Complex networks govern coiled-coil oligomerization--predicting and profiling by means of a machine learning approach.

ABSTRACT: 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 irrelevan

SUBMITTER: Mahrenholz CC 

PROVIDER: S-EPMC3098589 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature