Print Email Facebook Twitter Sequence features of viral and human Internal Ribosome Entry Sites predictive of their activity Title Sequence features of viral and human Internal Ribosome Entry Sites predictive of their activity Author Gritsenko, A. (TU Delft Pattern Recognition and Bioinformatics) Weingarten-Gabbay, Shira (Weizmann Institute of Science) Elias-Kirma, Shani (Weizmann Institute of Science) Nir, Ronit (Weizmann Institute of Science) de Ridder, D. (TU Delft Pattern Recognition and Bioinformatics; Wageningen University & Research) Segal, Eran (Weizmann Institute of Science) Date 2017-09-01 Abstract Translation of mRNAs through Internal Ribosome Entry Sites (IRESs) has emerged as a prominent mechanism of cellular and viral initiation. It supports cap-independent translation of select cellular genes under normal conditions, and in conditions when cap-dependent translation is inhibited. IRES structure and sequence are believed to be involved in this process. However due to the small number of IRESs known, there have been no systematic investigations of the determinants of IRES activity. With the recent discovery of thousands of novel IRESs in human and viruses, the next challenge is to decipher the sequence determinants of IRES activity. We present the first in-depth computational analysis of a large body of IRESs, exploring RNA sequence features predictive of IRES activity. We identified predictive k-mer features resembling IRES trans-acting factor (ITAF) binding motifs across human and viral IRESs, and found that their effect on expression depends on their sequence, number and position. Our results also suggest that the architecture of retroviral IRESs differs from that of other viruses, presumably due to their exposure to the nuclear environment. Finally, we measured IRES activity of synthetically designed sequences to confirm our prediction of increasing activity as a function of the number of short IRES elements. Subject OA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:133356a5-9200-4fcd-ac4c-1821eb5c1149 DOI https://doi.org/10.1371/journal.pcbi.1005734 ISSN 1553-734X Source PLoS Computational Biology (Print), 13 (9) Part of collection Institutional Repository Document type journal article Rights © 2017 A. Gritsenko, Shira Weingarten-Gabbay, Shani Elias-Kirma, Ronit Nir, D. de Ridder, Eran Segal Files PDF file.pdf 4.64 MB Close viewer /islandora/object/uuid:133356a5-9200-4fcd-ac4c-1821eb5c1149/datastream/OBJ/view