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The spatial architecture of protein function and adaptation

Author

Listed:
  • Richard N. McLaughlin Jr

    (Green Center for Systems Biology, University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center
    Present address: Division of Basic Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, Washington 98109-1024, USA.)

  • Frank J. Poelwijk

    (Green Center for Systems Biology, University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Arjun Raman

    (Green Center for Systems Biology, University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Walraj S. Gosal

    (Green Center for Systems Biology, University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Rama Ranganathan

    (Green Center for Systems Biology, University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

Abstract

A high-throughput mutagenesis study in a PDZ domain shows that biochemical function and adaptation primarily originate from a collectively evolving amino acid network within the structure termed a protein sector.

Suggested Citation

  • Richard N. McLaughlin Jr & Frank J. Poelwijk & Arjun Raman & Walraj S. Gosal & Rama Ranganathan, 2012. "The spatial architecture of protein function and adaptation," Nature, Nature, vol. 491(7422), pages 138-142, November.
    • Handle: RePEc:nat:nature:v:491:y:2012:i:7422:d:10.1038_nature11500
    DOI: 10.1038/nature11500
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    Citations

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    Cited by:

    1. Ahmed Abdul Quadeer & David Morales-Jimenez & Matthew R McKay, 2018. "Co-evolution networks of HIV/HCV are modular with direct association to structure and function," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-29, September.
    2. Khaled Daqrouq & Rami Alhmouz & Ahmed Balamesh & Adnan Memic, 2015. "Application of Wavelet Transform for PDZ Domain Classification," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-16, April.
    3. Emily K. Makowski & Patrick C. Kinnunen & Jie Huang & Lina Wu & Matthew D. Smith & Tiexin Wang & Alec A. Desai & Craig N. Streu & Yulei Zhang & Jennifer M. Zupancic & John S. Schardt & Jennifer J. Lin, 2022. "Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Bian Li & Dan M. Roden & John A. Capra, 2022. "The 3D mutational constraint on amino acid sites in the human proteome," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    5. 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.
    6. Alistair Bailey & Andy van Hateren & Tim Elliott & Jörn M Werner, 2014. "Two Polymorphisms Facilitate Differences in Plasticity between Two Chicken Major Histocompatibility Complex Class I Proteins," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    7. 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.
    8. 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.

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