About
Rhiju Das is a professor in the Department of Biochemistry at Stanford University. His research group seeks an agile and predictive understanding of how RNAs code for information processing and replication in living systems. They are creating new computational and chemical tools to enable the precise modeling and design of RNAs. Rhiju Das has been featured in Stanford Medicine Magazine for crowdsourced molecular computers that open a path toward a better tuberculosis test. His lab has also launched initiatives such as the Stanford RNA Folding Challenge on Kaggle, aiming to solve RNA structure prediction problems, including cryo-EM structures of new-to-Nature RNA folds, RNA complexes with small molecules and proteins, and assemblies up to 6000 nucleotides. Additionally, his lab has been recognized for discovering natural RNA-only assemblies, which were highlighted as the first Molecule of the Month by PDB. Rhiju Das has also contributed to the assessment of RNA structure predictions through publications in PROTEINS and has been named an HHMI Investigator along with alumnus Kristy Red-Horse.
Research topics
- Biology
- Genetics
- Computational biology
- Computer Science
- Biochemistry
- Artificial Intelligence
- Bioinformatics
- Programming language
- Human–computer interaction
- Data science
- Engineering
- Systems engineering
- Chemistry
- Virology
- World Wide Web
- Software engineering
- Physics
Selected publications
Geometric deep learning of RNA structure
Science · 2021 · 439 citations
- Artificial Intelligence
- Computer Science
- Artificial Intelligence
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.
<i>De novo</i>3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures
Nucleic Acids Research · 2021 · 82 citations
Senior authorCorresponding- Computational biology
- Biology
- Genetics
The rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta's FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5' UTR; the reverse complement of the 5' UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3' UTR. For eleven of these elements (the stems in SL1-8, reverse complement of SL1-4, FSE, s2m and 3' UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets ('FARFAR2-SARS-CoV-2', https://github.com/DasLab/FARFAR2-SARS-CoV-2; and 'FARFAR2-Apo-Riboswitch', at https://github.com/DasLab/FARFAR2-Apo-Riboswitch') include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.
De novo 3D models of SARS-CoV-2 RNA elements and small-molecule-binding RNAs to aid drug discovery
bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 21 citations
Senior authorCorresponding- Computational biology
- Biology
- Genetics
Abstract The rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta’s FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5’ UTR; the reverse complement of the 5’ UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3’ UTR. For eleven of these elements (the stems in SL1-8, reverse complement of SL1-4, FSE, s2m, and 3’ UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’, https://github.com/DasLab/FARFAR2-SARS-CoV-2 ; and ‘FARFAR2-Apo-Riboswitch’, at https://github.com/DasLab/FARFAR2-Apo-Riboswitch ’) include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.
RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers
RNA · 2020 · 172 citations
- Computational biology
- Biology
- Genetics
RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.
Macromolecular modeling and design in Rosetta: recent methods and frameworks
Nature Methods · 2020 · 902 citations
- Computer Science
- Computer Science
- Software engineering
RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look
RNA · 2020 · 289 citations
Senior authorCorresponding- Biology
- Genetics
- Computational biology
As the COVID-19 outbreak spreads, there is a growing need for a compilation of conserved RNA genome regions in the SARS-CoV-2 virus along with their structural propensities to guide development of antivirals and diagnostics. Here we present a first look at RNA sequence conservation and structural propensities in the SARS-CoV-2 genome. Using sequence alignments spanning a range of betacoronaviruses, we rank genomic regions by RNA sequence conservation, identifying 79 regions of length at least 15 nt as exactly conserved over SARS-related complete genome sequences available near the beginning of the COVID-19 outbreak. We then confirm the conservation of the majority of these genome regions across 739 SARS-CoV-2 sequences subsequently reported from the COVID-19 outbreak, and we present a curated list of 30 "SARS-related-conserved" regions. We find that known RNA structured elements curated as Rfam families and in prior literature are enriched in these conserved genome regions, and we predict additional conserved, stable secondary structures across the viral genome. We provide 106 "SARS-CoV-2-conserved-structured" regions as potential targets for antivirals that bind to structured RNA. We further provide detailed secondary structure models for the extended 5' UTR, frameshifting stimulation element, and 3' UTR. Lastly, we predict regions of the SARS-CoV-2 viral genome that have low propensity for RNA secondary structure and are conserved within SARS-CoV-2 strains. These 59 "SARS-CoV-2-conserved-unstructured" genomic regions may be most easily accessible by hybridization in primer-based diagnostic strategies.
Transcription polymerase–catalyzed emergence of novel RNA replicons
Science · 2020 · 38 citations
- Chemistry
- Biology
- Genetics
Transcription polymerases can exhibit an unusual mode of regenerating certain RNA templates from RNA, yielding systems that can replicate and evolve with RNA as the information carrier. Two classes of pathogenic RNAs (hepatitis delta virus in animals and viroids in plants) are copied by host transcription polymerases. Using in vitro RNA replication by the transcription polymerase of T7 bacteriophage as an experimental model, we identify hundreds of new replicating RNAs, define three mechanistic hallmarks of replication (subterminal de novo initiation, RNA shape-shifting, and interrupted rolling-circle synthesis), and describe emergence from DNA seeds as a mechanism for the origin of novel RNA replicons. These results inform models for the origins and replication of naturally occurring RNA genetic elements and suggest a means by which diverse RNA populations could be propagated as hereditary material in cellular contexts.
Recent grants
Non-coding RNA Structure through a Mutate-and-Map Strategy
NIH · $1.5M · 2012–2018
NIH · $430k · 2022
Internet-scale discovery of RNA bioengineering rules
NIH · $1.8M · 2012–2018
Computational tools for understanding chemically modified RNA structure and interactions
NIH · $334k · 2017–2021
NIH · $302k · 2016
Frequent coauthors
- 80 shared
Wipapat Kladwang
Stanford University
- 49 shared
Andrew M. Watkins
- 49 shared
Kalli Kappel
Broad Institute
- 47 shared
Wah Chiu
Stanford University
- 37 shared
Maya Topf
University Medical Center Hamburg-Eppendorf
- 36 shared
Ramya Rangan
Princeton University
- 34 shared
Rachael C. Kretsch
Stanford University
- 34 shared
Janusz M. Bujnicki
International Institute of Molecular and Cell Biology
Labs
Das LabPI
Awards & honors
- HHMI Investigators (2021)
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