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      RNA‐protein interactions in an unstructured context

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          Abstract

          Despite their importance, our understanding of noncovalent RNA–protein interactions is incomplete. This especially concerns the binding between RNA and unstructured protein regions, a widespread class of such interactions. Here, we review the recent experimental and computational work on RNA–protein interactions in an unstructured context with a particular focus on how such interactions may be shaped by the intrinsic interaction affinities between individual nucleobases and protein side chains. Specifically, we articulate the claim that the universal genetic code reflects the binding specificity between nucleobases and protein side chains and that, in turn, the code may be seen as the Rosetta stone for understanding RNA–protein interactions in general.

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          Most cited references71

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          The origin of the genetic code.

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            Amino acid-base interactions: a three-dimensional analysis of protein-DNA interactions at an atomic level.

            To assess whether there are universal rules that govern amino acid-base recognition, we investigate hydrogen bonds, van der Waals contacts and water-mediated bonds in 129 protein-DNA complex structures. DNA-backbone interactions are the most numerous, providing stability rather than specificity. For base interactions, there are significant base-amino acid type correlations, which can be rationalised by considering the stereochemistry of protein side chains and the base edges exposed in the DNA structure. Nearly two-thirds of the direct read-out of DNA sequences involves complex networks of hydrogen bonds, which enhance specificity. Two-thirds of all protein-DNA interactions comprise van der Waals contacts, compared to about one-sixth each of hydrogen and water-mediated bonds. This highlights the central importance of these contacts for complex formation, which have previously been relegated to a secondary role. Although common, water-mediated bonds are usually non-specific, acting as space-fillers at the protein-DNA interface. In conclusion, the majority of amino acid-base interactions observed follow general principles that apply across all protein-DNA complexes, although there are individual exceptions. Therefore, we distinguish between interactions whose specificities are 'universal' and 'context-dependent'. An interactive Web-based atlas of side chain-base contacts provides access to the collected data, including analyses and visualisation of the three-dimensional geometry of the interactions.
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              Predicting RNA-Protein Interactions Using Only Sequence Information

              Background RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions. Results We propose RPISeq , a family of classifiers for predicting R NA- p rotein i nteractions using only seq uence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens. Conclusions Our experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.
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                Author and article information

                Contributors
                bojan.zagrovic@univie.ac.at
                Journal
                FEBS Lett
                FEBS Lett
                10.1002/(ISSN)1873-3468
                FEB2
                Febs Letters
                John Wiley and Sons Inc. (Hoboken )
                0014-5793
                1873-3468
                21 June 2018
                September 2018
                : 592
                : 17 , Mirroring the multifaceted roles of RNA and its partners in gene expression ( doiID: 10.1002/feb2.2018.592.issue-17 )
                : 2901-2916
                Affiliations
                [ 1 ] Department of Structural and Computational Biology Max F. Perutz Laboratories University of Vienna Austria
                [ 2 ] MM Shemyakin and Yu A Ovchinnikov Institute of Bioorganic Chemistry Russian Academy of Sciences Moscow Russia
                Author notes
                [*] [* ] Correspondence

                B. Zagrovic, Department of Structural and Computational Biology, Max F. Perutz Laboratories, University of Vienna, Campus Vienna Biocenter 5, A‐1030 Vienna, Austria

                Fax: +43 1 4277 9522

                Tel: +43 1 4277 52271

                E‐mail: bojan.zagrovic@ 123456univie.ac.at

                Article
                FEB213116
                10.1002/1873-3468.13116
                6175095
                29851074
                4cfde4ca-0104-44fe-abd4-cac81d235040
                © 2018 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 April 2018
                : 12 May 2018
                : 13 May 2018
                Page count
                Figures: 5, Tables: 0, Pages: 16, Words: 9913
                Funding
                Funded by: European Research Council
                Award ID: 279408
                Funded by: Austrian Science Fund
                Award ID: 30680–B21
                Categories
                Review Article
                Review Articles
                RNA biology
                Custom metadata
                2.0
                feb213116
                September 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.5.0 mode:remove_FC converted:08.10.2018

                Molecular biology
                intrinsically disordered proteins,long noncoding rnas,nucleobase/amino acid interaction affinity scales,rna–protein granules,rna–protein interactions

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