Honors & Awards

  • Yale College Dean’s Research Fellow in the Sciences, Yale University (05-08/2008)
  • Yale College Fellow for International Research in the Sciences, Yale University, University of Cambridge (05-08/2009)
  • Sackler Institute Fellow for Biological, Physical, and Engineering Sciences, Raymond and Beverly Sackler Institute, Yale University (05-08/2010)

Education & Certifications

  • Bachelor of Science, Yale University, Molec. Biophys. and Biochem. (2011)

Stanford Advisors

  • Rhiju Das, Doctoral Dissertation Advisor (AC)


All Publications

  • RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures RNA Miao, Z., Adamiak, R. W., Blanchet, M., Boniecki, M., Bujnicki, J. M., Chen, S., Cheng, C., Chojnowski, G., Chou, F., Cordero, P., Cruz, J. A., Ferre-D'Amare, A. R., Das, R., Ding, F., Dokholyan, N. V., Dunin-Horkawicz, S., Kladwang, W., Krokhotin, A., Lach, G., Magnus, M., Major, F., Mann, T. H., Masquida, B., Matelska, D., Meyer, M., Peselis, A., Popenda, M., Purzycka, K. J., Serganov, A., Stasiewicz, J., Szachniuk, M., Tandon, A., Tian, S., Wang, J., Xia, Y., Xu, X., Zhang, J., Zha, P., Zok, T., Westhof, E. 2015; 21 (6): 1066-1084


    This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at

    View details for DOI 10.1261/rna.049502.114

    View details for Web of Science ID 000356316200002

  • Modeling complex RNA tertiary folds with rosetta. Methods in enzymology Cheng, C. Y., Chou, F., Das, R. 2015; 553: 35-64


    Reliable modeling of RNA tertiary structures is key to both understanding these structures' roles in complex biological machines and to eventually facilitating their design for molecular computing and robotics. In recent years, a concerted effort to improve computational prediction of RNA structure through the RNA-Puzzles blind prediction trials has accelerated advances in the field. Among other approaches, the versatile and expanding Rosetta molecular modeling software now permits modeling of RNAs in the 100-300 nucleotide size range at consistent subhelical (~1nm) resolution. Our laboratory's current state-of-the-art methods for RNAs in this size range involve Fragment Assembly of RNA with Full-Atom Refinement (FARFAR), which optimizes RNA conformations in the context of a physically realistic energy function, as well as hybrid techniques that leverage experimental data to inform computational modeling. In this chapter, we give a practical guide to our current workflow for modeling RNA three-dimensional structures using FARFAR, including strategies for using data from multidimensional chemical mapping experiments to focus sampling and select accurate conformations.

    View details for DOI 10.1016/bs.mie.2014.10.051

    View details for PubMedID 25726460

  • Consistent global structures of complex RNA states through multidimensional chemical mapping. eLife Cheng, C. Y., Chou, F., Kladwang, W., Tian, S., Cordero, P., Das, R. 2015; 4


    Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis. Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions. We report that integrating a new method called MOHCA-seq (Multiplexed •OH Cleavage Analysis with paired-end sequencing) with mutate-and-map secondary structure inference guides Rosetta 3D modeling to consistent 1-nm accuracy for intricately folded ncRNAs with lengths up to 188 nucleotides, including a blind RNA-puzzle challenge, the lariat-capping ribozyme. This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon. MCM offers a sequencing-based route to uncovering ncRNA 3D structure, applicable to functionally important but potentially heterogeneous states.

    View details for DOI 10.7554/eLife.07600

    View details for PubMedID 26035425

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