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Computational Design of Structured and Functional Peptide Macrocycles

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Peptide Macrocycles

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2371))

Abstract

Structure-based computational design methods have been developed to create proteins in silico with diverse shapes and sizes that accurately fold in vitro, from 7-residue macrocycles to megadalton-scale self-assembling nanomaterials. Precise control over protein shape has further enabled design and optimization of functional therapeutic proteins, including agonists, antagonists, enzymes, and vaccines. Computational design of functional peptides of smaller size presents a persistent challenge, with few successful examples to date. Herein we describe validated general methods for computational design of peptides using the Rosetta molecular modeling suite and discuss outstanding challenges and future directions.

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References

  1. Zorzi A, Deyle K, Heinis C (2017) Cyclic peptide therapeutics: past, present and future. Curr Opin Chem Biol 38:24–29. https://doi.org/10.1016/j.cbpa.2017.02.006

    Article  CAS  PubMed  Google Scholar 

  2. Craik DJ, Fairlie DP, Liras S, Price D (2013) The future of peptide-based drugs. Chem Biol Drug Des 81:136–147. https://doi.org/10.1111/cbdd.12055

    Article  CAS  PubMed  Google Scholar 

  3. Lau JL, Dunn MK (2018) Therapeutic peptides: historical perspectives, current development trends, and future directions. Bioorg Med Chem 26:2700–2707. https://doi.org/10.1016/j.bmc.2017.06.052

    Article  CAS  PubMed  Google Scholar 

  4. Rhodes CA, Doughtery PG, Cooper JK, Qian Z, Lindert S, Wang Q-E, Pei D (2018) Cell-permeable bicyclic peptidyl inhibitors against NEMO-IkappaB kinase interaction directly from a combinatorial library. J Am Chem Soc 140:12102–12110. https://doi.org/10.1021/jacs.8b06738

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Touti F, Gates ZP, Bandyopadhyay A, Lautrette G, Pentelute BL (2019) In-solution enrichment identifies peptide inhibitors of protein-protein interactions. Nat Chem Biol 15:410–418. https://doi.org/10.1038/s41589-019-0245-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Huang Y, Wiedmann MM, Suga H (2019) RNA display methods for the discovery of bioactive macrocycles. Chem Rev 119:10360–10391. https://doi.org/10.1021/acs.chemrev.8b00430

    Article  CAS  PubMed  Google Scholar 

  7. Liu R, Li X, Xiao W, Lam KS (2017) Tumor-targeting peptides from combinatorial libraries. Adv Drug Deliv Rev 110-111:13–37. https://doi.org/10.1016/j.addr.2016.05.009

    Article  CAS  PubMed  Google Scholar 

  8. Ashby M, Petkova A, Gani J, Mikut R, Hilpert K (2017) Use of peptide libraries for identification and optimization of novel antimicrobial peptides. Curr Top Med Chem 17:537–553. https://doi.org/10.2174/1568026616666160713125555

    Article  CAS  PubMed  Google Scholar 

  9. Lu P, Min D, DiMaio F, Wei KY, Vahey MD et al (2018) Accurate computational design of multipass transmembrane proteins. Science 359:1042–1046. https://doi.org/10.1126/science.aaq1739

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bale JB, Gonen S, Liu Y, Sheffler W, Ellis D et al (2016) Accurate design of megadalton-scale two-component icosahedral protein complexes. Science 353:389–394. https://doi.org/10.1126/science.aaf8818

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Dou J, Vorobieva AA, Sheffler W, Doyle LA, Park H et al (2018) De novo design of a fluorescence-activating beta-barrel. Nature 561:485–491. https://doi.org/10.1038/s41586-018-0509-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Huang PS, Feldmeier K, Parmeggiani F, Fernandez Velasco DA, Höker B, Baker D (2016) De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy. Nat Chem Biol 12:29–34. https://doi.org/10.1038/nchembio.1966

    Article  CAS  PubMed  Google Scholar 

  13. Boyken SE, Chen Z, Groves B, Langan RA, Oberdorfer G et al (2016) De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity. Science 352:680–687. https://doi.org/10.1126/science.aad8865

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Brunette TJ, Parmeggiani F, Huang P-S, Bhabha G, Ekiert DC et al (2015) Exploring the repeat protein universe through computational protein design. Nature 528:580–584. https://doi.org/10.1038/nature16162

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Marcos E, Basanta B, Chidyausiku T, Tang Y, Oberdorfer G et al (2017) Principles for designing proteins with cavities formed by curved beta sheets. Science 355:201–206. https://doi.org/10.1126/science.aah7389

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bhardwaj G, Mulligan VK, Bahl CD, Gilmore JM, Harvey PJ et al (2016) Accurate de novo design of hyperstable constrained peptides. Nature 538:329–335. https://doi.org/10.1038/nature19791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hosseinzadeh P, Bhardwaj G, Mulligan VK, Shortridge MD, Craven TW et al (2017) Comprehensive computational design of ordered peptide macrocycles. Science 358:1461–1466. https://doi.org/10.1126/science.aap7577

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J et al (2011) ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 487:545–574. https://doi.org/10.1016/B978-0-12-381270-4.00019-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Leman JK, Wietzner BD, Lewis SM, Adolf-Bryfogle J, Alam N et al (2020) Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 17(7):665–680. https://doi.org/10.1038/s41592-020-0848-2

    Article  CAS  PubMed  Google Scholar 

  20. Peraro L, Zou Z, Makwana KM, Cummings AE, Ball HL et al (2017) Diversity-oriented stapling yields intrinsically cell-penetrant inducers of autophagy. J Am Chem Soc 139:7792–7802. https://doi.org/10.1021/jacs.7b01698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Fairlie DP, Dantas A (2016) A. Review stapling peptides using cysteine crosslinking. Biopolymers 106:843–852. https://doi.org/10.1002/bip.22877

