Rhiju Das was born in 1978 in Houston, Texas. He received his undergraduate education at Harvard University, where he studied physics. Das then went on to complete a master's degree as a Marshall Scholar at Cambridge University and University College London, researching experimental cosmology and molecular phylogenetics. He obtained his Ph.D. in physics from Stanford University under the supervision of Sebastian Doniach and Daniel Herschlag.
Das began his career as a Jane Coffin Childs postdoctoral fellow, working on protein structure prediction with David Baker at the University of Washington. In 2009, he joined the biochemistry department at Stanford University, where he was promoted to a tenured position in 2016. Das was selected as a Howard Hughes investigator in 2021 and, in the same year, co-founded the RNA design startup Inceptive. Das currently holds the position of Professor of Biochemistry and Physics at Stanford University School of Medicine.
Das' research focuses on seeking a predictive understanding of how RNA molecules and their complexes form molecular machines that are fundamental to life. He develops methods for simulating and computationally designing RNA molecules, as well as experimental approaches to inferring RNA structure from multidimensional chemical mapping measurements. Das is also known for his work on demonstrating the application of cryo-electron microscopy to accelerate the structure determination of RNA.
Das directs the Eterna massive open laboratory, which integrates an internet-scale videogame with massively parallel experiments and machine learning. This project aims to empower citizen scientists to invent medicine. During the COVID-19 pandemic, Das and his team used the Eterna platform to investigate potentially shelf-stable RNA vaccines. Das also helped launch and serve as an assessor for the first RNA category in the Critical Assessment of Structure Prediction in 2022.
Rhiju Das HHMI & Stanford University http://daslab.stanford.edu/ 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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:LkGwnXOMwfcC Cited by: 1969
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:DkZNVXde3BIC Cited by: 1306
Macromolecular modeling with rosetta R Das, D Baker Annu. Rev. Biochem. 77 (1), 363-382, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:u5HHmVD_uO8C Cited by: 1106
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:yFnVuubrUp4C Cited by: 791
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:CB2v5VPnA5kC Cited by: 609
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:Y0pCki6q_DkC Cited by: 595
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:2osOgNQ5qMEC Cited by: 522
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:R3hNpaxXUhUC Cited by: 460
Understanding nucleic acid–ion interactions J Lipfert, S Doniach, R Das, D Herschlag Annual review of biochemistry 83 (1), 813-841, 2014 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:08ZZubdj9fEC Cited by: 441
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:SeFeTyx0c_EC Cited by: 408
Atomic accuracy in predicting and designing noncanonical RNA structure R Das, J Karanicolas, D Baker Nature methods 7 (4), 291-294, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:_FxGoFyzp5QC Cited by: 397
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:d1gkVwhDpl0C Cited by: 383
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:u-x6o8ySG0sC Cited by: 382
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:LgRImbQfgY4C Cited by: 379
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:zYLM7Y9cAGgC Cited by: 368
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:l7t_Zn2s7bgC Cited by: 350
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:1qzjygNMrQYC Cited by: 310
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:hFOr9nPyWt4C Cited by: 290
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:qjMakFHDy7sC Cited by: 276
RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look R Rangan, IN Zheludev, RJ Hagey, EA Pham, HK Wayment-Steele, … Rna 26 (8), 937-959, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:MhiOAD_qIWkC Cited by: 267
David Baker UKqIqRsAAAAJ
Kalli Kappel 5HuHCMQAAAAJ
Joseph D. Yesselman kcLsmp4AAAAJ
Hannah K Wayment-Steele MHNfkuUAAAAJ
Kyle A. Beauchamp fLHTqc0AAAAJ
Vijay Pande cWe_xpUAAAAJ
Rhiju Das HHMI & Stanford University http://daslab.stanford.edu/ 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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:LkGwnXOMwfcC Cited by: 1969
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:DkZNVXde3BIC Cited by: 1306
Macromolecular modeling with rosetta R Das, D Baker Annu. Rev. Biochem. 77 (1), 363-382, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:u5HHmVD_uO8C Cited by: 1106
