Das Group

Das group is from Howard Hughes Medical Institute and Department of Biochemistry,Stanford University.
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Prediction Approaches:

The Das lab submitted models for the majority of the Puzzles in Round V. The exceptions were two puzzles in which Das lab members were involved in experimental structure determination of the target or a near-identical molecule (PZ30, human telomerase; and PZ31, the SARS-CoV-2 frameshift stimulation element) and two puzzles that were also targets in the CASP15 competition (puzzles 35 and 36, CPEB ribozymes), for which Das lab members served as assessors. For the remaining 17 of 21 Puzzles, the Das lab made use of tools developed in the Rosetta3 codebase.

Fragment assembly of RNA with full atom refinement (FARFAR) has been the lab’s primary RNA modeling method since the beginning of the RNA Puzzles, with some automation piloted in the RNAWorks3D server and several further improvements collected in the FARFAR2 method, used in RNA PZ22 and later. For most of the Round V puzzles, more than one set of models was submitted, with one set selected manually (typically by R.D. in collaboration with lab members, tagged as Das), another selected based on the Rosetta energy (typically tagged as FARFAR2), and other sets selected by scoring from a tensor field network developed by the Dror group, tagged as “TFN”, or by the eventual name of the network, “ARES”, for Atomic Rotationally Equivariant Scorer). FARFAR(2) was used to submit at least one set of models for 16 of the 17 RNA Puzzles for which any models were submitted by the Das lab. In addition to the FARFAR-based models, for 8 of the 17 RNA Puzzles, the Das lab submitted models from a different Rosetta-based approach, Stepwise Monte Carlo (SWM). This method carries out high resolution conformational search of loops or junctions without use of fragments of prior RNA structures, guided by the all-atom Rosetta energy function, but its application is restricted to targets where only one such unknown loop or junction needs to be built. For Puzzle 11, our lab acquired chemical mapping data and released these data with anonymized sequences to the community in the following entries in the RNA Mapping DataBase: RNAPZ11_STD_0001, DMS, CMCT, and the SHAPE modifier 1M7; RNAPZ11_1M7_0001, RNAPZ11_1M7_0002, RNAPZ11_1M7_0003, for mutate-and-map data with three sets of mutations per nucleotide. De-anonymized versions of these datasets are now available at RNAPZ11_STD_0002, RNAPZ11_1M7_0004, RNAPZ11_1M7_0005, RNAPZ11_1M7_0006.

For all RNA puzzles to date, including targets for which the most accurate overall model was submitted, the Das lab has been able to identify background literature describing the function and, typically, candidate secondary structures or other constraints useful for modeling. For PZ18 (Zika xrRNA), a domain-swapped crystal structure of a different xrRNA served as a template, but expert input was required to inform which regions could be used as templates and which needed to be built de novo with SWM, which was being developed concomitantly with PZ18. For PZ22 (Hatchet ribozyme), secondary structures from RNAfold were refined based on manual inspection of covariance in published sequence alignments. For PZ23 (iMango-III), potential G-quadruplexes as well as the binding site of the ligand were hypothesized after inspection of previously solved Mango aptamers, and these hypotheses were used to seed distinct SWM runs that led to each of the submissions. For PZ24 (VA-I), a pseudoknotted secondary structure had been proposed in the literature and was used to seed FARFAR2 modeling, followed by ARES scoring, which was being developed concomitantly through PZ24-28 in close collaboration with the Dror lab. For Puzzles 26-28 (T-box/tRNA complexes), secondary structures, including the set of intermolecular base pairs between the tRNA CAA tail and the T-box, and templates from previously solved fragments of tRNA/T-box complexes were identified manually and used in FARFAR2 modeling, followed by ARES scoring. For PZ34 (methyltransferase ribozyme), special SWM runs were carried out that modeled the RNA with the enzymatic product m1A at the methylation site, and manual model selection took into consideration the steric accessibility for an exogenous O6-methylguanine as the methyl donor.

Publications:

  1. Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. Proteins. 2023 Dec;91(12):1747-1770.
  2. Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, Jacak R, Kaufman K, Renfrew PD, Smith CA, Sheffler W, Davis IW, Cooper S, Treuille A, Mandell DJ, Richter F, Ban YE, Fleishman SJ, Corn JE, Kim DE, Lyskov S, Berrondo M, Mentzer S, Popović Z, Havranek JJ, Karanicolas J, Das R, Meiler J, Kortemme T, Gray JJ, Kuhlman B, Baker D, Bradley P. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 2011;487:545-74.
  3. Das R, Karanicolas J, Baker D. Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Methods. 2010 Apr;7(4):291-4.
  4. Miao Z, Adamiak RW, Antczak M, Boniecki MJ, Bujnicki J, Chen SJ, Cheng CY, Cheng Y, Chou FC, Das R, Dokholyan NV, Ding F, Geniesse C, Jiang Y, Joshi A, Krokhotin A, Magnus M, Mailhot O, Major F, Mann TH, Piątkowski P, Pluta R, Popenda M, Sarzynska J, Sun L, Szachniuk M, Tian S, Wang J, Wang J, Watkins AM, Wiedemann J, Xiao Y, Xu X, Yesselman JD, Zhang D, Zhang Y, Zhang Z, Zhao C, Zhao P, Zhou Y, Zok T, Żyła A, Ren A, Batey RT, Golden BL, Huang L, Lilley DM, Liu Y, Patel DJ, Westhof E. RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers. RNA. 2020 Aug;26(8):982-995.
  5. Watkins AM, Rangan R, Das R. FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds. Structure. 2020 Aug 4;28(8):963-976.e6.
  6. Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Geometric deep learning of RNA structure. Science. 2021 Aug 27;373(6558):1047-1051. doi: 10.1126/science.abe5650. Erratum in: Science. 2023 Jan 27;379(6630):eadg6616.
  7. Watkins AM, Geniesse C, Kladwang W, Zakrevsky P, Jaeger L, Das R. Blind prediction of noncanonical RNA structure at atomic accuracy. Sci Adv. 2018 May 25;4(5):eaar5316.
  8. Cordero P, Kladwang W, VanLang CC, Das R. Quantitative dimethyl sulfate mapping for automated RNA secondary structure inference. Biochemistry. 2012 Sep 11;51(36):7037-9.
  9. Cordero P, Kladwang W, VanLang CC, Das R. The mutate-and-map protocol for inferring base pairs in structured RNA.) Methods Mol Biol. 2014;1086:53-77.
  10. Tian S, Das R. Primerize-2D: automated primer design for RNA multidimensional chemical mapping. Bioinformatics. 2017 May 1;33(9):1405-1406.
  11. Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL. The Vienna RNA websuite. Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W70-4.