Introduction

Understanding the relationship between protein sequence and structure is one of the great challenges in biology. In the case of the ubiquitous coiled-coil motif, structure and occurrence have been described in extensive detail, but there is a lack of insight into the rules that govern oligomerization, i.e. how many α-helices form a given coiled coil. To shed new light on the formation of two- and three-stranded coiled coils, we developed a machine learning approach to identify rules in the form of weighted amino acid patterns. These rules form the basis of our classification tool, PrOCoil, which also visualizes the contribution of each individual amino acid to the overall oligomeric tendency of a given coiled-coil sequence. We discovered that sequence positions previously thought irrelevant to direct coiled-coil interaction have an undeniable impact on stoichiometry. Our rules also demystify the oligomerization behavior of the yeast transcription factor GCN4, which can now be described as a hybrid--part dimer and part trimer--with both theoretical and experimental justification.

Publications

  1. Complex networks govern coiled-coil oligomerization--predicting and profiling by means of a machine learning approach.
    Cite this
    Mahrenholz CC, Abfalter IG, Bodenhofer U, Volkmer R, Hochreiter S, 2011-05-01 - Molecular & cellular proteomics : MCP

Credits

  1. Carsten C Mahrenholz
    Developer

  2. Ingrid G Abfalter
    Developer

  3. Ulrich Bodenhofer
    Developer

  4. Rudolf Volkmer
    Developer

  5. Sepp Hochreiter
    Investigator

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Summary
AccessionBT000443
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted BySepp Hochreiter