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.


JavaScript is disabled. Please, enable JavaScript to access the functionality of this website

Motif prediction with optimized parameters. Requires only a multiple FASTA file with protein sequences to start. Close orthologs will be predicted to assess the sequence conservation


Input FASTA sequences
-OR-
Sample
Input FASTA file (at least 3 protein sequences)
?
If no input provided, the sample file will be submitted


E-mail to notify on results (optional)


?
Job name (optional)
Output sample
More help

Prediction with flexible options for fine-tuned performance. Provides possibility to submit own collections of orthologs for each protein


Sample
Input protein set (a FASTA file OR ZIP archive of FASTA files)
?
File submission is mandatory in advanced mode


Sample
Query regions file (optional)
?


?
Search for orthologs


?
Restrict gaps (limit max. gap length to 1)


?
Check surface accessibility (mask inner globular regions)


?
Check disorder (mask ordered regions)


?
Smart homology filtering


?
Maximal regex p-value


?
Show best suboptimal if no motifs found


E-mail to notify on results (optional)


?
Job name (optional)
Output sample
More help

Search for a specific motif within the whole proteome to find new instnces of already known motifs. This is usually the next step after the de novo prediction within a subset of proteins.


Sample
Input motif (as a FASTA file)
?
If not provided, the sample file will be submitted


?
Organism



?
Only full-length motif matches


E-mail to notify on results (optional)


?
Job name (optional)
Output sample
More help
* 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.
STANDARD