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Large-scale, cooperative rearrangements underlie the functions of RNA in RNA-protein machines and gene regulation. To understand how such rearrangements are orchestrated, we used high-throughput chemical footprinting to dissect a seemingly concerted rearrangement in P5abc RNA, a paradigm of RNA folding studies. With mutations that systematically disrupt or restore putative structural elements, we found that this transition reflects local folding of structural modules, with modest and incremental cooperativity that results in concerted behavior. First, two distant secondary structure changes are coupled through a bridging three-way junction and Mg²+-dependent tertiary structure. Second, long-range contacts are formed between modules, resulting in additional cooperativity. Given the sparseness of RNA tertiary contacts after secondary structure formation, we expect that modular folding and incremental cooperativity are generally important for specifying functional structures while also providing productive kinetic paths to these structures. Additionally, we expect our approach to be useful for uncovering modularity in other complex RNAs. [ABSTRACT FROM AUTHOR]

Hypothesis-driven experimentation – the scientific method – can be subverted by fraud, irreproducibility, and lack of rigorous predictive tests. A robust solution to these problems may be the ‘massive open laboratory’ model, recently embodied in the internet-scale videogame EteRNA. Deploying similar platforms throughout biology could enforce the scientific method more broadly. [ABSTRACT FROM AUTHOR]

Summary RNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe RNP-denovo , a Rosetta method to simultaneously fold-and-dock RNA to a protein surface. On a benchmark set of diverse RNA-protein complexes not solvable with prior strategies, RNP-denovo consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges involving the spliceosome, telomerase, and a methyltransferase-ribosomal RNA complex in which previous methods gave poor results. When coupled with the same sparse FRET, crosslinking, and functional data used previously, RNP-denovo gave models with significantly improved accuracy. These results open a route to modeling global folds of RNA-protein complexes from low-resolution data. Graphical Abstract Highlights • A Rosetta method, RNP-denovo , folds and docks RNA to protein surfaces • RNP-denovo consistently samples native-like structures for a diverse benchmark set • Applying RNP-denovo to past modeling problems gives models with improved accuracy RNA and protein molecules interact intimately during ribosome translation, pre-mRNA splicing, and other fundamental cellular processes. Structures of the relevant RNA-protein complexes often elude high-resolution determination. Kappel and Das report a computational Rosetta method RNP-denovo that can incorporate sparse experimental data and sample native-like structures of RNA-protein complexes. [ABSTRACT FROM AUTHOR]

Predicting RNA three-dimensional structures from sequence could accelerate understanding of the growing number of RNA molecules being discovered across biology. Rosetta's Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) has shown promise in community-wide blind RNA-Puzzle trials, but lack of a systematic and automated benchmark has left unclear what limits FARFAR performance. Here, we benchmark FARFAR2, an algorithm integrating RNA-Puzzle-inspired innovations with updated fragment libraries and helix modeling. In 16 of 21 RNA-Puzzles revisited without experimental data or expert intervention, FARFAR2 recovers native-like structures more accurate than models submitted during the RNA-Puzzles trials. Remaining bottlenecks include conformational sampling for >80-nucleotide problems and scoring function limitations more generally. Supporting these conclusions, preregistered blind models for adenovirus VA-I RNA and five riboswitch complexes predicted native-like folds with 3- to 14 Å root-mean-square deviation accuracies. We present a FARFAR2 webserver and three large model archives (FARFAR2-Classics, FARFAR2-Motifs, and FARFAR2-Puzzles) to guide future applications and advances. • A new RNA fragment assembly method, FARFAR2, uses RNA-Puzzle-inspired innovations • FARFAR2 recovers native-like structures on six new blind challenges • A FARFAR2 webserver is available at https://rosie.rosettacommons.org/farfar2 • A dataset of millions of FARFAR2-predicted models is available for further study Watkins et al. benchmark FARFAR2, an algorithm for RNA structure prediction that consolidates a decade of ad hoc strategies. FARFAR2 recovers native-like structures more accurately than original challenge submissions for 16 of 21 RNA-Puzzles and achieves native-like preregistered blind predictions for adenovirus VA-I RNA and five riboswitch complexes. [ABSTRACT FROM AUTHOR]

We describe the proceedings and conclusions from the “Workshop on Applications of Protein Models in Biomedical Research” (the Workshop) that was held at the University of California, San Francisco on 11 and 12 July, 2008. At the Workshop, international scientists involved with structure modeling explored (i) how models are currently used in biomedical research, (ii) the requirements and challenges for different applications, and (iii) how the interaction between the computational and experimental research communities could be strengthened to advance the field. [Copyright &y& Elsevier]