| ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules A Leaver-Fay, M Tyka, SM Lewis, OF Lange, J Thompson, R Jacak, ... Methods in enzymology 487, 545-574, 2011 | 1988 | 2011 |
| The Rosetta all-atom energy function for macromolecular modeling and design RF Alford, A Leaver-Fay, JR Jeliazkov, MJ O’Meara, FP DiMaio, H Park, ... Journal of chemical theory and computation 13 (6), 3031-3048, 2017 | 1336 | 2017 |
| Macromolecular modeling with rosetta R Das, D Baker Annu. Rev. Biochem. 77 (1), 363-382, 2008 | 1115 | 2008 |
| Functional 5′ UTR mRNA structures in eukaryotic translation regulation and how to find them K Leppek, R Das, M Barna Nature reviews Molecular cell biology 19 (3), 158-174, 2018 | 800 | 2018 |
| Macromolecular modeling and design in Rosetta: recent methods and frameworks JK Leman, BD Weitzner, SM Lewis, J Adolf-Bryfogle, N Alam, RF Alford, ... Nature methods 17 (7), 665-680, 2020 | 619 | 2020 |
| Structure prediction for CASP8 with all‐atom refinement using Rosetta S Raman, R Vernon, J Thompson, M Tyka, R Sadreyev, J Pei, D Kim, ... Proteins: Structure, Function, and Bioinformatics 77 (S9), 89-99, 2009 | 601 | 2009 |
| Automated de novo prediction of native-like RNA tertiary structures R Das, D Baker Proceedings of the National Academy of Sciences 104 (37), 14664-14669, 2007 | 525 | 2007 |
| Are protein force fields getting better? A systematic benchmark on 524 diverse NMR measurements KA Beauchamp, YS Lin, R Das, VS Pande Journal of chemical theory and computation 8 (4), 1409-1414, 2012 | 460 | 2012 |
| Understanding nucleic acid–ion interactions J Lipfert, S Doniach, R Das, D Herschlag Annual review of biochemistry 83 (1), 813-841, 2014 | 451 | 2014 |
| Serverification of molecular modeling applications: the Rosetta Online Server that Includes Everyone (ROSIE) S Lyskov, FC Chou, SO Conchuir, BS Der, K Drew, D Kuroda, J Xu, ... PloS one 8 (5), e63906, 2013 | 413 | 2013 |
| Atomic accuracy in predicting and designing noncanonical RNA structure R Das, J Karanicolas, D Baker Nature methods 7 (4), 291-294, 2010 | 399 | 2010 |
| High-resolution structure prediction and the crystallographic phase problem B Qian, S Raman, R Das, P Bradley, AJ McCoy, RJ Read, D Baker Nature 450 (7167), 259-264, 2007 | 386 | 2007 |
| Spontaneous driving forces give rise to protein− RNA condensates with coexisting phases and complex material properties S Boeynaems, AS Holehouse, V Weinhardt, D Kovacs, J Van Lindt, ... Proceedings of the National Academy of Sciences 116 (16), 7889-7898, 2019 | 384 | 2019 |
| SAFA: semi-automated footprinting analysis software for high-throughput quantification of nucleic acid footprinting experiments R Das, A Laederach, SM Pearlman, D Herschlag, RB Altman Rna 11 (3), 344-354, 2005 | 383 | 2005 |
| Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters MA Jonikas, RJ Radmer, A Laederach, R Das, S Pearlman, D Herschlag, ... Rna 15 (2), 189-199, 2009 | 369 | 2009 |
| RNA design rules from a massive open laboratory J Lee, W Kladwang, M Lee, D Cantu, M Azizyan, H Kim, A Limpaecher, ... Proceedings of the National Academy of Sciences 111 (6), 2122-2127, 2014 | 354 | 2014 |
| RNA regulons in Hox 5′ UTRs confer ribosome specificity to gene regulation S Xue, S Tian, K Fujii, W Kladwang, R Das, M Barna Nature 517 (7532), 33-38, 2015 | 312 | 2015 |
| RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction JA Cruz, MF Blanchet, M Boniecki, JM Bujnicki, SJ Chen, S Cao, R Das, ... Rna 18 (4), 610-625, 2012 | 292 | 2012 |
| Structure prediction for CASP7 targets using extensive all‐atom refinement with Rosetta@ home R Das, B Qian, S Raman, R Vernon, J Thompson, P Bradley, S Khare, ... Proteins: Structure, Function, and Bioinformatics 69 (S8), 118-128, 2007 | 277 | 2007 |
| Geometric deep learning of RNA structure RJL Townshend, S Eismann, AM Watkins, R Rangan, M Karelina, R Das, ... Science 373 (6558), 1047-1051, 2021 | 275 | 2021 |
David BakerUniversity of Washington在 uw.edu 的电子邮件经过验证
