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Armaos, A.

Publications and source records attributed to Armaos, A..

3 recordsLinked to original sources

Insights into the structure-driven protein interactivity of RNA molecules

The combination of high-throughput sequencing and in vivo crosslinking approaches leads to the progressive uncovering of the complex interdependence between cellular transcriptome and proteome. Yet the molecular determinants that govern interactions in protein-RNA networks are poorly known at present. Here we used the most recent experimental data to investigate the relationship between RNA structure and protein interactions. Our results show that, independently of the particular technique, the amount of structure in RNA molecules correlates with the capacity of binding to proteins in vitro and in vivo. To validate this observation, we generated an in vitro network that mimics the composition of phase-separated RNA granules. We observed that RNA, when structured, competes with protein binding and can rearrange the interaction network. The simplicity of the principle bears great potential to boost the understanding and modelling of cellular processes involving RNA-protein interactions.

molecular biology

Xist lncRNA forms silencing granules that induce heterochromatin formation and repressive complexes recruitment by phase separation

Main text Main text Microscopy RNA Structure Protein-RNA interactions Granule propensity Material and methods in... References Long non-coding RNAs (lncRNAs) are RNA molecules longer than 200 bases that lack coding potential1,2. They represent a significant portion of the cell transcriptome3 and work as activators or repressors of gene transcription acting on different regulatory mechanisms4-6. Indeed, lncRNAs can act as macro-scaffolds for protein recruitment7-14 and behave as guides and sponges for titrating RNA and proteins, influencing transcription at regulatory regions or triggering transcriptional interfere ...

molecular biology

A method for RNA structure prediction shows evidence for structure in lncRNAs

To compare the secondary structures of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure at single-nucleotide resolution using sequence information, and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modelling. CROSSalign can be applied to perform pair-wise comparisons and is able to find homologues between thousands of matches identifying the exact regions of similarity between profiles of different lengths. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/CROSSalign.

bioinformatics