Author

D. Juergens

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biochemistry· 3 Biophysics· 2 Bioengineering· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 5 Research Focus:protein design· 4 Assay:protein structure· 4 Assay:protein sequence· 2 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Organism:human· 1 Research Focus:protein folding & dynamics· 1

Frequent Co-Authors

D. Baker· 6 shared I. Anishchenko· 3 shared J. Dauparas· 3 shared J. L. Watson· 3 shared P. M. Levine· 3 shared X. Li· 3 shared A. K. Bera· 2 shared A. Kang· 2 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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6 shared papersBiochemistry

D. Baker

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Author profile →

6 papers

Recent·Most cited

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles

L. An, M. Said, L. Tran, S. Majumder, I. Goreshnik, G. R. Lee, D. Juergens, J. Dauparas, I. Anishchenko, B. Coventry, A. K. Bera, A. Kang, P. M. Levine, V. Alvarez, A. Pillai, , D. Baker

The paper designs nanomolar binders for diverse small molecules using a pseudocycle-based shape complementarity method, enabling direct conversion into sensing systems.

bioRxiv · Dec 20, 2023 · PreprintBiochemistry

Design of stimulus-responsive two-state hinge proteins

F. Praetorius, P. J. Y. Leung, M. H. Tessmer, A. Broerman, C. Demakis, A. F. Dishman, A. Pillai, A. Idris, D. Juergens, J. Dauparas, X. Li, P. M. Levine, M. Lamb, R. K. Ballard, S. R. Gerben, , D. Baker

This paper designs two-state hinge proteins that switch between structural states in response to ligands, demonstrating atomic-level accuracy and tunable kinetics and thermodynamics.

bioRxiv · Jan 27, 2023Biophysics

De novo design of high-affinity protein binders to bioactive helical peptides

S. Vazquez Torres, P. J. Y. Leung, I. D. Lutz, P. Venkatesh, J. L. Watson, F. Hink, H.-H. Huynh, A. H.-W. Yeh, D. Juergens, N. R. Bennett, A. N. Hoofnagle, E. Huang, M. J. MacCoss, M. Exposit, G. R. Lee, , D. Baker

This paper designs high-affinity protein binders for bioactive helical peptides using deep learning and generative models, achieving picomolar affinity without experimental optimization.

bioRxiv · Dec 10, 2022 · PreprintBioengineering

Accurate Mutation Effect Prediction using RoseTTAFold

S. Mansoor, M. Baek, D. Juergens, J. L. Watson, D. Baker

This study evaluates RoseTTAFold's ability to predict mutation effects without additional training, achieving state-of-the-art accuracy across diverse protein families.

bioRxiv · Nov 4, 2022 · PreprintBiochemistry

Deep learning methods for designing proteins scaffolding functional sites

J. Wang, S. Lisanza, D. Juergens, D. Tischer, I. Anishchenko, M. Baek, J. L. Watson, J.-H. Chun, L. F. Milles, J. Dauparas, M. Exposit, W. Yang, A. Saragovi, S. Ovchinnikov, D. Baker

This paper introduces two deep learning methods for designing proteins with specific functional sites, showing they can generate diverse proteins with desired properties.

bioRxiv · Nov 10, 2021Biochemistry

Protein sequence design by explicit energy landscape optimization

C. Norn, B. I. M. Wicky, D. Juergens, S. Liu, D. Kim, B. Koepnick, I. Anishchenko, Foldit Players, D. Baker, S. Ovchinnikov

This paper designs protein sequences by optimizing an energy landscape to ensure they fold into a desired structure, showing improved performance over traditional methods.

bioRxiv · Jul 23, 2020 · PreprintBiophysics