PRIESSTESS: Interpretable, High-performing Models of the Sequence and Structure Preferences of RNA-binding Proteins

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2022 Aug 26

PMID 36018788

Authors

Kaitlin U Laverty

Arttu Jolma

Sara E Pour

Hong Zheng

Debashish Ray

Quaid Morris

Timothy R Hughes

Affiliations

Soon will be listed here.

Abstract

Modelling both primary sequence and secondary structure preferences for RNA binding proteins (RBPs) remains an ongoing challenge. Current models use varied RNA structure representations and can be difficult to interpret and evaluate. To address these issues, we present a universal RNA motif-finding/scanning strategy, termed PRIESSTESS (Predictive RBP-RNA InterpretablE Sequence-Structure moTif regrESSion), that can be applied to diverse RNA binding datasets. PRIESSTESS identifies dozens of enriched RNA sequence and/or structure motifs that are subsequently reduced to a set of core motifs by logistic regression with LASSO regularization. Importantly, these core motifs are easily visualized and interpreted, and provide a measure of RBP secondary structure specificity. We used PRIESSTESS to interrogate new HTR-SELEX data for 23 RBPs with diverse RNA binding modes and captured known primary sequence and secondary structure preferences for each. Moreover, when applying PRIESSTESS to 144 RBPs across 202 RNA binding datasets, 75% showed an RNA secondary structure preference but only 10% had a preference besides unpaired bases, suggesting that most RBPs simply recognize the accessibility of primary sequences.

Citing Articles

Cross-platform DNA motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.

Vorontsov I, Kozin I, Abramov S, Boytsov A, Jolma A, Albu M bioRxiv. 2024; .

PMID: 39605530 PMC: 11601219. DOI: 10.1101/2024.11.11.619379.

GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors.

Jolma A, Hernandez-Corchado A, Yang A, Fathi A, Laverty K, Brechalov A bioRxiv. 2024; .

PMID: 39605368 PMC: 11601218. DOI: 10.1101/2024.11.11.618478.

ePRINT: exonuclease assisted mapping of protein-RNA interactions.

Hawkins S, Mondaini A, Namboori S, Nguyen G, Yeo G, Javed A Genome Biol. 2024; 25(1):140.

PMID: 38807229 PMC: 11134894. DOI: 10.1186/s13059-024-03271-1.

Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead.

Rennie S Genes (Basel). 2024; 15(5).

PMID: 38790258 PMC: 11121098. DOI: 10.3390/genes15050629.

DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures.

Qiao Y, Yang R, Liu Y, Chen J, Zhao L, Huo P Comput Struct Biotechnol J. 2024; 23:617-625.

PMID: 38274994 PMC: 10808905. DOI: 10.1016/j.csbj.2023.12.040.

References

Maticzka D, Lange S, Costa F, Backofen R . GraphProt: modeling binding preferences of RNA-binding proteins. Genome Biol. 2014; 15(1):R17. PMC: 4053806. DOI: 10.1186/gb-2014-15-1-r17. View

Cook K, Vembu S, Ha K, Zheng H, Laverty K, Hughes T . RNAcompete-S: Combined RNA sequence/structure preferences for RNA binding proteins derived from a single-step in vitro selection. Methods. 2017; 126:18-28. DOI: 10.1016/j.ymeth.2017.06.024. View

Gerstberger S, Hafner M, Tuschl T . A census of human RNA-binding proteins. Nat Rev Genet. 2014; 15(12):829-45. PMC: 11148870. DOI: 10.1038/nrg3813. View

Tayara H, Chong K . Improved Predicting of The Sequence Specificities of RNA Binding Proteins by Deep Learning. IEEE/ACM Trans Comput Biol Bioinform. 2020; 18(6):2526-2534. DOI: 10.1109/TCBB.2020.2981335. View

Hiller M, Pudimat R, Busch A, Backofen R . Using RNA secondary structures to guide sequence motif finding towards single-stranded regions. Nucleic Acids Res. 2006; 34(17):e117. PMC: 1903381. DOI: 10.1093/nar/gkl544. View

