Biology subjects

Rhiju Das

Publications and source records attributed to Rhiju Das.

8 recordsLinked to original sources

bioRxiv2016

Blind tests of RNA nearest neighbor energy prediction

The predictive modeling and design of biologically active RNA molecules requires understanding the energetic balance amongst their basic components. Rapid developments in computer simulation promise increasingly accurate recovery of RNAs nearest neighbor (NN) free energy parameters, but these methods have not been tested in predictive trials or on non-standard nucleotides. Here, we present the first such tests through a RECCES-Rosetta (Reweighting of Energy-function Collection with Conformational Ensemble Sampling in Rosetta) framework that rigorously models conformational entropy, predicts previously unmeasured NN parameters, and estimates these values systematic uncertainties. RECCES-Rosetta recovers the ten NN parameters for Watson-Crick stacked base pairs and thirty-two single-nucleotide dangling-end parameters with unprecedented accuracies - root-mean-square deviations (RMSD) of 0.28 kcal/mol and 0.41 kcal/mol, respectively. For set-aside test sets, RECCES-Rosetta gives an RMSD of 0.32 kcal/mol on eight stacked pairs involving G-U wobble pairs and an RMSD of 0.99 kcal/mol on seven stacked pairs involving non-standard isocytidine-isoguanosine pairs. To more rigorously assess RECCES-Rosetta, we carried out four blind predictions for stacked pairs involving 2,6-diaminopurine-U pairs, which achieved 0.64 kcal/mol RMSD accuracy when tested by subsequent experiments. Overall, these results establish that computational methods can now blindly predict energetics of basic RNA motifs, including chemically modified variants, with consistently better than 1 kcal/mol accuracy. Systematic tests indicate that resolving the remaining discrepancies will require energy function improvements beyond simply reweighting component terms, and we propose further blind trials to test such efforts.\n\nSignificanceUnderstanding RNA machines and how their behavior can be modulated by chemical modification is increasingly recognized as an important biological and bioengineering problem, with continuing discoveries of riboswitches, mRNA regulons, CRISPR-guided editing complexes, and RNA enzymes. Computational strategies to understanding RNA energetics are being proposed, but have not yet faced rigorous tests. We describe a modeling strategy called RECCES-Rosetta that models the full ensemble of motions of RNA in single-stranded form and in helices, including non-standard nucleotides such as 2,6-diaminopurine, a variant of adenosine. When compared to experiments, including blind tests, the energetic accuracies of RECCES-Rosetta calculations are at levels close to experimental error, suggesting that computation can now be used to predict and design basic RNA energetics.

Biophysics↗

bioRxiv2016

RNA structure through multidimensional chemical mapping

The discoveries of myriad non-coding RNA molecules, each transiting through multiple flexible states in cells or virions, present major challenges for structure determination. Advances in high-throughput chemical mapping give new routes for characterizing entire transcriptomes in vivo, but the resulting one-dimensional data generally remain too information-poor to allow accurate de novo structure determination. Multidimensional chemical mapping (MCM) methods seek to address this challenge. Mutate-and-map (M2), RNA interaction groups by mutational profiling (RING-MaP and MaP-2D analysis) and multiplexed {middle dot}OH cleavage analysis (MOHCA) measure how the chemical reactivities of every nucleotide in an RNA molecule change in response to modifications at every other nucleotide. A growing body of in vitro blind tests and compensatory mutation/rescue experiments indicate that MCM methods give consistently accurate secondary structures and global tertiary structures for ribozymes, ribosomal domains and ligand-bound riboswitch aptamers up to two hundred nucleotides in length. Importantly, MCM analyses provide detailed information on structurally heterogeneous RNA states, such as ligand-free riboswitches, that are functionally important but difficult to resolve with other approaches. The sequencing requirements of currently available MCM protocols scale at least quadratically with RNA length, precluding general application to transcriptomes or viral genomes at present. We propose a modify-crosslink-map expansion to overcome this and other current limitations to resolving the in vivo RNA structurome.

Biochemistry↗

bioRxiv2015

Modeling Small Non-canonical RNA Motifs with the Rosetta FARFAR Server

Non-canonical RNA motifs help define the vast complexity of RNA structure and function, and in many cases, these loops and junctions are on the order of only 10 nucleotides in size. Unfortunately, despite their small size, there is no reliable method to determine the ensemble of lowest energy structures of junctions and loops at atomic accuracy. This chapter outlines straightforward protocols using a webserver for Rosetta Fragment Assembly of RNA with Full Atom Refinement (FARFAR) (http://rosie.rosettacommons.org/rna_denovo/submit) to model the 3D structure of small non-canonical RNA motifs for use in visualizing motifs and for further refinement or filtering with experimental data such as NMR chemical shifts.

