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DeepPBS Motif Specificity
License: This constraint can use multiple tools, each under its own license. See the Tools Used tab and each tool’s page for license details.

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evo-design/proto-language/proto_language/constraint/protein_structure/dna_motif_specificity_constraint.py
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Constraint contributors
Specificity score from DeepPBS predicted base-preference matrices. Same scoring as the NA-MPNN constraint, but with DeepPBS predictions. If the DeepPBS tool call fails, every candidate receives the worst score with an explanatory metadata key rather than crashing (structure prediction still raises).

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API Reference

ConfigDeepPBSMotifSpecificityConfig Source
Config for the deeppbs-motif-specificity constraint.
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DeepPBSSpecificityConfig
Tool config for deeppbs-specificity predictions.
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enum
default:"alphafold3"
Predictor for the protein-DNA complex; must be DNA-capable (alphafold3/boltz2/protenix).Options: esmfold, esmfold2, alphafold3, boltz2, chai1, protenix, alphafold2, alphafold2_binder
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ESMFoldConfig
Configuration for ESMFold structure prediction.
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ESMFold2Config
Configuration for ESMFold2 structure prediction.
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AlphaFold3Config
Configuration for AlphaFold3 structure prediction.
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Boltz2Config
Configuration for Boltz2 structure prediction.
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Chai1Config
Configuration for Chai1 structure prediction.
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ProtenixConfig
Configuration for Protenix structure prediction.
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AlphaFold2Config
Configuration for the general AlphaFold2 multimer structure predictor.
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AlphaFold2BinderStructureConfig
Configuration for the AF2 binder-design backend.
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string
required
Target DNA motif in A/C/G/T alphabet.
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List[string]
required
Off-target motifs used for the specificity margin.
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List[integer]
0-based DNA indices mapped to motif positions (required only when scoring_mode=‘cross_entropy’).
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number
default:"1.0"
Specificity advantage satisfying the term; scale follows scoring_mode (CE or log-prob).
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integer
default:"0"
Canonical DNA chain label for motif indexing when multiple DNA chains exist (fwd strand 0).
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enum
default:"sliding_logprob"
Motif scoring: fixed-index cross_entropy or sliding-window log-prob.Options: cross_entropy, sliding_logprob
ReturnsConstraintOutput
One result per candidate; score 0.0 (best) when the target beats every off-target by desired_margin, 1.0 (worst) otherwise.

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Usage

python

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Metadata