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Related Experiment Videos

Fold designability, distribution, and disease.

Philip Wong1, Dmitrij Frishman

  • 1Institute for Bioinformatics, GSF--National Research Center for Environment and Health, Neuherberg, Germany.

Plos Computational Biology
|May 9, 2006
PubMed
Summary
This summary is machine-generated.

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Protein fold family counts correlate with sequence divergence and promiscuity. Folds with more families exhibit higher divergence, while disease-related proteins often possess simpler folds, suggesting structural characteristics impact disease association.

Area of Science:

  • Protein structure and evolution
  • Bioinformatics and computational biology
  • Human disease genetics

Background:

  • Protein fold designability is often assessed by the number of protein families within a fold.
  • Understanding the relationship between protein structure, evolution, and disease is crucial.

Purpose of the Study:

  • To investigate the correlation between the number of families within protein folds and their evolutionary characteristics.
  • To explore the association between protein fold characteristics and human diseases.

Main Methods:

  • Analysis of orthologous proteins to quantify sequence divergence.
  • Assessment of fold promiscuity by counting unique partner folds.
  • Correlation analysis between fold family counts, sequence divergence, proteome occurrence, promiscuity, and disease association.

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Main Results:

  • Protein folds with a higher number of families exhibit greater sequence divergence among orthologs.
  • Folds with more families tend to appear more frequently in proteomes and show increased promiscuity.
  • Many disease-related proteins are associated with folds containing fewer families, including those linked to high-frequency diseases.

Conclusions:

  • The number of families within a protein fold is a significant factor influencing its evolutionary trajectory and distribution in nature.
  • Fold characteristics, specifically family counts, are linked to the prevalence and nature of certain human diseases.
  • This research highlights the importance of protein structural features in understanding disease etiology.