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:yFnVuubrUp4C Cited by: 791
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:CB2v5VPnA5kC Cited by: 609
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:Y0pCki6q_DkC Cited by: 595
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:2osOgNQ5qMEC Cited by: 522
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:R3hNpaxXUhUC Cited by: 460
Understanding nucleic acid–ion interactions J Lipfert, S Doniach, R Das, D Herschlag Annual review of biochemistry 83 (1), 813-841, 2014 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:08ZZubdj9fEC Cited by: 441
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:SeFeTyx0c_EC Cited by: 408
Atomic accuracy in predicting and designing noncanonical RNA structure R Das, J Karanicolas, D Baker Nature methods 7 (4), 291-294, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:_FxGoFyzp5QC Cited by: 397
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:d1gkVwhDpl0C Cited by: 383
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:u-x6o8ySG0sC Cited by: 382
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:LgRImbQfgY4C Cited by: 379
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:zYLM7Y9cAGgC Cited by: 368
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:l7t_Zn2s7bgC Cited by: 350
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:1qzjygNMrQYC Cited by: 310
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:hFOr9nPyWt4C Cited by: 290
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:qjMakFHDy7sC Cited by: 276
RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look R Rangan, IN Zheludev, RJ Hagey, EA Pham, HK Wayment-Steele, … Rna 26 (8), 937-959, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:MhiOAD_qIWkC Cited by: 267
David Baker googlescholarauthor_id:UKqIqRsAAAAJ
Kalli Kappel googlescholarauthor_id:5HuHCMQAAAAJ
Joseph D. Yesselman googlescholarauthor_id:kcLsmp4AAAAJ
Hannah K Wayment-Steele googlescholarauthor_id:MHNfkuUAAAAJ
Kyle A. Beauchamp googlescholarauthor_id:fLHTqc0AAAAJ
Vijay Pande googlescholarauthorid:cWexpUAAAAJ
Rhiju Das HHMI & Stanford University http://daslab.stanford.edu/ 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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:LkGwnXOMwfcC Cited by: 1969
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:DkZNVXde3BIC Cited by: 1306
Macromolecular modeling with rosetta R Das, D Baker Annu. Rev. Biochem. 77 (1), 363-382, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:u5HHmVD_uO8C Cited by: 1106
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:yFnVuubrUp4C Cited by: 791
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:CB2v5VPnA5kC Cited by: 609
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:Y0pCki6q_DkC Cited by: 595
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:2osOgNQ5qMEC Cited by: 522
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:R3hNpaxXUhUC Cited by: 460
Understanding nucleic acid–ion interactions J Lipfert, S Doniach, R Das, D Herschlag Annual review of biochemistry 83 (1), 813-841, 2014 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:08ZZubdj9fEC Cited by: 441
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:SeFeTyx0c_EC Cited by: 408
Atomic accuracy in predicting and designing noncanonical RNA structure R Das, J Karanicolas, D Baker Nature methods 7 (4), 291-294, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:_FxGoFyzp5QC Cited by: 397
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:d1gkVwhDpl0C Cited by: 383
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:u-x6o8ySG0sC Cited by: 382
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:LgRImbQfgY4C Cited by: 379
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:zYLM7Y9cAGgC Cited by: 368
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:l7t_Zn2s7bgC Cited by: 350
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:1qzjygNMrQYC Cited by: 310
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:hFOr9nPyWt4C Cited by: 290
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:qjMakFHDy7sC Cited by: 276
RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look R Rangan, IN Zheludev, RJ Hagey, EA Pham, HK Wayment-Steele, … Rna 26 (8), 937-959, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:MhiOAD_qIWkC Cited by: 267
David Baker googlescholarauthorid davidbaker.md:UKqIqRsAAAAJ
Kalli Kappel googlescholarauthorid kallikappel.md:5HuHCMQAAAAJ
Joseph D. Yesselman googlescholarauthorid josephd._yesselman.md:kcLsmp4AAAAJ
Hannah K Wayment-Steele googlescholarauthorid hannahk_wayment-steele.md:MHNfkuUAAAAJ
Kyle A. Beauchamp googlescholarauthorid kylea._beauchamp.md:fLHTqc0AAAAJ
Vijay Pande googlescholarauthorid vijaypande.md:cWe_xpUAAAAJ
