…Collection of empirical data under physiological salt conditions and continued development and testing of models of ionic effects on RNA stability are needed. Finally, there is evidence that RNA structure in cells is substantially less stable than predicted by NN algorithms (27). Some, but very likely not all, of this difference may be due to differences in physiologic ionic conditions and the ionic conditions used to obtain NN parameters.…”

Section: Assessing the Effects Of Ionic Conditions On Nn Parametersmentioning

confidence: 99%

“…The RNA-MaP platform allows direct, parallel equilibrium and kinetic measurements of binding of thousands of RNAs with their RNA or protein interaction partners. Here we used RNA-MaP to determine the stabilities of a library of over 1000 RNA hairpin constructs, by quantifying the extent to which structure formation inhibits binding of the well-characterized single-stranded RNA binding proteins PUM1 and PUM2 (22)(23)(24)(25)(26)(27). Our results reveal substantial and systematic deviations between structure stabilities predicted by current algorithms and measured stabilities.…”

Section: Introductionmentioning

confidence: 95%

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Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding

Becker

Jarmoskaite²,

Kappel

et al. 2019

Preprint

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Nearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are routinely used to understand biological function and to engineer and control new functions in biotechnology. However, NN applications to RNA structural features such as internal and terminal loops rely on approximations and assumptions, with sparse experimental coverage of the vast number of possible sequence and structural features.To test to what extent NN rules accurately predict thermodynamic stabilities across RNAs with non-WC features, we tested their predictions using a quantitative high-throughput assay platform, RNA-MaP. Using a thermodynamic assay with coupled protein binding, we carried out equilibrium measurements for over 1000 RNAs with a range of predicted secondary structure stabilities. Our results revealed substantial scatter and systematic deviations between NN predictions and observed stabilities. Solution salt effects and incorrect or omitted loop parameters contribute to these observed deviations. Our results demonstrate the need to independently and quantitatively test NN computational algorithms to identify their capabilities and limitations. RNA-MaP and related approaches can be used to test computational predictions and can be adapted to obtain experimental data to improve RNA secondary structure and other prediction algorithms. Significance statementRNA secondary structure prediction algorithms are routinely used to understand, predict and design functional RNA structures in biology and biotechnology. Given the vast number of RNA sequence and structural features, these predictions rely on a series of approximations, and independent tests are needed to quantitatively evaluate the accuracy of predicted RNA structural stabilities. Here we measure the stabilities of over 1000 RNA constructs by using a coupled protein binding assay. Our results reveal substantial deviations from the RNA stabilities predicted by popular algorithms, and identify factors contributing to the observed deviations. We demonstrate the importance of quantitative, experimental tests of computational RNA structure predictions and present an approach that can be used to routinely test and improve the prediction accuracy.

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Section: Validation Of a High-throughput Assay For Rna Structural Stamentioning

confidence: 99%

Section: Validation Of a High-throughput Assay For Rna Structural Stamentioning

confidence: 99%

Section: Assessing the Effects Of Ionic Conditions On Nn Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

See 2 more Smart Citations

Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding

Becker

Jarmoskaite²,

Kappel

et al. 2019

Preprint

Self Cite

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show abstract

“…PUMs recruit the CCR4-NOT deadenylase complex, resulting in a shortened poly(A) tail, reduced translation, and increased degradation of the target mRNA [25][26][27][28] . Because PUMs are excellent model of an RBP to study, extensive prior studies on the interaction between PUMs and mRNAs have been performed 8,22,23,29,30 .…”

Section: Introductionmentioning

confidence: 99%

Systematic Analysis of Targets of Pumilio (PUM)-Mediated mRNA Decay Identifies a Role of PUM1 in Regulating DNA Damage Response Pathway

Yamada

Imamachi

Imamura

et al. 2018

Preprint

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RNA-binding proteins (RBPs) play a pivotal role in gene expression by modulating the stability of transcripts; however, the identification of degradation targets of RBPs remains difficult. Here, we identified 48 target mRNAs of human Pumilio 1 (PUM1), an evolutionally conserved RBP, by combined analysis of transcriptome-wide mRNA stabilities and the binding of mRNAs to PUM1. Here, we developed an approach to identify mRNA targets of Pumilio 1 (PUM1), an evolutionally conserved RBP. By combined analysis of transcriptome-wide mRNA stabilities and the binding of mRNAs to PUM1, we identified 48 mRNAs that both bound to PUM1 and exhibited PUM1-dependent degradation. Analysis of changes in the abundance of PUM1 and its targets in RNA-seq data indicated that DNA-damaging agents negatively regulated PUM1-mediated mRNA decay. Cells exposed to cisplatin had reduced PUM1 abundance and increased PCNA and UBE2A mRNAs, encoding proteins involved in DNA repair by translesion synthesis (TLS). Cells overexpressing PUM1 exhibited impaired DNA synthesis and TLS and increased sensitivity to the cytotoxic effect of cisplatin. Thus, our method identified targets of PUM1-mediated decay and revealed that cells respond to DNA damage by inhibiting PUM1-mediated mRNA decay to activate TLS.

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Approaches for measuring the dynamics of RNA–protein interactions

Licatalosi

Jankowsky

2019

WIREs RNA

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RNA-protein interactions are pivotal for the regulation of gene expression from bacteria to human. RNA-protein interactions are dynamic; they change over biologically relevant timescales. Understanding the regulation of gene expression at the RNA level therefore requires knowledge of the dynamics of RNA-protein interactions. Here, we discuss the main experimental approaches to measure dynamic aspects of RNA-protein interactions. We cover techniques that assess dynamics of cellular RNA-protein interactions that accompany biological processes over timescales of hours or longer and techniques measuring the kinetic dynamics of RNA-protein interactions in vitro.

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A Quantitative and Predictive Model for RNA Binding by Human Pumilio Proteins

Cited by 66 publications

References 85 publications

Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding

Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding

Systematic Analysis of Targets of Pumilio (PUM)-Mediated mRNA Decay Identifies a Role of PUM1 in Regulating DNA Damage Response Pathway

Approaches for measuring the dynamics of RNA–protein interactions

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