Reading cleavage evidence

How to interpret what the cleavage models return. None of it is a probability that a peptide survives or is presented.

Interpreting rule-based evidence

The shared result contract also supports motif_rule evidence, reported as matched or not_matched with no numerical score. A rule match indicates a known recognition pattern; a non-match does not establish resistance. unsupported_reason means the input could not be assessed and carries no score. These states must remain separate in downstream displays and ranking.

Building a position track for a full sequence

A downstream tool (topiary, vaxrank) that wants to overlay cleavage evidence from several models onto one parent sequence, as a track indexed by position, reads source_bond from to_dict()["sites"] and keys everything on it. That coordinate is source_start + bond, so it is the same absolute number no matter which model or which fragment produced the site:

track = {}
for result in predict_cleavage(peptide, compartment="cytosol"):
    for site in result.to_dict()["sites"]:
        track.setdefault(site["source_bond"], []).append(
            {"model": result.model.name, "status": site["status"], "score": site["score"]})

Two model shapes contribute to that track differently, and a caller building one has to model both:

• Internal topology (mme-hydrophobic, fap-endo-gp, prep-pro) assesses every internal bond of whatever peptide it is given in one predict() call. Calling it once on the full input already produces a multi-position run of track entries.
• Terminal topology (the aminopeptidases, carboxypeptidases, DPP-family, dpp4-qpisa and eramer-step) only ever assesses the currently exposed end of its input: always exactly one bond, regardless of peptide length. It cannot tell you whether a bond in the middle of a long precursor is a plausible trimming stop; it can only assess a candidate fragment you supply. To extend a track with these models, model the hypothesized trimming step explicitly with parent.fragment(start, end, n_term=..., c_term=...) and predict on that fragment. Its source_bond still lands on the parent's absolute coordinates, so it merges into the same track. Producing a track over a whole precursor this way means enumerating candidate fragments yourself; the model does not search for them.

The same rule applies to substrate_reference evidence (THOP1, neurolysin, IRAP): it never invents a bond, so a source case that reports degradation without pinning one contributes no track entry, not a guessed position.

Because motif and source-reference models never emit numerical scores, and because not_matched/no_cleavage_detected are not probabilities of resistance, a track built this way is evidence to overlay and inspect, not a single per-position score to rank or threshold.

How strict is each motif?

A recognition pattern is only as informative as the evidence behind it, so every motif rule carries a motif_strictness grade and a strictness_basis naming the source observation that supports the grade. Read a decision through the grade rather than treating all matches alike:

Grade	What a match means	What a non-match means
required	The pattern is necessary for this activity, so a match clears a real gate. Rates, exposure and competition still decide the outcome.	Meaningful evidence against cleavage by this enzyme through this route. Still not proof of resistance.
preferred	A favoured context, not a gate.	Weak evidence. Non-matching bonds are cleaved, usually more slowly.
permissive	Little information; the rule mostly describes topology or a broad enzyme.	Almost no information.

Grades apply to motif rules only. Scored models report native units instead, and source references report what an experiment observed. Neither carries a grade, and both leave motif_strictness null.