Rhiju Das
HHMI & Stanford University
http://daslab.stanford.edu/
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:LkGwnXOMwfcC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:DkZNVXde3BIC
Macromolecular modeling with rosetta R Das, D Baker Annu. Rev. Biochem. 77 (1), 363-382, 2008 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:u5HHmVD_uO8C
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:yFnVuubrUp4C
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:CB2v5VPnA5kC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:Y0pCki6q_DkC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:2osOgNQ5qMEC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:R3hNpaxXUhUC
Understanding nucleic acid–ion interactions J Lipfert, S Doniach, R Das, D Herschlag Annual review of biochemistry 83 (1), 813-841, 2014 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:08ZZubdj9fEC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:SeFeTyx0c_EC
Atomic accuracy in predicting and designing noncanonical RNA structure R Das, J Karanicolas, D Baker Nature methods 7 (4), 291-294, 2010 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:_FxGoFyzp5QC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:d1gkVwhDpl0C
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:u-x6o8ySG0sC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:LgRImbQfgY4C
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:zYLM7Y9cAGgC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:l7t_Zn2s7bgC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:1qzjygNMrQYC
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:hFOr9nPyWt4C
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 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:qjMakFHDy7sC
RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look R Rangan, IN Zheludev, RJ Hagey, EA Pham, HK Wayment-Steele, … Rna 26 (8), 937-959, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=juUEPSAAAAAJ&citationforview=juUEPSAAAAAJ:MhiOAD_qIWkC
David Baker googlescholarauthorid davidbaker.md:UKqIqRsAAAAJ
Kalli Kappel googlescholarauthorid kallikappel.md:5HuHCMQAAAAJ
Joseph D. Yesselman googlescholarauthorid josephd._yesselman.md:kcLsmp4AAAAJ
Hannah K Wayment-Steele googlescholarauthorid hannahk_wayment-steele.md:MHNfkuUAAAAJ
Kyle A. Beauchamp googlescholarauthorid kylea._beauchamp.md:fLHTqc0AAAAJ
Vijay Pande googlescholarauthorid vijaypande.md:cWe_xpUAAAAJ
Summary: Rhiju Das (born 1978 in Houston, Texas) is a computational biochemist and a professor of biochemistry and physics at Stanford University. Research in his lab seeks a predictive understanding of how RNA molecules and their complexes form molecular machines fundamental to life.
URL: https://en.wikipedia.org/wiki/Rhiju_Das
Page ID: 55856763
Categories:
Links:
Content: Rhiju Das (born 1978 in Houston, Texas) is a computational biochemist and a professor of biochemistry and physics at Stanford University. Research in his lab seeks a predictive understanding of how RNA molecules and their complexes form molecular machines fundamental to life.
Education Das was trained as a physicist before switching to biochemistry. His undergraduate education was at Harvard, in physics, followed by master's research as a Marshall scholar at Cambridge University and University College London in experimental cosmology and molecular phylogenetics. He completed his Ph.D. in physics at Stanford University, supervised by Sebastian Doniach and Daniel Herschlag.
Career Das was a Jane Coffin Childs postdoctoral fellow working on protein structure prediction with David Baker at the University of Washington. He joined Stanford's biochemistry department in 2009 and was promoted with tenure in 2016. He was selected to be a Howard Hughes investigator in 2021, and co-founded the RNA design startup Inceptive that same year.
Research Das develops methods to simulate and computationally design RNA molecules as well as experimental methods to infer RNA structure from multidimensional chemical mapping measurements. Integrating these efforts, Das directs the Eterna massive open laboratory, which integrates an internet-scale videogame with massively parallel experiments and machine learning. The project aims to empower citizen scientists to invent medicine. In 2020, Das and his staff used the Eterna platform to investigate potentially shelf-stable RNA vaccines for COVID-19. An interview with Das about this work was featured in an episode of Nova, "Decoding COVID-19", in May 2020. Das also is known for his work on demonstrating the application of cryo-electron microscopy to accelerate the structure determination of RNA. He helped launch and served as an assessor for the first RNA category in the Critical Assessment of Structure Prediction in 2022.
