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 | 596 | 2020 |
Accurate de novo design of hyperstable constrained peptides G Bhardwaj, VK Mulligan, CD Bahl, JM Gilmore, PJ Harvey, O Cheneval, ... Nature 538 (7625), 329-335, 2016 | 393 | 2016 |
Geometric deep learning of RNA structure RJL Townshend, S Eismann, AM Watkins, R Rangan, M Karelina, R Das, ... Science 373 (6558), 1047-1051, 2021 | 257 | 2021 |
FARFAR2: improved de novo rosetta prediction of complex global RNA folds AM Watkins, R Rangan, R Das Structure 28 (8), 963-976. e6, 2020 | 180 | 2020 |
Accelerated cryo-EM-guided determination of three-dimensional RNA-only structures K Kappel, K Zhang, Z Su, AM Watkins, W Kladwang, S Li, G Pintilie, ... Nature methods 17 (7), 699-707, 2020 | 143 | 2020 |
Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics K Leppek, GW Byeon, W Kladwang, HK Wayment-Steele, CH Kerr, AF Xu, ... Nature communications 13 (1), 1536, 2022 | 139 | 2022 |
Anatomy of β-strands at protein–protein interfaces AM Watkins, PS Arora ACS chemical biology 9 (8), 1747-1754, 2014 | 125 | 2014 |
RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers Z Miao, RW Adamiak, M Antczak, MJ Boniecki, J Bujnicki, SJ Chen, ... Rna 26 (8), 982-995, 2020 | 114 | 2020 |
OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization G Ahdritz, N Bouatta, C Floristean, S Kadyan, Q Xia, W Gerecke, ... Nature Methods, 1-11, 2024 | 113 | 2024 |
Theoretical basis for stabilizing messenger RNA through secondary structure design HK Wayment-Steele, DS Kim, CA Choe, JJ Nicol, R Wellington-Oguri, ... Nucleic acids research 49 (18), 10604-10617, 2021 | 103 | 2021 |
Protein domain mimics as modulators of protein–protein interactions N Sawyer, AM Watkins, PS Arora Accounts of chemical research 50 (6), 1313-1322, 2017 | 93 | 2017 |
Expanding the limits of the second genetic code with ribozymes J Lee, KE Schwieter, AM Watkins, DS Kim, H Yu, KJ Schwarz, J Lim, ... Nature communications 10 (1), 5097, 2019 | 83 | 2019 |
Protein–protein interactions mediated by helical tertiary structure motifs AM Watkins, MG Wuo, PS Arora Journal of the American Chemical Society 137 (36), 11622-11630, 2015 | 83 | 2015 |
Adding diverse noncanonical backbones to rosetta: enabling peptidomimetic design K Drew, PD Renfrew, TW Craven, GL Butterfoss, FC Chou, S Lyskov, ... PLoS One 8 (7), e67051, 2013 | 74 | 2013 |
Structure-based inhibition of protein–protein interactions AM Watkins, PS Arora European journal of medicinal chemistry 94, 480-488, 2015 | 67 | 2015 |
De novo 3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures R Rangan, AM Watkins, J Chacon, R Kretsch, W Kladwang, IN Zheludev, ... Nucleic acids research 49 (6), 3092-3108, 2021 | 63 | 2021 |
Designing peptides on a quantum computer VK Mulligan, H Melo, HI Merritt, S Slocum, BD Weitzner, AM Watkins, ... BioRxiv, 752485, 2019 | 52 | 2019 |
Blind prediction of noncanonical RNA structure at atomic accuracy AM Watkins, C Geniesse, W Kladwang, P Zakrevsky, L Jaeger, R Das Science Advances 4 (5), eaar5316, 2018 | 47 | 2018 |
Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1 VK Mulligan, S Workman, T Sun, S Rettie, X Li, LJ Worrall, TW Craven, ... Proceedings of the National Academy of Sciences 118 (12), e2012800118, 2021 | 46 | 2021 |
HippDB: a database of readily targeted helical protein–protein interactions CM Bergey, AM Watkins, PS Arora Bioinformatics 29 (21), 2806-2807, 2013 | 44 | 2013 |