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Principles for designing ideal protein structures

Author

Listed:
  • Nobuyasu Koga

    (University of Washington)

  • Rie Tatsumi-Koga

    (University of Washington)

  • Gaohua Liu

    (Rutgers, The State University of New Jersey, Center for Advanced Biotechnology and Medicine, Northeast Structural Genomics Consortium, Piscataway, New Jersey 08854, USA
    Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey)

  • Rong Xiao

    (Rutgers, The State University of New Jersey, Center for Advanced Biotechnology and Medicine, Northeast Structural Genomics Consortium, Piscataway, New Jersey 08854, USA
    Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey)

  • Thomas B. Acton

    (Rutgers, The State University of New Jersey, Center for Advanced Biotechnology and Medicine, Northeast Structural Genomics Consortium, Piscataway, New Jersey 08854, USA
    Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey)

  • Gaetano T. Montelione

    (Rutgers, The State University of New Jersey, Center for Advanced Biotechnology and Medicine, Northeast Structural Genomics Consortium, Piscataway, New Jersey 08854, USA
    Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey)

  • David Baker

    (University of Washington)

Abstract

Unlike random heteropolymers, natural proteins fold into unique ordered structures. Understanding how these are encoded in amino-acid sequences is complicated by energetically unfavourable non-ideal features—for example kinked α-helices, bulged β-strands, strained loops and buried polar groups—that arise in proteins from evolutionary selection for biological function or from neutral drift. Here we describe an approach to designing ideal protein structures stabilized by completely consistent local and non-local interactions. The approach is based on a set of rules relating secondary structure patterns to protein tertiary motifs, which make possible the design of funnel-shaped protein folding energy landscapes leading into the target folded state. Guided by these rules, we designed sequences predicted to fold into ideal protein structures consisting of α-helices, β-strands and minimal loops. Designs for five different topologies were found to be monomeric and very stable and to adopt structures in solution nearly identical to the computational models. These results illuminate how the folding funnels of natural proteins arise and provide the foundation for engineering a new generation of functional proteins free from natural evolution.

Suggested Citation

  • Nobuyasu Koga & Rie Tatsumi-Koga & Gaohua Liu & Rong Xiao & Thomas B. Acton & Gaetano T. Montelione & David Baker, 2012. "Principles for designing ideal protein structures," Nature, Nature, vol. 491(7423), pages 222-227, November.
  • Handle: RePEc:nat:nature:v:491:y:2012:i:7423:d:10.1038_nature11600
    DOI: 10.1038/nature11600
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    Citations

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    Cited by:

    1. Thomas W. Linsky & Kyle Noble & Autumn R. Tobin & Rachel Crow & Lauren Carter & Jeffrey L. Urbauer & David Baker & Eva-Maria Strauch, 2022. "Sampling of structure and sequence space of small protein folds," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Pralay Mitra & David Shultis & Jeffrey R Brender & Jeff Czajka & David Marsh & Felicia Gray & Tomasz Cierpicki & Yang Zhang, 2013. "An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-18, October.
    3. Willow Coyote-Maestas & David Nedrud & Antonio Suma & Yungui He & Kenneth A. Matreyek & Douglas M. Fowler & Vincenzo Carnevale & Chad L. Myers & Daniel Schmidt, 2021. "Probing ion channel functional architecture and domain recombination compatibility by massively parallel domain insertion profiling," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    4. Anindya Roy & Lei Shi & Ashley Chang & Xianchi Dong & Andres Fernandez & John C. Kraft & Jing Li & Viet Q. Le & Rebecca Viazzo Winegar & Gerald Maxwell Cherf & Dean Slocum & P. Daniel Poulson & Garret, 2023. "De novo design of highly selective miniprotein inhibitors of integrins αvβ6 and αvβ8," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    5. Hiroto Murata & Hayao Imakawa & Nobuyasu Koga & George Chikenji, 2021. "The register shift rules for βαβ-motifs for de novo protein design," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-24, August.
    6. Tamuka M. Chidyausiku & Soraia R. Mendes & Jason C. Klima & Marta Nadal & Ulrich Eckhard & Jorge Roel-Touris & Scott Houliston & Tibisay Guevara & Hugh K. Haddox & Adam Moyer & Cheryl H. Arrowsmith & , 2022. "De novo design of immunoglobulin-like domains," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Jaume Bonet & Sarah Wehrle & Karen Schriever & Che Yang & Anne Billet & Fabian Sesterhenn & Andreas Scheck & Freyr Sverrisson & Barbora Veselkova & Sabrina Vollers & Roxanne Lourman & Mélanie Villard , 2018. "Rosetta FunFolDes – A general framework for the computational design of functional proteins," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-30, November.
    8. Sagar D Khare & Timothy A Whitehead, 2015. "Introduction to the Rosetta Special Collection," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-5, December.
    9. Marc Corrales & Pol Cuscó & Dinara R Usmanova & Heng-Chang Chen & Natalya S Bogatyreva & Guillaume J Filion & Dmitry N Ivankov, 2015. "Machine Learning: How Much Does It Tell about Protein Folding Rates?," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-12, November.
    10. Lindsey A. Doyle & Brittany Takushi & Ryan D. Kibler & Lukas F. Milles & Carolina T. Orozco & Jonathan D. Jones & Sophie E. Jackson & Barry L. Stoddard & Philip Bradley, 2023. "De novo design of knotted tandem repeat proteins," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    11. Kozyrev, S.V. & Volovich, I.V., 2014. "Quinary lattice model of secondary structures of polymers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 86-95.
    12. Rebecca F Alford & Andrew Leaver-Fay & Lynda Gonzales & Erin L Dolan & Jeffrey J Gray, 2017. "A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-13, December.
    13. Jorge Roel-Touris & Marta Nadal & Enrique Marcos, 2023. "Single-chain dimers from de novo immunoglobulins as robust scaffolds for multiple binding loops," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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