mhctools 3.17.0

Python interface to MHC binding, presentation, immunogenicity, and antigen processing predictors

mhctools

Python interface to MHC binding, presentation, immunogenicity, and antigen processing predictors.

Installation

pip install mhctools

For MHCflurry support, also run:

mhcflurry-downloads fetch

Quick start

from mhctools import NetMHCpan41

predictor = NetMHCpan41(alleles=["HLA-A*02:01", "HLA-B*07:02"])

# predict() returns a list of PeptideResult — one per peptide
results = predictor.predict(["SIINFEKL", "GILGFVFTL"])

for r in results:
    if r.affinity:
        print(f"{r.peptide} -> {r.affinity.allele} IC50={r.affinity.value:.1f}nM")

Data model

predict() returns a list of PeptideResult — one per peptide. Each result carries the peptide string and provides accessors for each prediction kind (affinity, presentation, stability, etc.). Accessors return None when a predictor doesn't produce that kind.

results = predictor.predict(["SIINFEKL", "GILGFVFTL"])
r = results[0]

r.peptide                    # "SIINFEKL"
r.affinity.value             # IC50 in nM
r.affinity.percentile_rank   # 0-100, lower = better
r.affinity.allele            # best allele for this kind
r.presentation               # None if predictor doesn't produce it

Under the hood, each PeptideResult wraps a tuple of Prediction objects — frozen dataclasses, one per allele-kind combination. Everything converts to DataFrames with consistent column names.

Python API

Predicting peptides

from mhctools import NetMHCpan41

predictor = NetMHCpan41(alleles=["HLA-A*02:01", "HLA-B*07:02"])
results = predictor.predict(["SIINFEKL", "GILGFVFTL"])

r = results[0]
r.peptide                      # "SIINFEKL"
r.offset                       # position in source protein (if scanned)
r.kinds                        # {"pMHC_affinity", "pMHC_presentation"}
r.alleles                      # {"HLA-A*02:01", "HLA-B*07:02"}

# best prediction by kind — None when the kind is absent
r.affinity                     # Prediction or None
r.presentation                 # Prediction or None
r.stability                    # None (predictor doesn't produce it)

if r.affinity:
    r.affinity.value            # IC50 in nM
    r.affinity.percentile_rank  # 0-100, lower = better
    r.affinity.score            # ~0-1, higher = better
    r.affinity.allele           # best allele for this kind

# by rank instead of score
r.best_affinity_by_rank        # Prediction with lowest percentile rank, or None

# all predictions
r.preds                        # tuple of all Prediction objects
r.filter(kind="pMHC_affinity")
r.filter(allele="HLA-A*02:01")

NetMHCpan 4.1 automatically emits both pMHC_affinity and pMHC_presentation predictions per peptide-allele pair.

Scanning proteins

predict_proteins() takes a dictionary of protein sequences and returns {sequence_name: list[PeptideResult]}:

proteins = predictor.predict_proteins(
    {"TP53": "MEEPQSDPSVEPPLSQETFS...", "KRAS": "MTEYKLVVVGAGGVGKS..."},
    peptide_lengths=[9, 10],
)

for r in proteins["TP53"]:
    if r.affinity and r.affinity.value < 500:
        print(f"  offset={r.offset} {r.peptide} IC50={r.affinity.value:.0f}")

DataFrames

Every level has a _dataframe variant that flattens to a pandas DataFrame with consistent columns:

df = predictor.predict_dataframe(["SIINFEKL"], sample_name="pat001")
df = predictor.predict_proteins_dataframe({"TP53": "MEEPQ..."}, sample_name="pat001")

Columns: sample_name, peptide, n_flank, c_flank, source_sequence_name, offset, predictor_name, predictor_version, allele, kind, score, value, percentile_rank.

Multi-sample predictions

MultiSample runs a predictor across multiple samples, each with its own HLA genotype:

from mhctools import MultiSample, NetMHCpan41

ms = MultiSample(
    samples={
        "pat001": ["HLA-A*02:01", "HLA-B*07:02"],
        "pat002": ["HLA-A*01:01", "HLA-B*08:01"],
    },
    predictor_class=NetMHCpan41,
)

# {sample_name: list[PeptideResult]}
results = ms.predict(["SIINFEKL", "GILGFVFTL"])

# {sample_name: {seq_name: list[PeptideResult]}}
protein_results = ms.predict_proteins({"TP53": "MEEPQ..."})

# flat DataFrames with sample_name column
df = ms.predict_dataframe(["SIINFEKL"])
df = ms.predict_proteins_dataframe({"TP53": "MEEPQ..."})

Measurement kinds and MHC context

Each Prediction has a kind string describing what it measures:

The canonical prediction kind strings are defined in mhctools.pred.Kind.

