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, 10 (5), M110.004994

Complex Networks Govern Coiled-Coil Oligomerization--Predicting and Profiling by Means of a Machine Learning Approach

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Complex Networks Govern Coiled-Coil Oligomerization--Predicting and Profiling by Means of a Machine Learning Approach

Carsten C Mahrenholz et al. Mol Cell Proteomics.

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 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.

Figures

Fig. 1.
Fig. 1.
Average ROC curve of the PrOCoil model, obtained from 10-fold cross-validation on the 60%-clustered data set. The superimposed error bars illustrate the deviations of the 10 ROC curves from which the average (red curve) was computed.
Fig. 2.
Fig. 2.
List of the 25 strongest pairwise patterns. Dimer patterns are highlighted in pink, and trimer patterns are highlighted in blue. For instance, the top dimer pattern E … L, spanning columns g to d2, describes a pattern with Glu at a g position, Leu at the next d position, and three arbitrary amino acids in between.
Fig. 3.
Fig. 3.
Sequence profiling and classification of a typical dimer and a typical trimer. The plot shows (A) the hemagglutinin trimer and (B) the JUN-JUN dimer based on pairwise patterns of the PrOCoil model, visualizing the contribution of each amino acid position to the overall oligomeric tendency. The area above the base line equates to the positive/trimeric contributions, the area below corresponds to the negative/dimeric contributions. The three-dimensional structures were plotted using PyMOL (http://pymol.sourceforge.net/).
Fig. 4.
Fig. 4.
Sequence profiling and classification of GCN4 and its mutants. The plot shows (A) the dimeric transcriptional activator protein GCN4wt, (B) the trimeric GCN4N16I,L19N mutant, (C) the trimeric GCN4E22R,K27E mutant, and (D) the dimeric GCN4V23K,K27E mutant. The sequence profiling plots on the right side visualize the contribution of each amino acid position to the overall oligomeric tendency, based on the pairwise patterns from the PrOCoil model. The area above the base line (blue) equates to the positive/trimeric contributions, the area below (orange) corresponds to the negative/dimeric contributions. Red bars mark the positions that were mutated. Patterns of the top 90 pairwise pattern list found in GCN4 and its mutants are depicted on the left side to visualize which patterns are added (dark color) or lost (gray) through mutation.

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