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Peptide half-life predictors

Predictors of how long a free peptide survives. They emit peptide_half_life, read with result.peptide_half_life; the sample matrix (serum, plasma, whole blood) lives in the measurement context. For the wider set of pharmacokinetic, uptake and tissue-exposure kinds, see peptide PK, uptake, and tissue exposure.

Predictor What it estimates Needs
PeptiVerse Parent-peptide half-life in human serum pinned PeptiVerse + ESM2 snapshots (PEPTIVERSE_HOME, PEPTIVERSE_ESM_HOME) + a torch/transformers Python
PlifePred2 Parent-peptide half-life, matrix unknown plifepred2==1.0 (PLIFEPRED2_HOME) + pinned Pfeature (PFEATURE_HOME)

peptide_half_life records how long the parent peptide persists, in hours. Its context distinguishes a defined solution, serum/plasma/whole blood, cellular compartments, and systemic in-vivo PK without multiplying kind strings.

It is deliberately a separate kind from pMHC_stability, which is the dissociation half-life of an assembled peptide-MHC complex (a different molecule in a different assay), and from the cleavage kinds, which are site-resolved and intracellular. MeasurementContext preserves the matrix, compartment, analyte, and systemic scope when they are known.

PeptiVerse

PeptiVerse wraps one endpoint of the upstream multi-property platform. Its dependencies (torch, transformers==4.46.0, xgboost, lightning, and ESM2) stay out of the mhctools environment: inference runs offline in a subprocess under PEPTIVERSE_PYTHON. Provision the exact snapshots before prediction:

git clone https://huggingface.co/ChatterjeeLab/PeptiVerse
git -C PeptiVerse checkout 8cf0b21dae356278ae96b414a088e4360357d16c
huggingface-cli download facebook/esm2_t33_650M_UR50D \
  --revision 08e4846e537177426273712802403f7ba8261b6c \
  --include config.json tokenizer_config.json special_tokens_map.json vocab.txt model.safetensors \
  --local-dir /models/esm2_t33_650M_UR50D
export PEPTIVERSE_HOME="$PWD/PeptiVerse"
export PEPTIVERSE_ESM_HOME=/models/esm2_t33_650M_UR50D

from mhctools import PeptideContext, PeptideInput, PeptiVerse

predictor = PeptiVerse(device="cpu")       # resolves PEPTIVERSE_HOME / ~/PeptiVerse
exact_input = PeptideInput(
    "SIINFEKL",
    occurrence_id="sample-1:occurrence-2",
    context=PeptideContext(matrix="serum", assay_species="Homo sapiens"),
)
results = predictor.predict([exact_input, "KLGGALQAK"])
results[0].peptide_half_life.value         # hours, higher = longer-lived
results[0].serum_half_life.peptide_input   # exact chemistry + context
results[0].serum_half_life.cache_key       # input + assets + settings
predictor.artifact_inventory.to_dict()     # exact files, hashes, capability

Sequence input only. Upstream's SMILES models return a number that is not on the hours scale, because the expm1 inverse transform is applied only to the sequence model. Although mhctools records exact chemical form, this adapter does not consume it, so terminal modifications, attachments, and non-standard residues are rejected rather than scored as their unmodified sequence. Pass on_unsupported="record" to retain unsupported entries in a mixed batch.

The sequence half-life model was fit on 130 examples and evaluated by cross-validation only, from a preprint, with no external test set and no evaluation on long vaccine peptides. Upstream declares Apache-2.0 on its model card and MIT in its README. mhctools verifies the exact inference source, model, calibration, ESM2 weights, configuration and tokenizer files before launch. The PeptiVerse checkpoint and calibration still use unsafe pickle serialization, and a matching checksum shows identity, not safety, so use only snapshots you trust.

PlifePred2

This endpoint's semantics are not established. PlifePred2 ships no publication, no training data and no target definition, so its units, transform, species and assay matrix are all inferred from the artifacts. By default the wrapper reports only the model's native output and claims no duration at all.

from mhctools import PlifePred2

predictor = PlifePred2()                       # PLIFEPRED2_HOME + PFEATURE_HOME
results = predictor.predict(["SIINFEKLGGALQAKKY"])
results[0].peptide_half_life.score             # native output, higher = longer-lived
results[0].peptide_half_life.value             # None by default
predictor.artifact_inventory.to_dict()         # exact files, hashes, capability
predictor.last_qc["log10_seconds"]             # the same value, named

# Opt in to a duration, accepting the inference below:
opted_in = PlifePred2(assume_log10_seconds=True)
opted_in.predict(["SIINFEKLGGALQAKKY"])[0].peptide_half_life.value # hours

What is known. Both shipped models are RandomForestRegressor, verified by loading them. So the output is not a class probability. Upstream's docs ("Halflife … Predicted probability") and its CLI's predict_proba branch are both wrong, and the branch is dead code. Being monotone in half-life, the output ranks correctly whatever the transform turns out to be.

What is inferred. log10(half-life in seconds) is the strongest reading: inverting the forests' extreme leaf values under it gives round durations: exactly 7.000 days for the natural model and 95.0 days for the modified one, to about seven significant figures, where log2 and ln both invert the whole training range to a few seconds up to a couple of minutes. The minimum also lands on 20.2 s, matching the 20-second floor in the lineage paper. That last point is corroboration rather than proof: the same forests hold targets past that paper's 24-hour ceiling, so PlifePred2 was trained on a different dataset and the old filter cannot establish the new target. Note also that the lineage paper states log2, not log10.

What is not established. The species and assay matrix. The result therefore uses generic peptide_half_life with matrix=None; do not report it as a measured whole-blood property or treat it as interchangeable with PeptiVerse's human-serum endpoint.

Natural peptides only, 12–100 residues. Upstream's CLI silently drops out-of-range and modified sequences into an eliminated_sequences.csv and returns a shorter result set; mhctools rejects them instead so a caller never gets a quietly truncated answer.

The Linux-only pfeature_comp binary that plifepred2 bundles is not used. It is a PyInstaller freeze of Pfeature's pfeature_comp.py, and that plain Python source computes the same descriptor on any platform. Both upstreams are GPLv3, so neither is vendored and neither is imported into the mhctools interpreter.

PlifePred2 cites no publication of its own, so its training set is unverified beyond what the artifacts reveal. In the lineage paper the composition-based natural model was the weaker of the pair (r = 0.643 against 0.743), and sequences up to 90% similar were deliberately kept in the data, so reported accuracy is optimistic for novel peptides.