Kind	Meaning
pMHC_affinity	Peptide-MHC binding affinity
pMHC_presentation	Likelihood of surface presentation (EL/processing)
pMHC_stability	Peptide-MHC complex stability
pMHC_TCR_binding	TCR recognition of a peptide-MHC (pMHC:TCR binding)
immunogenicity	T-cell immunogenicity
antigen_processing	Combined processing score
proteasome_cleavage	Proteasomal (MHC-I, cytosolic) C-terminal cleavage score
endolysosomal_cleavage	Endolysosomal (MHC-II, cathepsin) C-terminal cleavage score
tap_transport	TAP transport score (reserved, not yet used)
erap_trimming	ERAP trimming score (reserved, not yet used)

Predictors also expose kind_support() so downstream code can tell what MHC context is meaningful for each emitted kind:

support = predictor.kind_support()
support["pMHC_affinity"]
# {"mhc_dependence": "single_allele", "mhc_class": "I"}

mhc_dependence is one of:

Value	Meaning
none	The prediction is MHC-independent; Prediction.allele is empty.
single_allele	The prediction is for one peptide/MHC allele pair; Prediction.allele is part of the key.
haplotype	The prediction uses the requested MHC repertoire jointly; Prediction.allele may carry best-allele attribution but is not the prediction key.

mhc_class is one of none, I, II, or both.

The allowed metadata values are defined in mhctools.pred as MHC_DEPENDENCE_VALUES and MHC_CLASS_VALUES.

Examples:

Predictor	Kind	mhc_dependence	mhc_class
NetMHCpan41	pMHC_affinity	single_allele	I
NetMHCpan41	pMHC_presentation	single_allele	I
NetMHCIIpan4_EL	pMHC_presentation	single_allele	II
NetMHCstabpan	pMHC_stability	single_allele	I
MHCflurry	pMHC_affinity	single_allele	I
MHCflurry haplotype mode	pMHC_presentation	haplotype	I
MHCflurry per-allele panel mode	pMHC_presentation	single_allele	I
Pepsickle	proteasome_cleavage	none	none
NetCleave_I	proteasome_cleavage	none	I
NetCleave_II	endolysosomal_cleavage	none	II
NetTCR	pMHC_TCR_binding	none	I

For MHCflurry presentation, presentation_allele_mode="haplotype" treats the requested alleles as one sample genotype and emits one pMHC_presentation record per peptide. The allele field carries MHCflurry's best_allele attribution when available. presentation_allele_mode="per_allele" treats each allele as a separate one-allele synthetic sample and emits one presentation record per peptide/allele pair. The default "auto" mode uses haplotype mode for up to six alleles and per-allele mode for larger allele panels.

The Prediction object

Every prediction is a frozen, self-contained Prediction dataclass:

from mhctools import Prediction

pred = Prediction(
    kind="pMHC_affinity",
    score=0.85,           # ~0-1, higher = better
    peptide="SIINFEKL",
    allele="HLA-A*02:01",
    value=120.5,          # IC50 in nM
    percentile_rank=0.8,
    source_sequence_name="TP53",
    offset=42,
    predictor_name="netMHCpan",
    predictor_version="4.1",
)

score is always higher-is-better. value is in native units (nM for affinity, hours for stability). percentile_rank is always optional, 0-100, lower = stronger.

Supported predictors

MHC binding & presentation

Predictor	Kinds produced	Requires
NetMHCpan / NetMHCpan41 / NetMHCpan42	affinity + presentation	NetMHCpan
NetMHCpan4	affinity or presentation	NetMHCpan 4.0
NetMHCpan3 / NetMHCpan28	affinity	older NetMHCpan
NetMHC / NetMHC3 / NetMHC4	affinity	NetMHC
NetMHCIIpan / NetMHCIIpan43	affinity or presentation	NetMHCIIpan
NetMHCcons	affinity	NetMHCcons
NetMHCstabpan	stability	NetMHCstabpan
MHCflurry	affinity + presentation	pip install mhcflurry + mhcflurry-downloads fetch
MHCflurry_Affinity	affinity	pip install mhcflurry + mhcflurry-downloads fetch
BigMHC	presentation or immunogenicity	BigMHC clone (set BIGMHC_DIR)
MixMHCpred	presentation	MixMHCpred
IedbNetMHCpan / IedbSMM / IedbNetMHCIIpan	affinity	IEDB web API
RandomBindingPredictor	affinity	(built-in)