== References ==
Youtube Title: Dr. Rhiju Das - Ribonanza: big data for RNA structure prediction
Youtube Link: link
Youtube Channel Name: CASP RNA SIG
Youtube Channel Link: https://www.youtube.com/@CASPRNASIG
Dr. Rhiju Das - Ribonanza: big data for RNA structure prediction
Youtube Title: Rhiju Das
Youtube Link: link
Youtube Channel Name: Stanford Medicine X
Youtube Channel Link: https://www.youtube.com/@StanfordMedicineX
Rhiju Das
Youtube Title: RNA Collaborative - Bay Area RNA Club (BARC), April 26, 2023
Youtube Link: link
Youtube Channel Name: RNA Collaborative Seminar Series
Youtube Channel Link: https://www.youtube.com/@rnacollaborativeseminarser5993
RNA Collaborative - Bay Area RNA Club (BARC), April 26, 2023
Youtube Title: NGBS2022 Talk 10: RNA modelling and design - Rhiju Das
Youtube Link: link
Youtube Channel Name: MRC Laboratory of Molecular Biology
Youtube Channel Link: https://www.youtube.com/@LMBCambridge
NGBS2022 Talk 10: RNA modelling and design - Rhiju Das
Youtube Title: Rhiju Das, Stanford University - Stanford Big Data 2015
Youtube Link: link
Youtube Channel Name: Stanford
Youtube Channel Link: https://www.youtube.com/@stanford
Rhiju Das, Stanford University - Stanford Big Data 2015
Youtube Title: Innovation Ecosystems: Rhiju Das on how to use video games to create better medicines
Youtube Link: link
Youtube Channel Name: STIP IdeaLab
Youtube Channel Link: https://www.youtube.com/@stipidealab7302
Innovation Ecosystems: Rhiju Das on how to use video games to create better medicines
Youtube Title: BioML Seminar - Rhiju Das on the RNA Folding Problem
Youtube Link: link
Youtube Channel Name: Machine Learning at Berkeley
Youtube Channel Link: https://www.youtube.com/@machinelearningatberkeley8868
BioML Seminar - Rhiju Das on the RNA Folding Problem
Youtube Title: SST02-IX: Crowdsourced design of stabilized COVID-19… - Rhiju Das - Special Sessions - ISMB 2020
Youtube Link: link
Youtube Channel Name: ISCB
Youtube Channel Link: https://www.youtube.com/@ISCBtv
SST02-IX: Crowdsourced design of stabilized COVID-19... - Rhiju Das - Special Sessions - ISMB 2020
Youtube Title: STANFORD GRADUATE FELLOWSHIPS PROGRAM IN SCIENCE AND ENGINEERING
Youtube Link: link
Youtube Channel Name: Giving To Stanford
Youtube Channel Link: https://www.youtube.com/@givingtostanford
STANFORD GRADUATE FELLOWSHIPS PROGRAM IN SCIENCE AND ENGINEERING
Youtube Title: Rhiju Das and Boris Rudolfs MTS Talk at UC Merced
Youtube Link: link
Youtube Channel Name: UCMerced CogSci
Youtube Channel Link: https://www.youtube.com/@ucmercedcogsci7075
Rhiju Das and Boris Rudolfs MTS Talk at UC Merced
Youtube Title: Rhiju Das on OpenVaccine Success
Youtube Link: link
Youtube Channel Name: Eterna
Youtube Channel Link: https://www.youtube.com/@eternagame
Rhiju Das on OpenVaccine Success
Youtube Title: Special Session 3: Atomic accuracy and blind predictions through… – Rhiju Das - ISMB/ECCB 2011
Youtube Link: link
Youtube Channel Name: ISCB
Youtube Channel Link: https://www.youtube.com/@ISCBtv
Special Session 3: Atomic accuracy and blind predictions through... – Rhiju Das - ISMB/ECCB 2011
Youtube Title: Welcome to Eternacon 8 - Rhiju Das
Youtube Link: link
Youtube Channel Name: Eterna
Youtube Channel Link: https://www.youtube.com/@eternagame
Welcome to Eternacon 8 - Rhiju Das
Youtube Title: CASP15 Rhiju Das
Youtube Link: link
Youtube Channel Name: CASP
Youtube Channel Link: https://www.youtube.com/@CASP-Prediction-Center
CASP15 Rhiju Das
Youtube Title: Stanford Seminar - EteRNE: RNA Nanoengineering through Crowd Science
Youtube Link: link
Youtube Channel Name: Stanford Online
Youtube Channel Link: https://www.youtube.com/@stanfordonline
Stanford Seminar - EteRNE: RNA Nanoengineering through Crowd Science
Youtube Title: Hannah Wayment Steele & Rhiju Das: CASP SIG on Modeling Conformational Ensembles
Youtube Link: link
Youtube Channel Name: CASP-SIG Conformational Ensembles of Protein
Youtube Channel Link: https://www.youtube.com/@CASPSIGConformationalEnsembles
Hannah Wayment Steele & Rhiju Das: CASP SIG on Modeling Conformational Ensembles
Youtube Title: EteRNA: A Videogame and a Massive Open Lab
Youtube Link: link
Youtube Channel Name: Stanford
Youtube Channel Link: https://www.youtube.com/@stanford
EteRNA: A Videogame and a Massive Open Lab
Youtube Title: StepWise MonteCarlo for modeling/design RNA & protein
Youtube Link: link
Youtube Channel Name: Rhiju Das
Youtube Channel Link: https://www.youtube.com/@rhijudas2048
StepWise MonteCarlo for modeling/design RNA & protein
Youtube Title: tetraloop GCAA
Youtube Link: link
Youtube Channel Name: Rhiju Das
Youtube Channel Link: https://www.youtube.com/@rhijudas2048
tetraloop GCAA