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Title: 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 » of new possibilities for biomolecular engineering.« less

Authors:
 [1];  [1];  [1];  [2];  [3];  [4];  [5];  [6];  [7]
  1. Univ. of Washington, Seattle, WA (United States). Dept. of Biochemistry. Inst. for Protein Design
  2. Univ. of California, San Francisco, CA (United States). Dept. of Cellular and Molecular Pharmacology
  3. Univ. of California, San Francisco, CA (United States). Dept. of Microbiology and Immunology
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Molecular Biophysics and Integrated Bioimaging
  5. 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
  6. 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
  7. 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 Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); 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:
Journal Article: 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. 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., and Baker, David. Wed . "Exploring the repeat protein universe through computational protein design". United States. doi: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}
}

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