mhctools 3.13.0

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

Each Prediction has a kind string describing what it measures:

Kind	Meaning
pMHC_affinity	Peptide-MHC binding affinity
pMHC_presentation	Likelihood of surface presentation (EL/processing)
pMHC_stability	Peptide-MHC complex stability
immunogenicity	T-cell immunogenicity
antigen_processing	Combined processing score
proteasome_cleavage	Proteasomal cleavage score
tap_transport	TAP transport score (reserved, not yet used)
erap_trimming	ERAP trimming score (reserved, not yet used)

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

Processing predictors use configurable scoring to aggregate per-position cleavage probabilities into peptide-level scores. See ProcessingPredictor and ProteasomePredictor for details.

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