Representation of a motif tree
HH-MOTiF: de novo detection of functional short linear motifs in proteins
Protein motifs are defined as self-sufficient functional units. They are typically only between 3 and 23 amino acids long and have various functions in proteins. They can serve as cleavage sites, are required for proteasomal degradation, are involved in docking and ligand binding, serve as signals for post-translational modification or are signals for subcellular localization.
Their shortness and the fact that they typically lack substantial sequence conservation makes them very difficult to find de novo – i.e. without prior information on the localization or nature of the motif. We are using evolutionary restricted Hidden Markov Model (HMM) comparison in combination with a hierarchical model of motif trees to identify short functional motifs in proteins de novo (Prytuliak, et al., NAR 2017;45 (W1):W470-W477, 2017, doi: 10.1093/nar/gkx341). In collaboration with wet-lab researchers, we experimentally test our predicted motifs.
We have meanwhile developed a stand-alone version of HH-MOTiF that can be downloaded from our git-repository. The stand-alone version allows local usage and processing of large datasets.
This work was supported by the Max Planck Society. The stand-alone version was developed with the support of the CNRS and the IFB.