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Hallucination of closed repeat proteins containing central pockets
Open accessPublished version of a preprint
Abstract
In pseudocyclic proteins, such as TIM barrels, β barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity that can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep-learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in Escherichia coli are consistent with the design models, and the three crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small-molecule binders and enzymes.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
Self-citationRobust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,210
Self-citationRobust deep learning based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107
Self-citationDe novo protein design by deep network hallucination
Anishchenko, I., Chidyausiku, T. M., Ovchinnikov, S., Pellock, S. J., & Baker, D. (2020) · doi:10.1101/2020.07.22.211482 · Cited by 629
Self-citationHigh thermodynamic stability of parametrically designed helical bundles
Huang, P.-S., Oberdorfer, G., Xu, C., Pei, X. Y., Nannenga, B. L., Rogers, J. M., DiMaio, F., Gonen, T., Luisi, B., & Baker, D. (2014) · Science · doi:10.1126/science.1257481 · Cited by 277
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Research works printed but not linked: 30
- Nagano, N., Orengo, C. A. & Thornton, J. M. One fold with many functions: the evolutionary relationships between TIM barrel families based on their sequences, structures and functions. J. Mol. Biol. 321, 741–765 (2002). · DOI
- Park, K. et al. Control of repeat-protein curvature by computational protein design. Nat. Struct. Mol. Biol. 22, 167–174 (2015). · DOI
- Brunette, T. et al. Exploring the repeat protein universe through computational protein design. Nature 528, 580–584 (2015). · DOI
- Huang, P.-S. et al. De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy. Nat. Chem. Biol. 12, 29–34 (2016). · DOI
- Doyle, L. et al. Rational design of α-helical tandem repeat proteins with closed architectures. Nature 528, 585–588 (2015). · DOI
- Wicky, B. I. M. et al. Hallucinating symmetric protein assemblies. Science 378, 56–61 (2022). · DOI · Paper page
- Hura, G. L. et al. Robust, high-throughput solution structural analyses by small angle X-ray scattering (SAXS). Nat. Methods 6, 606–612 (2009). · DOI
- Dou, J. et al. De novo design of a fluorescence-activating β-barrel. Nature 561, 485–491 (2018). · DOI · Paper page
- Cao, L. et al. Design of protein-binding proteins from the target structure alone. Nature 605, 551–560 (2022). · DOI · Paper page
- Su, M. et al. Comparative assessment of scoring functions: the CASF-2016 update. J. Chem. Inf. Model. 59, 895–913 (2019). · DOI
- Basanta, B. et al. An enumerative algorithm for de novo design of proteins with diverse pocket structures. Proc. Natl Acad. Sci. USA 117, 22135–22145 (2020). · DOI
- Lipsh‐Sokolik, R., Listov, D. & Fleishman, S. J. The AbDesign computational pipeline for modular backbone assembly and design of binders and enzymes. Protein Sci. 30, 151–159 (2021). · DOI
- Polizzi, N. F. & DeGrado, W. F. A defined structural unit enables de novo design of small-molecule–binding proteins. Science 369, 1227–1233 (2020). · DOI
- Voynov, V., Chennamsetty, N., Kayser, V., Helk, B. & Trout, B. L. Predictive tools for stabilization of therapeutic proteins. mAbs 1, 580–582 (2009). · DOI
- Chaudhury, S., Lyskov, S. & Gray, J. J. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinforma. Oxf. Engl. 26, 689–691 (2010). · DOI
- Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017). · DOI · Paper page
- Dong, R., Pan, S., Peng, Z., Zhang, Y. & Yang, J. mTM-align: a server for fast protein structure database search and multiple protein structure alignment. Nucleic Acids Res. https://doi.org/10.1093/nar/gky430 (2018). · DOI
- Groom, C. R., Bruno, I. J., Lightfoot, M. P. & Ward, S. C. The Cambridge Structural Database. Acta Crystallogr. B Struct. Sci. Cryst. Eng. Mater. 72, 171–179 (2016). · DOI · Paper page
