Citizen gamers help scientists train machine-learning algorithms by analyzing flow cytometry data from immunology and cancer research.

The emergence of a chemical system capable of self-replication and evolution is a critical event in the origin of life. RNA polymerase ribozymes can replicate RNA, but their large size and structural ...

McGill University, a world-renowned research intensive institution, invites applications for the Canada Impact+ Research Chairs (Impact+) Program, open to the rank of Associate or Full Professor. The ...

Federal investment boosts McGill’s research leadership with over $13 million for Canada Research Chairs Today, the Honourable Mélanie Joly, Minister of Industry and Minister responsible for Canada Eco...

Structural RNAs exhibit a vast array of recurrent short three-dimensional (3D) elements found in loop regions involving non-Watson–Crick interactions that help arrange canonical double helices into tertiary structures. Here we present CaCoFold-R3D, a probabilistic grammar that predicts these RNA 3D motifs (also termed modules) jointly with RNA secondary structure over a sequence or alignment. CaCoFold-R3D uses evolutionary information present in an RNA alignment to reliably identify canonical helices (including pseudoknots) by covariation. Here we further introduce the R3D grammars, which also exploit helix covariation that constrains the positioning of the mostly noncovarying RNA 3D motifs. Our method runs predictions over an almost-exhaustive list of over 50 known RNA motifs (‘everything’). Motifs can appear in any nonhelical loop region (including three-way, four-way and higher junctions) (‘everywhere’). All structural motifs as well as the canonical helices are arranged into one single structure predicted by one single joint probabilistic grammar (‘all-at-once’). Our results demonstrate that CaCoFold-R3D is a valid alternative for predicting the all-residue interactions present in a RNA 3D structure. CaCoFold-R3D is fast and easily customizable for novel motif discovery and shows promising value both as a strong input for deep learning approaches to all-atom structure prediction as well as toward guiding RNA design as drug targets for therapeutic small molecules.

Join D2R for this hybrid seminar with Professor Eric Westhof from University of Strasbourg From its origins in the 1960s to today’s cutting-edge approaches, this keynote will highlight evolution of RN...

150 years ago, guanidine was found to affect muscle function via unknown mechanisms. Here, Kavita et al. provide evidence that guanine-sensing RNAs are present in many eukaryotic transcripts relevant ...

CIFAR AI Chair Joelle Pineau believes ethics and the common good must guide technological development.

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RNA is a promising reservoir of new drug targets. Here, the authors introduce RNAmigos2, a structure-based deep learning model that speeds up screening by 10,000x while rivaling docking accuracy, show...

The Human Microbiome Compendium is a unified database of publicly available human gut microbiome 16S samples, built with the integrated data from hundreds of independent projects. The compendium is us...