I am interested studying complex RNA processes like pre-mRNA splicing using 2D and 3D computational structural modeling along with experimental structural probing techniques.

Stanford Advisors

  • Rhiju Das, Doctoral Dissertation Advisor (AC)

All Publications

  • RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look. RNA (New York, N.Y.) Rangan, R., Zheludev, I. N., Das, R. 2020


    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 nucleotides 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, frame-shifting element, and 3' UTR. Last, 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 targeted in primer-based diagnostic and oligonucleotide-based therapeutic strategies.

    View details for DOI 10.1261/rna.076141.120

    View details for PubMedID 32398273

  • Using Rosetta for RNA homology modeling. Methods in enzymology Watkins, A. M., Rangan, R., Das, R. 2019; 623: 177–207


    The three-dimensional structures of RNA molecules provide rich and often critical information for understanding their functions, including how they recognize small molecule and protein partners. Computational modeling of RNA 3D structure is becoming increasingly accurate, particularly with the availability of growing numbers of template structures already solved experimentally and the development of sequence alignment and 3D modeling tools to take advantage of this database. For several recent "RNA puzzle" blind modeling challenges, we have successfully identified useful template structures and achieved accurate structure predictions through homology modeling tools developed in the Rosetta software suite. We describe our semi-automated methodology here and walk through two illustrative examples: an adenine riboswitch aptamer, modeled from a template guanine riboswitch structure, and a SAM I/IV riboswitch aptamer, modeled from a template SAM I riboswitch structure.

    View details for DOI 10.1016/bs.mie.2019.05.026

    View details for PubMedID 31239046

  • Determination of Structural Ensembles of Proteins: Restraining vs Reweighting JOURNAL OF CHEMICAL THEORY AND COMPUTATION Rangan, R., Bonomi, M., Heller, G. T., Cesari, A., Bussi, G., Vendruscolo, M. 2018; 14 (12): 6632–41


    The conformational fluctuations of proteins can be described using structural ensembles. To address the challenge of determining these ensembles accurately, a wide range of strategies have recently been proposed to combine molecular dynamics simulations with experimental data. Quite generally, there are two ways of implementing this type of approach, either by applying structural restraints during a simulation, or by reweighting a posteriori the conformations from an a priori ensemble. It is not yet clear, however, whether these two approaches can offer ensembles of equivalent quality. The advantages of the reweighting method are that it can involve any type of starting simulation and that it enables the integration of experimental data after the simulations are run. A disadvantage, however, is that this procedure may be inaccurate when the a priori ensemble is of poor quality. Here, our goal is to systematically compare the restraining and reweighting approaches and to explore the conditions required for the reweighted ensembles to be accurate. Our results indicate that the reweighting approach is computationally efficient and can perform as well as the restraining approach when the a priori sampling is already relatively accurate. More generally, to enable an effective use of the reweighting approach by avoiding the pitfalls of poor sampling, we suggest metrics for the quality control of the reweighted ensembles.

    View details for DOI 10.1021/acs.jctc.8b00738

    View details for Web of Science ID 000453489100044

    View details for PubMedID 30428663