- O’Boyle, N. M. et al. Open Babel: an open chemical toolbox. J. Cheminformatics 3, 33 (2011). · DOI
- Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996). · DOI · Paper page
- Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021). · DOI
- Sharp, P. M. & Li, W.-H. The codon adaptation index—a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 15, 1281–1295 (1987). · DOI · Paper page
- Dang, B. et al. SNAC-tag for sequence-specific chemical protein cleavage. Nat. Methods 16, 319–322 (2019). · DOI
- Kabsch, W. XDS. Acta Crystallogr. D Biol. Crystallogr. 66, 125–132 (2010). · DOI · Paper page
- Minor, W., Cymborowski, M., Otwinowski, Z. & Chruszcz, M. HKL-3000: the integration of data reduction and structure solution — from diffraction images to an initial model in minutes. Acta Crystallogr. D Biol. Crystallogr. 62, 859–866 (2006). · DOI
- Winn, M. D. et al. Overview of the CCP 4 suite and current developments. Acta Crystallogr. D Biol. Crystallogr. 67, 235–242 (2011). · DOI · Paper page
- McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007). · DOI · Paper page
- Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D. Biol. Crystallogr. 66, 213–221 (2010). · DOI · Paper page
- Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004). · DOI · Paper page
- Williams, C. J. et al. MolProbity: More and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018). · DOI · Paper page
Metadata
- Authors
- An, L., Hicks, D. R., Zorine, D., Dauparas, J., Wicky, B. I. M., Milles, L. F., Courbet, A., Bera, A. K., Nguyen, H., Kang, A., Carter, L., & Baker, D.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- An, Linna
- ORCID
- Venue
- Nature Structural & Molecular Biology
- Published
- 2023-09-28
- Volume
- 30
- Issue
- 11
- Pages
- 1755-1760
- ISSN
- 1545-9985 (ISSN-L)
- 1545-9993
- PMC
- PMC10643118
- OpenAlex
- W4387130982
- Cited by
- 15
- References
- 6
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 4 · 2023: 3
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Structural & Molecular Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, cc-by)
- Funders
- U.S. Department of Defense (DE-AC02-06CH11357, DE-AC02-05CH11231)
- Howard Hughes Medical Institute (DE-AC02-05CH11231)
- U.S. Department of Energy (DE‐ AC02‐06CH11357, -AC02-05CH11231, Contract No. DE-AC02-05-CH11231, S10OD018483, AC02‐06CH11357, 05CH11231, P30 GM124169, No. DE-AC02-05-CH11231, P30 GM124165, DE AC02 05CH11231, 06CH11357, GM124169, DE-AC02-)
- Washington Research Foundation (DE-AC02-06CH11357)
- Open Philanthropy Project
- National Energy Research Scientific Computing Center (DE–AC02–05CH11231, 05CH11231, AC02-05CH11231, Contract No. DE-AC02-05CH11231)
- National Institutes of Health (1s10od018483-01, DE-AC02–05CH11231, DE-AC02–06CH11357, GM124165, GM124169)
- Office of Science (P30 GM124165, No. DE-AC02- 05-CH11231, P30 GM124169, AC02‐06CH11357, DEAC02–05CH11231, AC02-05CH11231, DE-AC02, 05CH11231, 06CH11357, Contract No. DE-AC02-05CH11231, DE-AC02-06CH11357)
- National Institute on Aging (U19AG065156)
- National Institute of General Medical Sciences (DE-AC02-05CH11231, DE-AC02-06CH11357, GM124169, GM124165, S10OD018483, P30GM124169, P30GM124165)
- Basic Energy Sciences (S10OD018483, DE-AC02, 05CH11231, DE-AC02-05CH11231., DE-AC- 02-06CH11357, AC02‐06CH11357, AC02–05CH11231, 06CH11357)
- Biological and Environmental Research (05CH11231, DE-AC02 − 05CH11231, P30 GM124169, DEAC0206CH11357, S10OD018483, P30 GM124165, AC02-05CH11231)
- Argonne National Laboratory (DE- AC02-06-CH11357, DE-AC02, AC02–06CH11357, DE-AC02-05CH11231, 06CH11357, P30 GM124165)
- Lawrence Berkeley National Laboratory (AC02‐05CH11231, DE-AC02-05CH11231, S10OD018483, DE-AC02-06CH11357, 05CH11231)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Structural Characterization
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein design, deep learning, TIM barrel, beta-barrel, central cavity, binding pocket, small-molecule binders, enzyme design, Escherichia coli, protein folding
- MeSH terms
- Hallucinations, Humans, Proteins
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