mhctools 3.0.1

Python interface to running command-line and web-based MHC binding predictors

mhctools

Python interface to running command-line and web-based MHC binding 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 for specific peptides
results = predictor.predict(["SIINFEKL", "GILGFVFTL"])

# results is a list of PeptidePreds — one per peptide
for pp in results:
    best = pp.best_affinity
    if best:
        print(f"{best.peptide} -> {best.allele} IC50={best.value:.1f}nM")

Python API

Predicting peptides

predict() takes a list of peptide sequences and returns a list[PeptidePreds]. Each PeptidePreds contains Pred objects for every allele and measurement kind the predictor supports.

from mhctools import NetMHCpan41

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

pp = results[0]
pp.best_affinity                # Pred with highest affinity score
pp.best_affinity.allele         # "HLA-A*02:01"
pp.best_affinity.value          # IC50 in nM
pp.best_affinity.score          # higher = better (~0-1)
pp.best_affinity.percentile_rank  # lower = better (0-100)

pp.best_affinity_by_rank        # Pred with lowest percentile rank
pp.best_presentation            # best EL/presentation score
pp.best_presentation_by_rank    # best EL percentile rank
pp.best_stability               # best pMHC stability (if available)
pp.best_stability_by_rank

# filter by kind or allele
pp.filter(kind=Kind.pMHC_affinity)
pp.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[PeptidePreds]}:

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

for pp in proteins["TP53"]:
    best = pp.best_affinity
    if best and best.value < 500:
        print(f"  offset={best.offset} {best.peptide} IC50={best.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[PeptidePreds]}
results = ms.predict(["SIINFEKL", "GILGFVFTL"])

# {sample_name: {seq_name: list[PeptidePreds]}}
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

The Kind enum describes what biological quantity a Pred measures:

Kind	Meaning
pMHC_affinity	Peptide-MHC binding affinity
pMHC_presentation	Likelihood of surface presentation (EL)
pMHC_stability	Peptide-MHC complex stability
cellular_presentation	Cross-allele presentation (e.g. MHCflurry)
antigen_processing	Combined processing score
proteasome_cleavage	Proteasomal cleavage score
tap_transport	TAP transport score
erap_trimming	ERAP trimming score

The Pred object

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

from mhctools import Pred, Kind

pred = Pred(
    kind=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

Predictor	Kinds produced	Requires
NetMHCpan / NetMHCpan41	affinity + presentation	NetMHCpan
NetMHCpan4	affinity or presentation	NetMHCpan 4.0
NetMHCpan3 / NetMHCpan28	affinity	older NetMHCpan
NetMHC / NetMHC3 / NetMHC4	affinity	NetMHC
NetMHCIIpan	affinity or presentation	NetMHCIIpan
NetMHCcons	affinity	NetMHCcons
NetMHCstabpan	stability	NetMHCstabpan
MHCflurry	affinity	pip install mhcflurry + mhcflurry-downloads fetch
MixMHCpred	presentation	MixMHCpred
RandomBindingPredictor	affinity	(built-in)
NetChop	cleavage	NetChop

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 Pred
pp_list = collection.to_peptide_preds() # list of PeptidePreds