Pan X, Rijnbeek P, Yan J, Shen H . Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genomics. 2018; 19(1):511. PMC: 6029131. DOI: 10.1186/s12864-018-4889-1. View

Lunde B, Moore C, Varani G . RNA-binding proteins: modular design for efficient function. Nat Rev Mol Cell Biol. 2007; 8(6):479-90. PMC: 5507177. DOI: 10.1038/nrm2178. View

Ben-Bassat I, Chor B, Orenstein Y . A deep neural network approach for learning intrinsic protein-RNA binding preferences. Bioinformatics. 2018; 34(17):i638-i646. DOI: 10.1093/bioinformatics/bty600. View

Murakawa Y, Hinz M, Mothes J, Schuetz A, Uhl M, Wyler E . RC3H1 post-transcriptionally regulates A20 mRNA and modulates the activity of the IKK/NF-κB pathway. Nat Commun. 2015; 6:7367. PMC: 4510711. DOI: 10.1038/ncomms8367. View

10.

Stefanovic L, Longo L, Zhang Y, Stefanovic B . Characterization of binding of LARP6 to the 5' stem-loop of collagen mRNAs: implications for synthesis of type I collagen. RNA Biol. 2015; 11(11):1386-401. PMC: 4615758. DOI: 10.1080/15476286.2014.996467. View

11.

Lambert N, Robertson A, Jangi M, McGeary S, Sharp P, Burge C . RNA Bind-n-Seq: quantitative assessment of the sequence and structural binding specificity of RNA binding proteins. Mol Cell. 2014; 54(5):887-900. PMC: 4142047. DOI: 10.1016/j.molcel.2014.04.016. View

12.

Mukherjee N, Corcoran D, Nusbaum J, Reid D, Georgiev S, Hafner M . Integrative regulatory mapping indicates that the RNA-binding protein HuR couples pre-mRNA processing and mRNA stability. Mol Cell. 2011; 43(3):327-39. PMC: 3220597. DOI: 10.1016/j.molcel.2011.06.007. View

13.

Hall K . Interaction of RNA hairpins with the human U1A N-terminal RNA binding domain. Biochemistry. 1994; 33(33):10076-88. DOI: 10.1021/bi00199a035. View

14.

Nam Y, Chen C, Gregory R, Chou J, Sliz P . Molecular basis for interaction of let-7 microRNAs with Lin28. Cell. 2011; 147(5):1080-91. PMC: 3277843. DOI: 10.1016/j.cell.2011.10.020. View

15.

Du Z, Xiao X, Uversky V . DeepA-RBPBS: A hybrid convolution and recurrent neural network combined with attention mechanism for predicting RBP binding site. J Biomol Struct Dyn. 2020; 40(9):4250-4258. DOI: 10.1080/07391102.2020.1854861. View

16.

Chung T, Kim D . Prediction of binding property of RNA-binding proteins using multi-sized filters and multi-modal deep convolutional neural network. PLoS One. 2019; 14(4):e0216257. PMC: 6485761. DOI: 10.1371/journal.pone.0216257. View

17.

Lorenz R, Bernhart S, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler P . ViennaRNA Package 2.0. Algorithms Mol Biol. 2011; 6:26. PMC: 3319429. DOI: 10.1186/1748-7188-6-26. View

18.

Ray D, Ha K, Nie K, Zheng H, Hughes T, Morris Q . RNAcompete methodology and application to determine sequence preferences of unconventional RNA-binding proteins. Methods. 2016; 118-119():3-15. PMC: 5411283. DOI: 10.1016/j.ymeth.2016.12.003. View

19.

Ruan S, Swamidass S, Stormo G . BEESEM: estimation of binding energy models using HT-SELEX data. Bioinformatics. 2017; 33(15):2288-2295. PMC: 5860122. DOI: 10.1093/bioinformatics/btx191. View

20.

Orenstein Y, Wang Y, Berger B . RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data. Bioinformatics. 2016; 32(12):i351-i359. PMC: 4908343. DOI: 10.1093/bioinformatics/btw259. View