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Related Experiment Videos

How are model protein structures distributed in sequence space?

E Bornberg-Bauer1

  • 1Abteilung Theoretische Bioinformatik, Deutsches Krebsforschungszentrum, Heidelberg, Germany. bornberg@dkfz-heidelberg.de

Biophysical Journal
|November 25, 1997
PubMed
Summary
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The hydrophobic-polar (HP) model reveals that protein structure evolution favors few common folds over many rare ones, similar to real proteins. Neutral nets in sequence space explain this dominance and protein family organization.

Area of Science:

  • Computational biology
  • Protein folding
  • Evolutionary modeling

Background:

  • Understanding protein sequence-structure relationships is crucial for molecular biology.
  • The hydrophobic-polar (HP) model simplifies protein folding on a lattice to study fundamental principles.
  • Evolutionary dynamics of protein folds remain an area of active research.

Purpose of the Study:

  • To analyze the sequence-to-structure map in the HP model to understand protein evolution.
  • To investigate the distribution and characteristics of protein folds in sequence space.
  • To explore the role of neutral mutations and network structures in protein evolution.

Main Methods:

  • Analysis of figure-to-structure maps for all unique HP model protein sequences on a square lattice.

Related Experiment Videos

  • Ranking protein structures by frequency and fitting the distribution to a generalized Zipf's law.
  • Identification and characterization of "neutral nets" and prototype sequences within sequence space.

    • Main Results:

    • A dominance of few very frequent protein structures and many rare ones was observed, following a generalized Zipf's law.
    • Most sequences map to the same structure within "neutral nets," centered around sequences tolerating many neutral mutations.
    • Frequent structures primarily conserve hydrophobic cores, while rare structures are sensitive to surface mutations; shape space covering is unlikely.

    Conclusions:

    • The simple HP model captures key features of real protein sequence-to-structure maps, including fold dominance.
    • Neutral nets in sequence space, analogous to protein families, are dense and well-separated.
    • Findings offer insights into protein evolution and potential applications for database searches.