Biophysics↗

bioRxiv2015

Rich structure landscapes in both natural and artificial RNAs revealed by mutate-and-map analysis

Landscapes exhibiting multiple secondary structures arise in natural RNA molecules that modulate gene expression, protein synthesis, and viral infection, but it is unclear whether such rich landscapes are special features of biological sequences or whether they can arise without explicit selection. We report herein that high-throughput chemical experiments can isolate an RNAs multiple alternative secondary structures as they are stabilized by systematic mutagenesis (mutate-and-map, M2) and that a computational algorithm, REEFFIT, enables unbiased reconstruction of these states structures and populations. In an in silico benchmark on noncoding RNAs with complex landscapes, M2-REEFFIT recovers 95% of RNA helices present with at least 25% population while maintaining a low false discovery rate (10%) and conservative error estimates. In experimental benchmarks, M2-REEFFIT recovers the structure landscapes of a 35-nt MedLoop hairpin, a 110-nt 16S rRNA fourway junction with an excited state, a 25-nt bistable hairpin, and a 112-nt three-state adenine riboswitch with its expression platform, molecules whose characterization previously required expert mutational analysis and specialized NMR or chemical mapping experiments. With this validation, M2-REEFFIT enabled tests of whether artificial RNA sequences might exhibit complex landscapes in the absence of explicit design. An artificial flavin mononucleotide riboswitch and a randomly generated RNA sequence are found to interconvert between three or more states, including structures for which there was no design, but that could be stabilized through mutations. These results highlight the likely pervasiveness of rich landscapes with multiple secondary structures in both natural and artificial RNAs and demonstrate an automated chemical/computational route for their empirical characterization.

Biochemistry↗

bioRxiv2014

MOHCA-seq: RNA 3D models from single multiplexed proximity-mapping experiments

Large RNAs control myriad biological processes but challenge tertiary structure determination. We report that integrating multiplexed *OH cleavage analysis with tabletop deep sequencing (MOHCA-seq) gives nucleotide-resolution proximity maps of RNA structure from single straightforward experiments. After achieving 1-nm resolution models for RNAs of known structure, MOHCA-seq reveals previously unattainable 3D information for ligand-induced conformational changes in a double glycine riboswitch and the sixth community-wide RNA puzzle, an adenosylcobalamin riboswitch.

Biochemistry↗

bioRxiv2014

Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles

Predicting biological structure has remained challenging for systems such as disordered proteins that take on myriad conformations. Hybrid simulation/experiment strategies have been undermined by difficulties in evaluating errors from computa- tional model inaccuracies and data uncertainties. Building on recent proposals from maximum entropy theory and nonequilibrium thermodynamics, we address these issues through a Bayesian Energy Landscape Tilting (BELT) scheme for computing Bayesian \"hyperensembles\" over conformational ensembles. BELT uses Markov chain Monte Carlo to directly sample maximum-entropy conformational ensembles consistent with a set of input experimental observables. To test this framework, we apply BELT to model trialanine, starting from disagreeing simulations with the force fields ff96, ff99, ff99sbnmr-ildn, CHARMM27, and OPLS-AA. BELT incorporation of limited chemical shift and 3J measurements gives convergent values of the peptides , {beta}, and PPII conformational populations in all cases. As a test of predictive power, all five BELT hyperensembles recover set-aside measurements not used in the fitting and report accu- rate errors, even when starting from highly inaccurate simulations. BELTs principled fxramework thus enables practical predictions for complex biomolecular systems from discordant simulations and sparse data.

Biophysics↗

bioRxiv2014

Correcting a SHAPE-directed RNA structure by a mutate-map-rescue approach

The three-dimensional conformations of non-coding RNAs underpin their biochemical functions but have largely eluded experimental characterization. Here, we report that integrating a classic mutation/rescue strategy with high-throughput chemical mapping enables rapid RNA structure inference with unusually strong validation. We revisit a paradigmatic 16S rRNA domain for which SHAPE (selective 2'-hydroxyl acylation with primer extension) suggested a conformational change between apo-and holo-ribosome conformations. Computational support estimates, data from alternative chemical probes, and mutate-and-map (M2) experiments expose limitations of prior methodology and instead give a near-crystallographic secondary structure. Systematic interrogation of single base pairs via a high-throughput mutation/rescue approach then permits incisive validation and refinement of the M2-based secondary structure and further uncovers the functional conformation as an excited state (25{+/-}5% population) accessible via a single-nucleotide register shift. These results correct an erroneous SHAPE inference of a ribosomal conformational change and suggest a general mutate-map-rescue approach for dissecting RNA dynamic structure landscapes.

Biochemistry↗

bioRxiv2013

RNA Structure Refinement using the ERRASER-Phenix pipeline

The final step of RNA crystallography involves the fitting of coordinates into electron density maps. The large number of backbone atoms in RNA presents a difficult and tedious challenge, particularly when experimental density is poor. The ERRASER-Phenix pipeline can improve an initial set of RNA coordinates automatically based on a physically realistic model of atomic-level RNA interactions. The pipeline couples diffraction-based refinement in Phenix with the Rosetta-based real-space refinement protocol ERRASER (Enumerative Real-Space Refinement ASsisted by Electron density under Rosetta). The combination of ERRASER and Phenix can improve the geometrical quality of RNA crystallographic models while maintaining or improving the fit to the diffraction data (as measured by Rfree). Here we present a complete tutorial for running ERRASER-Phenix through the Phenix GUI, from the command-line, and via an application in the Rosetta On-line Server that Includes Everyone (ROSIE).

Biophysics↗