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 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.

APCluster: an R package for affinity propagation clustering

“…7 and its exemplar sequence. Of the top five trimer patterns (Mahrenholz et al, 2011), three occur prominently in trimer cluster no. 3: E..Ie, E....Kb and I...Id, two of which occur twice or more often in the cluster and in the corresponding exemplar.…”

“…Mahrenholz et al (2011) have studied which sequence characteristics determine whether a given coiled coil segment tends to form a dimer or trimer. Mahrenholz et al (2011) found out that pair interactions of amino acids are the key to understanding coiled coil oligomerization and, consequently, propose a custom string kernel based on pair interactions, the coiled coil kernel, upon which they base their prediction tool PrOCoil.…”

“…This is also clearly visible from the excellent matching of the exemplars with the sequence logos displayed on top of the alignments. Below the alignments, we displayed the heptad annotation and which of the most indicative oligomerization patterns according to Mahrenholz et al (2011) match the majority of sequences in the cluster. We see that all top five dimer patterns E...Lg, L...Nd, N..La, E.Ee and N.....Ee are well represented in dimer cluster no.…”

“…Those seven residues are usually annotated with letters a-g, where positions a and d are often occupied by hydrophobic residues and positions e and g are often occupied by charged residues (Mason and Arndt, 2004). Mahrenholz et al (2011) have studied which sequence characteristics determine whether a given coiled coil segment tends to form a dimer or trimer. Mahrenholz et al (2011) found out that pair interactions of amino acids are the key to understanding coiled coil oligomerization and, consequently, propose a custom string kernel based on pair interactions, the coiled coil kernel, upon which they base their prediction tool PrOCoil.…”

“…Therefore, the coiled coil kernel is an ideal choice for clustering this type of sequences, too. We used the coiled coil dataset of Mahrenholz et al (2011) consisting of a selection of 477 confirmed coiled coils (385 dimers and 92 trimers) from the PDB and proceeded as follows to identify most typical coiled coil sequences: adopting the coiled coil kernel with the same settings as in (Mahrenholz et al, 2011) as sequence similarity measure, we first used preferenceRange() to determine reasonable input preferences. We decided to use a default input preference of −1 for all further studies.…”