| ColabFold: making protein folding accessible to all M Mirdita, K Schütze, Y Moriwaki, L Heo, S Ovchinnikov, M Steinegger Nature methods 19 (6), 679-682, 2022 | 8996 | 2022 |
| Accurate prediction of protein structures and interactions using a three-track neural network M Baek, F DiMaio, I Anishchenko, J Dauparas, S Ovchinnikov, GR Lee, ... Science 373 (6557), 871-876, 2021 | 5930 | 2021 |
| De novo design of protein structure and function with RFdiffusion JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, ... Nature 620 (7976), 1089-1100, 2023 | 2193 | 2023 |
| Improved protein structure prediction using predicted interresidue orientations J Yang, I Anishchenko, H Park, Z Peng, S Ovchinnikov, D Baker Proceedings of the National Academy of Sciences 117 (3), 1496-1503, 2020 | 1612 | 2020 |
| 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 | 936 | 2020 |
| Assessing the utility of coevolution-based residue–residue contact predictions in a sequence-and structure-rich era H Kamisetty, S Ovchinnikov, D Baker Proceedings of the National Academy of Sciences 110 (39), 15674-15679, 2013 | 797 | 2013 |
| Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information S Ovchinnikov, H Kamisetty, D Baker elife 3, e02030, 2014 | 758 | 2014 |
| De novo protein design by deep network hallucination I Anishchenko, SJ Pellock, TM Chidyausiku, TA Ramelot, S Ovchinnikov, ... Nature 600 (7889), 547-552, 2021 | 696 | 2021 |
| A structural biology community assessment of AlphaFold2 applications M Akdel, DEV Pires, EP Pardo, J Jänes, AO Zalevsky, B Mészáros, ... Nature Structural & Molecular Biology 29 (11), 1056-1067, 2022 | 664 | 2022 |
| Protein structure determination using metagenome sequence data S Ovchinnikov, H Park, N Varghese, PS Huang, GA Pavlopoulos, DE Kim, ... Science 355 (6322), 294-298, 2017 | 587 | 2017 |
| Computed structures of core eukaryotic protein complexes IR Humphreys, J Pei, M Baek, A Krishnakumar, I Anishchenko, ... Science 374 (6573), eabm4805, 2021 | 547 | 2021 |
| Scaffolding protein functional sites using deep learning J Wang, S Lisanza, D Juergens, D Tischer, JL Watson, KM Castro, ... Science 377 (6604), 387-394, 2022 | 542 | 2022 |
| Predicting multiple conformations via sequence clustering and AlphaFold2 HK Wayment-Steele, A Ojoawo, R Otten, JM Apitz, W Pitsawong, ... Nature 625 (7996), 832-839, 2024 | 528* | 2024 |
| Transformer protein language models are unsupervised structure learners R Rao, J Meier, T Sercu, S Ovchinnikov, A Rives Biorxiv, 2020.12. 15.422761, 2020 | 449 | 2020 |
| De novo design of a fluorescence-activating β-barrel J Dou, AA Vorobieva, W Sheffler, LA Doyle, H Park, MJ Bick, B Mao, ... Nature 561 (7724), 485-491, 2018 | 428 | 2018 |
| Mega-scale experimental analysis of protein folding stability in biology and design K Tsuboyama, J Dauparas, J Chen, E Laine, Y Mohseni Behbahani, ... Nature 620 (7973), 434-444, 2023 | 333 | 2023 |
| Large-scale determination of previously unsolved protein structures using evolutionary information S Ovchinnikov, L Kinch, H Park, Y Liao, J Pei, DE Kim, H Kamisetty, ... elife 4, e09248, 2015 | 286 | 2015 |
| Protein interaction networks revealed by proteome coevolution Q Cong, I Anishchenko, S Ovchinnikov, D Baker Science 365 (6449), 185-189, 2019 | 285 | 2019 |
| Architectures of lipid transport systems for the bacterial outer membrane DC Ekiert, G Bhabha, GL Isom, G Greenan, S Ovchinnikov, IR Henderson, ... Cell 169 (2), 273-285. e17, 2017 | 271 | 2017 |
| State-of-the-art estimation of protein model accuracy using AlphaFold JP Roney, S Ovchinnikov Physical Review Letters 129 (23), 238101, 2022 | 268 | 2022 |
David BakerUniversity of WashingtonVerified email at uw.edu
