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Evolutionary information for specifying a protein fold

Author

Listed:
  • Michael Socolich

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Steve W. Lockless

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center
    The Rockefeller University)

  • William P. Russ

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Heather Lee

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Kevin H. Gardner

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Rama Ranganathan

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

Abstract

Classical studies show that for many proteins, the information required for specifying the tertiary structure is contained in the amino acid sequence. Here, we attempt to define the sequence rules for specifying a protein fold by computationally creating artificial protein sequences using only statistical information encoded in a multiple sequence alignment and no tertiary structure information. Experimental testing of libraries of artificial WW domain sequences shows that a simple statistical energy function capturing coevolution between amino acid residues is necessary and sufficient to specify sequences that fold into native structures. The artificial proteins show thermodynamic stabilities similar to natural WW domains, and structure determination of one artificial protein shows excellent agreement with the WW fold at atomic resolution. The relative simplicity of the information used for creating sequences suggests a marked reduction to the potential complexity of the protein-folding problem.

Suggested Citation

  • Michael Socolich & Steve W. Lockless & William P. Russ & Heather Lee & Kevin H. Gardner & Rama Ranganathan, 2005. "Evolutionary information for specifying a protein fold," Nature, Nature, vol. 437(7058), pages 512-518, September.
    • Handle: RePEc:nat:nature:v:437:y:2005:i:7058:d:10.1038_nature03991
    DOI: 10.1038/nature03991
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    Citations

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    Cited by:

    1. Xu, Xiu-Lian & Shi, Jin-Xuan & Wang, Jun & Li, Wenfei, 2021. "Long-range correlation and critical fluctuations in coevolution networks of protein sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    2. Shunshi Kohyama & Béla P. Frohn & Leon Babl & Petra Schwille, 2024. "Machine learning-aided design and screening of an emergent protein function in synthetic cells," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Tiberiu Teşileanu & Lucy J Colwell & Stanislas Leibler, 2015. "Protein Sectors: Statistical Coupling Analysis versus Conservation," PLOS Computational Biology, Public Library of Science, vol. 11(2), pages 1-20, February.
    4. Umberto Lupo & Damiano Sgarbossa & Anne-Florence Bitbol, 2022. "Protein language models trained on multiple sequence alignments learn phylogenetic relationships," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    5. Jennifer L Lahti & Adam P Silverman & Jennifer R Cochran, 2009. "Interrogating and Predicting Tolerated Sequence Diversity in Protein Folds: Application to E. elaterium Trypsin Inhibitor-II Cystine-Knot Miniprotein," PLOS Computational Biology, Public Library of Science, vol. 5(9), pages 1-15, September.
    6. Yasser Roudi & Sheila Nirenberg & Peter E Latham, 2009. "Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-18, May.
    7. Shou-Wen Wang & Anne-Florence Bitbol & Ned S Wingreen, 2019. "Revealing evolutionary constraints on proteins through sequence analysis," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-16, April.
    8. Francisco McGee & Sandro Hauri & Quentin Novinger & Slobodan Vucetic & Ronald M. Levy & Vincenzo Carnevale & Allan Haldane, 2021. "The generative capacity of probabilistic protein sequence models," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    9. Hugo Jacquin & Amy Gilson & Eugene Shakhnovich & Simona Cocco & Rémi Monasson, 2016. "Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-18, May.
    10. Erik van Nimwegen, 2016. "Inferring Contacting Residues within and between Proteins: What Do the Probabilities Mean?," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-10, May.

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