Justas Dauparas
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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
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
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
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
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
Hallucinating symmetric protein assemblies
BIM Wicky, LF Milles, A Courbet, RJ Ragotte, J Dauparas, E Kinfu, S Tipps, ...
Science 378 (6615), 56-61, 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
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
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
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
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
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
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
Improving protein expression, stability, and function with ProteinMPNN
KH Sumida, R Nez-Franco, I Kalvet, SJ Pellock, BIM Wicky, LF Milles, ...
Journal of the American Chemical Society 146 (3), 2054-2061, 2024
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
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
Design of stimulus-responsive two-state hinge proteins
F Praetorius, PJY Leung, MH Tessmer, A Broerman, C Demakis, ...
Science 381 (6659), 754-760, 2023
Iterative se (3)-transformers
FB Fuchs, E Wagstaff, J Dauparas, I Posner
Geometric Science of Information: 5th International Conference, GSI 2021…, 2021
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
Flagellar flows around bacterial swarms
J Dauparas, E Lauga
Physical Review Fluids 1 (4), 043202, 2016
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Articles 1–20