| 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 | 3959 | 2021 |
| Robust deep learning–based protein sequence design using ProteinMPNN J Dauparas, I Anishchenko, N Bennett, H Bai, RJ Ragotte, LF Milles, ... Science 378 (6615), 49-56, 2022 | 760 | 2022 |
| 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 | 295 | 2022 |
| Improved protein structure refinement guided by deep learning based accuracy estimation N Hiranuma, H Park, M Baek, I Anishchenko, J Dauparas, D Baker Nature communications 12 (1), 1340, 2021 | 227 | 2021 |
| De novo design of luciferases using deep learning AHW Yeh, C Norn, Y Kipnis, D Tischer, SJ Pellock, D Evans, P Ma, ... Nature 614 (7949), 774-780, 2023 | 222 | 2023 |
| Hallucinating symmetric protein assemblies BIM Wicky, LF Milles, A Courbet, RJ Ragotte, J Dauparas, E Kinfu, S Tipps, ... Science 378 (6615), 56-61, 2022 | 148 | 2022 |
| Improving de novo protein binder design with deep learning NR Bennett, B Coventry, I Goreshnik, B Huang, A Allen, D Vafeados, ... Nature Communications 14 (1), 2625, 2023 | 134 | 2023 |
| 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 | 129 | 2023 |
| Language models generalize beyond natural proteins R Verkuil, O Kabeli, Y Du, BIM Wicky, LF Milles, J Dauparas, D Baker, ... BioRxiv, 2022.12. 21.521521, 2022 | 70 | 2022 |
| Peptide-binding specificity prediction using fine-tuned protein structure prediction networks A Motmaen, J Dauparas, M Baek, MH Abedi, D Baker, P Bradley Proceedings of the National Academy of Sciences 120 (9), e2216697120, 2023 | 56 | 2023 |
| Improving protein expression, stability, and function with ProteinMPNN KH Sumida, R Núñez-Franco, I Kalvet, SJ Pellock, BIM Wicky, LF Milles, ... Journal of the American Chemical Society 146 (3), 2054-2061, 2024 | 52 | 2024 |
| Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14 I Anishchenko, M Baek, H Park, N Hiranuma, DE Kim, J Dauparas, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1722-1733, 2021 | 46 | 2021 |
| Self-organization of swimmers drives long-range fluid transport in bacterial colonies H Xu, J Dauparas, D Das, E Lauga, Y Wu Nature communications 10 (1), 1792, 2019 | 44 | 2019 |
| Deep learning methods for designing proteins scaffolding functional sites J Wang, S Lisanza, D Juergens, D Tischer, I Anishchenko, M Baek, ... BioRxiv, 2021.11. 10.468128, 2021 | 40 | 2021 |
| End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman S Petti, N Bhattacharya, R Rao, J Dauparas, N Thomas, J Zhou, AM Rush, ... Bioinformatics 39 (1), btac724, 2023 | 32 | 2023 |
| Single Layers of Attention Suffice to Predict Protein Contacts N Bhattacharya, N Thomas, R Rao, J Dauparas, P Koo, D Baker, YS Song, ... bioRxiv, 2020 | 30 | 2020 |
| Design of stimulus-responsive two-state hinge proteins F Praetorius, PJY Leung, MH Tessmer, A Broerman, C Demakis, ... Science 381 (6659), 754-760, 2023 | 25 | 2023 |
| Atomic context-conditioned protein sequence design using LigandMPNN J Dauparas, GR Lee, R Pecoraro, L An, I Anishchenko, C Glasscock, ... Biorxiv, 2023.12. 22.573103, 2023 | 25 | 2023 |
| Iterative se (3)-transformers FB Fuchs, E Wagstaff, J Dauparas, I Posner Geometric Science of Information: 5th International Conference, GSI 2021 …, 2021 | 23 | 2021 |
| Flagellar flows around bacterial swarms J Dauparas, E Lauga Physical Review Fluids 1 (4), 043202, 2016 | 14 | 2016 |
