Rhiju Das

FAST-MaP provides a protocol for per-nucleotide RNA structure characterization using chemical modification and primer-less sequencing without requiring sequencing infrastructure or bioinformatics expertise.

This paper demonstrates that deep learning can accurately design RNA pseudoknots, achieving results comparable to human experts in a competition.

The paper uses cryo-EM to study a ribozyme's structure, revealing how it evolved to polymerize nucleotides and how mutations can enhance its function.

The paper describes a Kaggle competition for RNA 3D structure prediction, showing that template-based methods can outperform deep learning approaches.

The paper identifies a structured RNA region, MLS, necessary and sufficient for Mbp mRNA transport to oligodendrocyte projections and shows it interacts with hnRNP-F, suggesting a structural switch model for mRNA transport to translation.

The paper designs compact RNA sensors for complex functions of multiple inputs using crowdsourced designs and an algorithm, achieving applications like tuberculosis diagnosis.

This paper evaluates blind predictions of dynamic water and ion ensembles around RNA using cryo-EM densities from CASP16, finding that molecular dynamics approaches perform best.

This paper evaluates progress in predicting protein complex structures in CASP16, finding moderate improvements but ongoing challenges in model ranking, stoichiometry prediction, and handling difficult targets.

Across 42 CASP16 nucleic acid targets, accuracy stayed tied to template availability; human experts (Vfold first) led servers and AlphaFold 3, though servers now improve on templates on average.

Structure providers for 18 CASP16 nucleic acid targets find blind predictions capture secondary structure but miss non-canonical interactions, backbone kinks, ligand sites, and intermolecular interfaces.

This study uses community science and RNA structure prediction to design splice-modulating ASOs that enhance exon 16 splicing in a hemophilia A model.

Cryo-EM at 2.9-3.1 Å shows bacterial OLE, ROOL and GOLLD RNAs self-assemble without protein into a dimer, an 8-chain nanocage and a 14-chain nanocage.

Two Tetrahymena ribozyme cryo-EM maps (2.2 and 2.3 Å) reveal cross-validated ordered waters and Mg2+ ions, plus reproducible diffuse solvent density resembling molecular dynamics water networks.

The paper introduces RibonanzaNet, a deep learning model trained on crowdsourced RNA chemical mapping data to predict RNA structure and related properties.

The paper describes the design and characterization of mini-PTC 1.1, a minimal ribosomal PTC RNA optimized through community science and RNA structural analysis.

The paper evaluates CASP15 cryoEM predictions using density maps, showing high accuracy but challenges in side-chain positioning and loop modeling.

This study uses community science and experimental screening to design mutant ribosomes with improved protein synthesis and cell growth in vitro and vivo.

The paper designs and selects ribosomal active site mutants with high activity using a computational approach to overcome limitations in rRNA modification.

This study develops PERSIST-seq and In-line-seq to optimize mRNA stability and translation, finding that in-cell stability is more critical than ribosome load for protein output.

The paper presents a framework for ensuring reproducibility in bio-macromolecular modeling through automated benchmarks and a test server, demonstrating its utility for Rosetta and other tools.

The paper updates the Eterna100 RNA design benchmark to work with ViennaRNA 2 and shows that design performance depends on the folding engine used.

The paper develops a CRISPR-based mutagenesis approach combined with machine learning to model cis-regulatory features of ADAR-mediated RNA editing, explaining over 70% of editing level variation.

The paper identifies conserved 5'UTRs as regulators of translation and reveals their structural dynamics using a new method.

The paper identifies conserved RNA regions in SARS-CoV-2 and SARS-related viruses, predicts their secondary structures, and highlights potential antiviral and diagnostic targets.

A crowdsourced RNA design approach discovers efficient, reversible molecular sensors that couple small molecule inputs to protein binding and fluorogenic outputs.

The paper introduces EternaBrain, an automated RNA design algorithm trained on player moves from an RNA videogame, achieving performance comparable to human players on benchmark puzzles.

The paper quantitatively tests RNA secondary structure prediction algorithms using RNA-MaP, revealing significant deviations between predictions and experimental stabilities.

The paper predicts RNA tertiary structure energetics using an updated helix ensemble model, showing accurate thermodynamic predictions for tertiary assemblies.

The paper introduces LIFFT, a Bayesian framework for analyzing biomolecule folding transitions, enabling uncertainty assessment in high-throughput chemical mapping data.

This paper evaluates a recurrent neural network model, Ribonet, for learning RNA biophysics from high-throughput data, showing it can predict RNA interactions and design functional RNAs but requires more diverse datasets for accurate biophysical modeling.