Welcome to HH-MOTiF, a novel protein motif discovery method that combines hidden Markov model (HH-) comparisons with a hierarchical representation of identified SLiMs in motif trees. Due to extensive validation of motif trees, HH-MOTiF can find remotely conserved motifs in data sets with low-complexity regions or high redundancy.
HH-MOTiF is designed for datasets <50 proteins. A typical application would be to search for a common binding motif in a set of proteins interacting with the same hub protein.
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* If you use the tool, please cite: Prytuliak, R., Volkmer, M., Meier, M. & Habermann, B. H. (2017). HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons. Nucleic Acids Research.