Antigen processing

Predictor	Kinds produced	Requires
Pepsickle	proteasome cleavage	pip install pepsickle (paper)
NetChop	proteasome cleavage	NetChop
NetCleave_I / NetCleave_II	proteasomal (I) / endolysosomal (II) C-terminal cleavage	NetCleave clone (set NETCLEAVE_DIR)

Pepsickle and NetChop use configurable scoring to aggregate per-position cleavage probabilities into peptide-level scores (see ProcessingPredictor and ProteasomePredictor).

NetCleave is different: it emits a single C-terminal cleavage score per peptide and covers both the MHC-I proteasomal (NetCleave_I → proteasome_cleavage) and MHC-II endolysosomal (NetCleave_II → endolysosomal_cleavage) pathways — MHC-II processing is otherwise a gap in the predictor set. It needs the residues downstream of the peptide to build the cleavage site, so pass c_flanks (or scan proteins). Its weights ship in the git repo; the R dependency in NetCleave's README is only for its training pipeline, not prediction.

from mhctools import NetCleave_II

predictor = NetCleave_II()                 # resolves NETCLEAVE_DIR / ~/NetCleave
# score peptides with their C-terminal flanking residues (>= 3)
results = predictor.predict(["SIINFEKL"], c_flanks=["DGH"])
results[0].endolysosomal_cleavage.score

# or scan a protein so each peptide is scored in real context
by_protein = predictor.predict_proteins({"TP53": "MEEPQ..."}, peptide_lengths=[15])

⚠️ NetCleave's own paper reports class-II C-terminal cleavage is a much weaker signal than class I (AUC ~0.66 vs ~0.91). Treat endolysosomal_cleavage scores accordingly.

TCR specificity

Predictor	Kinds produced	Requires
NetTCR	pMHC:TCR binding	NetTCR-2.2 clone (set NETTCR_DIR) + a TFLite runtime (pip install mhctools[nettcr])

NetTCR predicts whether a paired αβ T-cell receptor recognises a (class-I) peptide. Unlike the MHC-ligand predictors, its input is a peptide plus a TCR (the six CDR loops), not an allele, and it emits the pMHC_TCR_binding kind. NetTCR ships its pretrained weights in its git repository as small TFLite models; this wrapper runs the pan cross-validation ensemble in-process and does not need NetTCR's conda environment.

from mhctools import NetTCR, TCR

predictor = NetTCR()   # resolves NETTCR_DIR / ~/NetTCR-2.2
tcr = TCR(
    cdr1a="NSASQS", cdr2a="VYSSG", cdr3a="VVEGDKVI",
    cdr1b="MGHRA", cdr2b="YSYEKL", cdr3b="ASSHSGYEQF", name="clone1")

# Score explicit (peptide, TCR) pairs...
results = predictor.predict_pairs([("LLWNGPMAV", tcr)])
results[0].tcr_binding.score        # ensemble-mean recognition probability

# ...or every peptide x TCR combination.
results = predictor.predict(["LLWNGPMAV", "GILGFVFTL"], [tcr])

Commandline examples

Prediction for user-supplied peptide sequences

mhctools --sequence SIINFEKL SIINFEKLQ --mhc-predictor netmhc --mhc-alleles A0201

Automatically extract peptides as subsequences of specified length

mhctools --sequence AAAQQQSIINFEKL --extract-subsequences --mhc-peptide-lengths 8-10 --mhc-predictor mhcflurry --mhc-alleles A0201

Legacy API

The old predict_peptides() and predict_subsequences() methods still work and return BindingPredictionCollection objects:

predictor = NetMHCpan(alleles=["A*02:01"])
collection = predictor.predict_subsequences(
    {"1L2Y": "NLYIQWLKDGGPSSGRPPPS"},
    peptide_lengths=[9],
)
df = collection.to_dataframe()

for bp in collection:
    if bp.affinity < 100:
        print("Strong binder: %s" % bp)

To convert legacy results to the new types:

preds = collection.to_preds()           # list of Prediction
pp_list = collection.to_peptide_preds() # list of PeptideResult