De novo design of a non-local β-sheet protein with high stability and accuracy
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem
Grantová podpora
Howard Hughes Medical Institute - United States
R35 GM125034
NIGMS NIH HHS - United States
S10 OD018455
NIH HHS - United States
PubMed
30374087
PubMed Central
PMC6219906
DOI
10.1038/s41594-018-0141-6
PII: 10.1038/s41594-018-0141-6
Knihovny.cz E-zdroje
- MeSH
- konformace proteinů, beta-řetězec MeSH
- konformace proteinů MeSH
- molekulární modely MeSH
- nukleární magnetická rezonance biomolekulární MeSH
- počítačová simulace MeSH
- proteinové inženýrství metody MeSH
- proteiny chemie genetika MeSH
- sbalování proteinů MeSH
- sekvence aminokyselin MeSH
- stabilita proteinů MeSH
- vodíková vazba MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Názvy látek
- proteiny MeSH
β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein design. Despite this potential, de novo design of all-β-sheet proteins from first principles lags far behind the design of all-α or mixed-αβ domains owing to their non-local nature and the tendency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have identified a series of structural relationships between loop geometry, side chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to de novo design jellyroll structures with double-stranded β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable design closely matched the computational model, demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the design of a broad range of non-local β-sheet protein structures.
CEITEC Central European Institute of Technology Masaryk University Brno Czech Republic
Cyrus Biotechnology Seattle WA USA
Department of Biochemistry University of Washington Seattle WA USA
Department of Chemistry and Biochemistry University of California Santa Cruz Santa Cruz CA USA
Department of Computer Science University of California Santa Cruz Santa Cruz CA USA
Institute for Protein Design University of Washington Seattle WA USA
Institute for Research in Biomedicine Barcelona Institute of Science and Technology Barcelona Spain
Institute of Biochemistry Graz University of Technology Graz Austria
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Kortemme T, Ramírez-Alvarado M & Serrano L Design of a 20-amino acid, three-stranded beta-sheet protein. Science 281, 253–256 (1998). PubMed
Searle MS & Ciani B Design of beta-sheet systems for understanding the thermodynamics and kinetics of protein folding. Curr. Opin. Struct. Biol. 14, 458–464 (2004). PubMed
Hughes RM & Waters ML Model systems for beta-hairpins and beta-sheets. Curr. Opin. Struct. Biol. 16, 514–524 (2006). PubMed
Marcos E & Adriano-Silva D Essentials of de novo protein design: Methods and applications. WIREs Comput Mol Sci e1374 (2018).
Koga N et al. Principles for designing ideal protein structures. Nature 491, 222–227 (2012). PubMed PMC
Hecht MH De novo design of beta-sheet proteins. Proceedings of the National Academy of Sciences 91, 8729–8730 (1994). PubMed PMC
Plaxco KW, Simons KT & Baker D Contact order, transition state placement and the refolding rates of single domain proteins. J. Mol. Biol. 277, 985–994 (1998). PubMed
Quinn TP, Tweedy NB, Williams RW, Richardson JS & Richardson DC Betadoublet: de novo design, synthesis, and characterization of a beta-sandwich protein. Proc. Natl. Acad. Sci. U. S. A. 91, 8747–8751 (1994). PubMed PMC
Nanda V et al. De novo design of a redox-active minimal rubredoxin mimic. J. Am. Chem. Soc. 127, 5804–5805 (2005). PubMed
Dou J et al. De novo design of a fluorescence-activating β-barrel. Nature (2018). doi:10.1038/s41586-018-0509-0 PubMed DOI PMC
Voet ARD et al. Computational design of a self-assembling symmetrical β-propeller protein. Proceedings of the National Academy of Sciences 111, 15102–15107 (2014). PubMed PMC
MacDonald JT et al. Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension. Proceedings of the National Academy of Sciences 113, 10346–10351 (2016). PubMed PMC
Ottesen JJ & Imperiali B. Design of a discretely folded mini-protein motif with predominantly beta-structure. Nat. Struct. Biol. 8, 535–539 (2001). PubMed
Hu X, Wang H, Ke H & Kuhlman B Computer-based redesign of a beta sandwich protein suggests that extensive negative design is not required for de novo beta sheet design. Structure 16, 1799–1805 (2008). PubMed PMC
Hennetin J, Jullian B, Steven AC & Kajava AV Standard conformations of beta-arches in beta-solenoid proteins. J. Mol. Biol. 358, 1094–1105 (2006). PubMed
Lin Y-R et al. Control over overall shape and size in de novo designed proteins. Proc. Natl. Acad. Sci. U. S. A. 112, E5478–85 (2015). PubMed PMC
Kajava AV, Baxa U & Steven AC β arcades: recurring motifs in naturally occurring and disease-related amyloid fibrils. The FASEB Journal 24, 1311–1319 (2010). PubMed PMC
