> ## Documentation Index > Fetch the complete documentation index at: https://proto.evodesign.org/docs/llms.txt > Use this file to discover all available pages before exploring further. # Sequence Motif Match > Score DNA sequences against motifs using MEME FIMO
Sequence Motif Match
**License:** MEME Suite (FIMO) is licensed under Custom (MEME Suite Academic License) and has restrictions around commercial use and may require explicit attribution when utilized. Please refer to [the license](https://github.com/althonos/pymemesuite/blob/main/vendor/meme/COPYING) for full terms.

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evo-design/proto-language/proto\_language/constraint/sequence\_annotation/seq\_motif\_constraint.py
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```bibtex theme={null} @article{grant2011fimo, title={FIMO: scanning for occurrences of a given motif}, author={Grant, Charles E. and Bailey, Timothy L. and Noble, William Stafford}, journal={Bioinformatics}, volume={27}, number={7}, pages={1017--1018}, year={2011}, publisher={Oxford University Press}, doi={10.1093/bioinformatics/btr064} } ```
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Score DNA sequences against sequence motifs using MEME. This constraint function uses MEME Suite's Find Individual Motif Occurrences tool to search for sequence motifs represented as position weight matrices in DNA sequences. It evaluates whether sequences contain desired motifs (wanted) or unwanted motifs (not\_wanted). The scoring strategy penalizes sequences based on motif presence: * **Unwanted motifs**: Strong matches (low p-values) result in high penalties, encouraging sequences without these binding sites * **Wanted motifs**: Strong matches result in low penalties (rewards), while missing wanted motifs result in high penalties * **No motif specification**: Any motif matches are penalized (novelty constraint) ## API Reference
ConfigSeqMotifConfig Source
Configuration for sequence motif constraint using MEME. This class defines configuration parameters for evaluating DNA sequences against known transcription factor binding motifs using MEME Suite's Find Individual Motif Occurrences tool. The constraint searches for position weight matrix motifs in sequences and can either encourage specific motifs (wanted) or discourage them (not\_wanted), enabling design of sequences with controlled sites. Motif names must match exactly with the names in the MEME file (case-sensitive). Use the MOTIF lines in the .meme file to identify available motif names. Path to MEME format motif file (.meme) containing PWMs. FIMO scan parameters (p-value threshold, both-strands); defaults match FIMO's nucleotide behavior. Motifs that should be present: 'all' (all motifs), 'none' (no requirement), or list of motif names. Motifs that should NOT be present: 'all' (reject all), 'none' (allow all), or list of motif names. Scaling factor to adjust penalty magnitude (>1 = stricter, \<1 = more lenient). Example: 1.0 If True, automatically sets unwanted motifs as complement of wanted motifs How to aggregate penalties: 'smart' (adaptive), 'average', 'max' (strictest), 'percentile' Options: `smart`, `average`, `max`, `percentile` Which percentile to use when aggregation='percentile' (0-100) When both wanted and unwanted motifs exist, weight unwanted motifs more heavily in final score
ReturnsConstraintOutput
One result per sequence. Score ranges from 0.0 (all criteria satisfied) to 1.0 (severe violations). `metadata` carries a single `motif_constraint` dict: * `penalty`: Float overall penalty score (0.0-1.0) * `wanted`: Sorted list of wanted motif names * `not_wanted`: Sorted list of unwanted motif names * `found`: Dictionary mapping motif names to their best (lowest) FIMO p-values * `details`: Dictionary with per-motif scoring details including: * `penalty`: Individual motif penalty * `status`: "wanted\_found", "wanted\_missing", "unwanted", or "unwanted\_absent" * `p_value`: FIMO p-value if motif was found * `aggregation_info`: Dictionary with aggregation statistics: * `method`: Aggregation method used * `unwanted_count`: Number of unwanted motif evaluations * `wanted_count`: Number of wanted motif evaluations * `unwanted_matches`: Number of unwanted motifs found * `wanted_matches`: Number of wanted motifs found
## Usage Requiring specific transcription factor binding sites: ```python python icon="python" theme={null} >>> from proto_language.core import Sequence, SequenceType >>> promoter_seq = Sequence("ATCGGCGGGATCGTAATATAGCATGC", "dna") >>> config = SeqMotifConfig( ... motifs_path="/data/jaspar_vertebrates.meme", ... wanted=["SP1", "lacI"], ... aggregation="average", ... ) >>> results = seq_motif_constraint([(promoter_seq,)], config) >>> print(results[0].score) # e.g., 0.15 >>> print(results[0].metadata["motif_constraint"]["found"]) # e.g., {"SP1": 1e-8} ``` ## Metadata | Property | Value | | --------------- | ---------------------- | | Key | `seq-motif` | | Function | `seq_motif_constraint` | | Category | `sequence_annotation` | | Mode | `discrete` | | Uses GPU | `False` | | Supported Types | `dna` |