RNAProbe: a web server for normalization and analysis of RNA structure probing data.

RNA molecules play key roles in all living cells. Knowledge of the structural characteristics of RNA molecules allows for a better understanding of the mechanisms of their action. RNA chemical probing allows us to study the susceptibility of nucleotides to chemical modification, and the information obtained can be used to guide secondary structure prediction. These experimental results can be analyzed using various computational tools, which, however, requires additional, tedious steps (e.g., further normalization of the reactivities and visualization of the results), for which there are no fully automated methods. Here, we introduce RNAProbe, a web server that facilitates normalization, analysis, and visualization of the low-pass SHAPE, DMS and CMCT probing results with the modification sites detected by capillary electrophoresis. RNAProbe automatically analyzes chemical probing output data and turns tedious manual work into a one-minute assignment. RNAProbe performs normalization based on a well-established protocol, utilizes recognized secondary structure prediction methods, and generates high-quality images with structure representations and reactivity heatmaps. It summarizes the results in the form of a spreadsheet, which can be used for comparative analyses between experiments. Results of predictions with normalized reactivities are also collected in text files, providing interoperability with bioinformatics workflows. RNAProbe is available at https://rnaprobe.genesilico.pl.

Computational modeling of RNA 3D structure based on experimental data.

RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.

A Comprehensive Mutagenesis Screen of the Adhesion GPCR Latrophilin-1/ADGRL1.

Adhesion G-protein-coupled receptors (aGPCRs) play critical roles in diverse cellular processes in neurobiology, development, immunity, and numerous diseases. The lack of molecular understanding of their activation mechanisms, especially with regard to the transmembrane domains, hampers further studies to facilitate aGPCR-targeted drug development. Latrophilin-1/ADGRL1 is a model aGPCR that regulates synapse formation and embryogenesis, and its mutations are associated with cancer and attention-deficit/hyperactivity disorder. Here, we established functional assays to monitor latrophilin-1 function and showed the activation of latrophilin-1 by its endogenous agonist peptide. Via a comprehensive mutagenesis screen, we identified transmembrane domain residues essential for latrophilin-1 basal activity and for agonist peptide response. Strikingly, a cancer-associated mutation exhibited increased basal activity and failed to rescue the embryonic developmental phenotype in transgenic worms. These results provide a mechanistic foundation for future aGPCR-targeted drug design.