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


E-mail to notify on results (optional)


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Job name (optional)

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


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


Query regions file (optional)
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Search for orthologs


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Restrict gaps (limit max. gap length to 1)


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Check surface accessibility (mask inner globular regions)


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Check disorder (mask ordered regions)


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Smart homology filtering


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Maximal regex p-value


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Show best suboptimal if no motifs found


E-mail to notify on results (optional)


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Job name (optional)

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.


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


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Organism



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Only full-length motif matches


E-mail to notify on results (optional)


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Job name (optional)
* If you use the tool, please cite: Roman Prytuliak and Bianca H. Habermann: (publication in preparation)
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