Kuhlman B & Baker D Native protein sequences are close to optimal for their structures. Proc. Natl. Acad. Sci. U. S. A. 97, 10383–10388 (2000). PubMed PMC
Kuhlman B et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 (2003). PubMed
Richardson JS & Richardson DC Natural -sheet proteins use negative design to avoid edge-to-edge aggregation. Proceedings of the National Academy of Sciences 99, 2754–2759 (2002). PubMed PMC
Marcos E et al. Principles for designing proteins with cavities formed by curved β sheets. Science 355, 201–206 (2017). PubMed PMC
Rohl CA, Strauss CEM, Misura KMS & Baker D Protein structure prediction using Rosetta. Methods Enzymol. 383, 66–93 (2004). PubMed
Bradley P Toward High-Resolution de Novo Structure Prediction for Small Proteins. Science 309, 1868–1871 (2005). PubMed
Kuhn M, Meiler J & Baker D Strand-loop-strand motifs: prediction of hairpins and diverging turns in proteins. Proteins 54, 282–288 (2004). PubMed
Bradley P & Baker D Improved beta-protein structure prediction by multilevel optimization of nonlocal strand pairings and local backbone conformation. Proteins: Struct. Funct. Bioinf. 65, 922–929 (2006). PubMed
Altschul SF et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997). PubMed PMC
Camacho C et al. BLAST : architecture and applications. BMC Bioinformatics 10, 421 (2009). PubMed PMC
Evangelidis T et al. Automated NMR resonance assignments and structure determination using a minimal set of 4D spectra. Nat. Commun. 9, 384 (2018). PubMed PMC
Holm L. & Laakso LM Dali server update. Nucleic Acids Res. 44, W351–5 (2016). PubMed PMC
Zimmermann L et al. A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core. J. Mol. Biol. 430, 2237–2243 (2018). PubMed
Clark P. Protein folding in the cell: reshaping the folding funnel. Trends Biochem. Sci. 29, 527–534 (2004). PubMed
Wang G. & Dunbrack RL Jr. PISCES: a protein sequence culling server. Bioinformatics 19, 1589–1591 (2003). PubMed
Kabsch W. & Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577–2637 (1983). PubMed
Fleishman SJ et al. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS One 6, e20161 (2011). PubMed PMC
O’Meara MJ et al. Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta. J. Chem. Theory Comput. 11, 609–622 (2015). PubMed PMC
Bhardwaj G. et al. Accurate de novo design of hyperstable constrained peptides. Nature 538, 329–335 (2016). PubMed PMC
Sheffler W. & Baker D. RosettaHoles2: a volumetric packing measure for protein structure refinement and validation. Protein Sci. 19, 1991–1995 (2010). PubMed PMC
Jones DT Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999). PubMed
Alford RF et al. The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. J. Chem. Theory Comput. 13, 3031–3048 (2017). PubMed PMC
Studier FW Protein production by auto-induction in high density shaking cultures. Protein Expr. Purif. 41, 207–234 (2005). PubMed
Delaglio F et al. NMRPipe: a multidimensional spectral processing system based on UNIX pipes. J. Biomol. NMR 6, 277–293 (1995). PubMed
Ying J, Delaglio F, Torchia DA & Bax A Sparse multidimensional iterative lineshape-enhanced (SMILE) reconstruction of both non-uniformly sampled and conventional NMR data. J. Biomol. NMR 68, 101–118 (2017). PubMed PMC
Lee W, Tonelli M & Markley JL NMRFAM-SPARKY: enhanced software for biomolecular NMR spectroscopy. Bioinformatics 31, 1325–1327 (2015). PubMed PMC
Nerli S, McShan AC & Sgourakis NG Chemical shift-based methods in NMR structure determination. Prog. Nucl. Magn. Reson. Spectrosc. 106-107, 1–25 (2018). PubMed PMC
Lange OF Automatic NOESY assignment in CS-RASREC-Rosetta. J. Biomol. NMR 59, 147–159 (2014). PubMed
Lange OF & Baker D Resolution-adapted recombination of structural features significantly improves sampling in restraint-guided structure calculation. Proteins 80, 884–895 (2012). PubMed PMC
Berjanskii MV & Wishart DS Unraveling the meaning of chemical shifts in protein NMR. Biochim. Biophys. Acta 1865, 1564–1576 (2017). PubMed
Nilges M A calculation strategy for the structure determination of symmetric dimers by 1H NMR. Proteins 17, 297–309 (1993). PubMed
Nilges M Ambiguous distance data in the calculation of NMR structures. Fold. Des. 2, S53–7 (1997). PubMed
Herrmann T, Güntert P & Wüthrich K Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA. J. Mol. Biol. 319, 209–227 (2002). PubMed
Shen Y & Bax A Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. J. Biomol. NMR 56, 227–241 (2013). PubMed PMC
Chen VB et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2010). PubMed PMC
Costantini S, Colonna G & Facchiano AM ESBRI: a web server for evaluating salt bridges in proteins. Bioinformation 3, 137–138 (2008). PubMed PMC
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