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Natural-like function in artificial WW domains

Author

Listed:
  • William P. Russ

    (University of Texas Southwestern Medical Center)

  • Drew M. Lowery

    (Massachusetts Institute of Technology)

  • Prashant Mishra

    (University of Texas Southwestern Medical Center)

  • Michael B. Yaffe

    (Massachusetts Institute of Technology)

  • Rama Ranganathan

    (University of Texas Southwestern Medical Center)

Abstract

Follow the sequence It is widely believed that a protein's amino acid sequence contains all the information needed to dictate its structure, but exactly what information is both necessary and sufficient for generating a folded, functional protein is not clear. Two papers by Rama Ranganathan and co-workers tackle this question using computational protein design to construct artificial WW domains, small proteins of approximately 40 amino acid residues that bind to proline-rich sequences. The synthetic proteins adopt the characteristic WW structure and recognize typical WW target sequences. Since the information used in designing these proteins was obtained from multiple sequence alignments only, with no prior knowledge of three-dimensional structure, it is clear that for some proteins, a relatively small quantity of sequence information is sufficient to specify the complex amino acid interactions that make up a functional protein.

Suggested Citation

  • William P. Russ & Drew M. Lowery & Prashant Mishra & Michael B. Yaffe & Rama Ranganathan, 2005. "Natural-like function in artificial WW domains," Nature, Nature, vol. 437(7058), pages 579-583, September.
    • Handle: RePEc:nat:nature:v:437:y:2005:i:7058:d:10.1038_nature03990
    DOI: 10.1038/nature03990
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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. 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.
    3. 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.
    4. 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.

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