The Bianca Habermann team offers the research community a new research tool for the identification of functional domains in proteins.
Proteins often make use of short sequence stretches to fulfill their function in a cell. They use these so-called short linear motifs (SLiMs) as recognition sites for binding to another protein or to DNA or RNA, for receiving a specific modification or as signals for being transported to a particular region in a cell.
Without any prior knowledge on the nature of such a protein motif, it is very difficult to discover a functional SLiM in a protein. This is mainly due to their shortness (less than 20 amino acids) and their very poor conservation.
The Habermann team at the IBDM in Marseille has developed a web-based application called HH-MOTiF, which can find novel functional motifs in proteins. To make their algorithm more powerful, they make use of evolutionary information from closely related proteins. Moreover, they have developed a specific representation of protein motifs, so-called motif trees, which helps them in detecting very weak motif signals in proteins. By trimming a motif tree, they can eliminate too weak motif candidates, as well as narrow down motifs to important, conserved residues.
HH-MOTiF beats all currently available web-tools in predicting novel protein motifs. It is available as a web-server at here.
Reference: Prytuliak R, Volkmer M, Meier M, Habermann BH. (2017). HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons. Nucleic Acids Res. 2017 Apr 29. doi: 10.1093/nar/gkx341. [Epub ahead of print]