Exploring the repeat protein universe through computational protein design
Abstract
A central question in protein evolution is the extent to which naturally occurring proteins sample the space of folded structures accessible to the polypeptide chain. Repeat proteins composed of multiple tandem copies of a modular structure unit are widespread in nature and have critical roles in molecular recognition, signalling, and other essential biological processes. Naturally occurring repeat proteins have been re-engineered for molecular recognition and modular scaffolding applications. In this paper, we use computational protein design to investigate the space of folded structures that can be generated by tandem repeating a simple helix–loop–helix–loop structural motif. Eighty-three designs with sequences unrelated to known repeat proteins were experimentally characterized. Of these, 53 are monomeric and stable at 95 °C, and 43 have solution X-ray scattering spectra consistent with the design models. Crystal structures of 15 designs spanning a broad range of curvatures are in close agreement with the design models with root mean square deviations ranging from 0.7 to 2.5 Å. Finally, our results show that existing repeat proteins occupy only a small fraction of the possible repeat protein sequence and structure space and that it is possible to design novel repeat proteins with precisely specified geometries, opening up a wide arraymore »
- Authors:
-
- Univ. of Washington, Seattle, WA (United States). Dept. of Biochemistry. Inst. for Protein Design
- Univ. of California, San Francisco, CA (United States). Dept. of Cellular and Molecular Pharmacology
- Univ. of California, San Francisco, CA (United States). Dept. of Microbiology and Immunology
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Molecular Biophysics and Integrated Bioimaging
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Molecular Biophysics and Integrated Bioimaging; Univ. of California, Santa Cruz, CA (United States). Dept. of Chemistry and Biochemistry
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Molecular Biophysics and Integrated Bioimaging; Univ. of Texas M. D. Anderson Cancer Center, Houston, TX (United States). Dept. of Molecular and Cellular Oncology
- Univ. of Washington, Seattle, WA (United States). Dept. of Biochemistry. Inst. for Protein Design. Howard Hughes Medical Inst.
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); Defense Threat Reduction Agency (DTRA) (United States); US Air Force Office of Scientific Research (AFOSR); Howard Hughes Medical Inst. (HHMI) (United States); National Inst. of Health (NIH) (United States); Damon Runyon Cancer Research Foundation (United States); Swiss National Science Foundation (SNSF); Human Frontier Science Program (HFSP) (France)
- Contributing Org.:
- Univ. of California, Santa Cruz, CA (United States); Univ. of Texas M. D. Anderson Cancer Center, Houston, TX (United States)
- OSTI Identifier:
- 1378707
- Grant/Contract Number:
- AC02-05CH11231; MCB-1445201; CHE-1332907; FA950-12-10112; HHMI-027779; GM105404; K99GM112982; DRG-2140-12; DRG-2136-12; PBZHP3-125470; LT000070/2009-L
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature (London)
- Additional Journal Information:
- Journal Name: Nature (London); Journal Volume: 528; Journal Issue: 7583; Journal ID: ISSN 0028-0836
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; protein design; protein structure predictions
Citation Formats
Brunette, TJ, Parmeggiani, Fabio, Huang, Po-Ssu, Bhabha, Gira, Ekiert, Damian C., Tsutakawa, Susan E., Hura, Greg L., Tainer, John A., and Baker, David. Exploring the repeat protein universe through computational protein design. United States: N. p., 2015.
Web. doi:10.1038/nature16162.
Brunette, TJ, Parmeggiani, Fabio, Huang, Po-Ssu, Bhabha, Gira, Ekiert, Damian C., Tsutakawa, Susan E., Hura, Greg L., Tainer, John A., & Baker, David. Exploring the repeat protein universe through computational protein design. United States. https://doi.org/10.1038/nature16162
Brunette, TJ, Parmeggiani, Fabio, Huang, Po-Ssu, Bhabha, Gira, Ekiert, Damian C., Tsutakawa, Susan E., Hura, Greg L., Tainer, John A., and Baker, David. Wed .
"Exploring the repeat protein universe through computational protein design". United States. https://doi.org/10.1038/nature16162. https://www.osti.gov/servlets/purl/1378707.
@article{osti_1378707,
title = {Exploring the repeat protein universe through computational protein design},
author = {Brunette, TJ and Parmeggiani, Fabio and Huang, Po-Ssu and Bhabha, Gira and Ekiert, Damian C. and Tsutakawa, Susan E. and Hura, Greg L. and Tainer, John A. and Baker, David},
abstractNote = {A central question in protein evolution is the extent to which naturally occurring proteins sample the space of folded structures accessible to the polypeptide chain. Repeat proteins composed of multiple tandem copies of a modular structure unit are widespread in nature and have critical roles in molecular recognition, signalling, and other essential biological processes. Naturally occurring repeat proteins have been re-engineered for molecular recognition and modular scaffolding applications. In this paper, we use computational protein design to investigate the space of folded structures that can be generated by tandem repeating a simple helix–loop–helix–loop structural motif. Eighty-three designs with sequences unrelated to known repeat proteins were experimentally characterized. Of these, 53 are monomeric and stable at 95 °C, and 43 have solution X-ray scattering spectra consistent with the design models. Crystal structures of 15 designs spanning a broad range of curvatures are in close agreement with the design models with root mean square deviations ranging from 0.7 to 2.5 Å. Finally, our results show that existing repeat proteins occupy only a small fraction of the possible repeat protein sequence and structure space and that it is possible to design novel repeat proteins with precisely specified geometries, opening up a wide array of new possibilities for biomolecular engineering.},
doi = {10.1038/nature16162},
journal = {Nature (London)},
number = 7583,
volume = 528,
place = {United States},
year = {Wed Dec 16 00:00:00 EST 2015},
month = {Wed Dec 16 00:00:00 EST 2015}
}
Web of Science
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- Bioinformatics, Vol. 36, Issue 6
RepeatsDB 2.0: improved annotation, classification, search and visualization of repeat protein structures
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The topology of evolutionary novelty and innovation in macroevolution
journal, October 2017
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Design: An assay based on single-polypeptide-chain heterodimeric A2AR/D2R and non-oligomerized fusions for in vivo analysis of their allosteric receptor-receptor interactions
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The solvent-excluded surfaces of water-soluble proteins
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Sibe: a computation tool to apply protein sequence statistics to folding and design
journal, April 2019
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Pathway dissection, regulation, engineering and application: lessons learned from biobutanol production by solventogenic clostridia
journal, March 2020
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Inferring repeat-protein energetics from evolutionary information
journal, June 2017
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Long-range correlation in protein dynamics: Confirmation by structural data and normal mode analysis
journal, February 2020
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Consistency Principle for Protein Design
journal, January 2020
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The Folding of de Novo Designed Protein DS119 via Molecular Dynamics Simulations
journal, April 2016
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