    Article  CAS  PubMed  Google Scholar 

  22. Coutsias EA, Seok C, Jacobson MP, Dill KA (2004) A kinematic view of loop closure. J Comput Chem 25:510–528. https://doi.org/10.1002/jcc.10416

    Article  CAS  PubMed  Google Scholar 

  23. Mandell DJ, Coutsias EA, Kortemme T (2009) Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling. Nat Methods 6:551–552. https://doi.org/10.1038/nmeth0809-551

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Renfrew PD, Craven TW, Butterfoss GL, Kirshenbaum K, Bonneau R (2014) A rotamer library to enable modeling and design of peptoid foldamers. J Am Chem Soc 136:8772–8782. https://doi.org/10.1021/ja503776z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Renfrew PD, Choi EJ, Bonneau R, Kuhlman B (2012) Incorporation of noncanonical amino acids into Rosetta and use in computational protein-peptide interface design. PLoS One 7:e32637. https://doi.org/10.1371/journal.pone.0032637

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. http://www.rosettacommons.org/docs/latest/scripting_documentation/RosettaScripts/composite_protocols/generalized_kic/GeneralizedKIC

  27. Alford RF, Leaver-Fay A, Jeliazkov JR, O’Meara MJ, DiMaio FP et al (2017) The Rosetta all-atom energy function for macromolecular modeling and design. J Chem Theory Comput 13:3031–3048. https://doi.org/10.1021/acs.jctc.7b00125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Park H, Bradley P, Greisen P Jr, Liu Y, Mulligan VK et al (2016) Simultaneous optimization of biomolecular energy functions on features from small molecules and macromolecules. J Chem Theory Comput 12:6201–6212. https://doi.org/10.1021/acs.jctc.6b00819

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. http://www.rosettacommons.org/demos/latest/tutorials/Optimizing_Sidechains_The_Packer/Optimizing_Sidechains_The_Packer

  30. http://www.rosettacommons.org/demos/latest/tutorials/minimization/minimization

  31. Mulligan VK, Kang C, Sawaya MR, Rettie S, Li X et al (2020) Computational design of mixed chirality peptide macrocycles with internal symmetry. Protein Sci 29:2433–2445. https://doi.org/10.1002/pro.3974

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Caro JA, Harpole KW, Kasinath V, Lim J, Granja J et al (2017) Entropy in molecular recognition by proteins. Proc Natl Acad Sci U S A 114:6563–6568. https://doi.org/10.1073/pnas.1621154114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Malde AK, Hill TA, Iyer A, Fairlie DP (2019) Crystal structures of protein-bound cyclic peptides. Chem Rev 119:9861–9914. https://doi.org/10.1021/acs.chemrev.8b00807

    Article  CAS  PubMed  Google Scholar 

  34. Rautureau GJ, Day CL, Hinds MG (2010) Intrinsically disordered proteins in bcl-2 regulated apoptosis. Int J Mol Sci 11:1808–1824. https://doi.org/10.3390/ijms11041808

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. http://www.rosettacommons.org/docs/latest/structure_prediction/simple_cycpep_predict

  36. http://www.rosettacommons.org/docs/latest/scripting_documentation/RosettaScripts/RosettaScripts

  37. Silva DA, Correia BE, Procko E (2016) Motif-driven design of protein-protein interfaces. Methods Mol Biol 1414:285–304. https://doi.org/10.1007/978-1-4939-3569-7_17

    Article  CAS  PubMed  Google Scholar 

  38. Siegert TR, Bird M, Kritzer JA (2017) Identifying loop-mediated protein-protein interactions using LoopFinder. Methods Mol Biol 1561:255–277. https://doi.org/10.1007/978-1-4939-6798-8_15

    Article  CAS  PubMed  Google Scholar 

  39. Sedan Y, Marcu O, Lyskov S, Schueler-Furman O (2016) Peptiderive server: derive peptide inhibitors from protein-protein interactions. Nucleic Acids Res 44:W536–W541. https://doi.org/10.1093/nar/gkw385

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. http://www.rosettacommons.org/docs/latest/rosetta_basics/preparation/preparing-structures

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Acknowledgments

We thank the Rosetta community (RosettaCommons) for decades of work leading up to and including the development of these methods. We also thank the commons for detailed documentation of these methods, which provide an invaluable guide for Rosetta users both new and experienced. We specifically acknowledge Dr. V.K. Mulligan for implementation of much of the code described in this chapter and Dr. G. Bhardwaj for his contributions to developing many of the methods. We would also like to thank Dr. L. Goldschmidt for consultation on requirements for Rosetta software installation.

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Authors and Affiliations

  1. Institute for Protein Design, University of Washington, Seattle, WA, USA

    Stephanie Berger

  2. Knight Campus, University of Oregon, Eugene, OR, USA

    Parisa Hosseinzadeh

Authors
  1. Stephanie Berger
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  2. Parisa Hosseinzadeh
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Corresponding author

Correspondence to Parisa Hosseinzadeh .

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Editors and Affiliations

  1. DEVCOM Army Research Lab, Adelphi, MD, USA

    Matthew B. Coppock

  2. DEVCOM Army Research Lab, Adelphi, MD, USA

    Alexander J. Winton

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Berger, S., Hosseinzadeh, P. (2022). Computational Design of Structured and Functional Peptide Macrocycles . In: Coppock, M.B., Winton, A.J. (eds) Peptide Macrocycles. Methods in Molecular Biology, vol 2371. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1689-5_5

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  • DOI: https://doi.org/10.1007/978-1-0716-1689-5_5

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1688-8

  • Online ISBN: 978-1-0716-1689-5

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