mhctools 3.30.0

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 / binding score
erap_trimming	ERAP1 N-terminal trimming score

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
MixMHC2pred	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
MHCflurry	antigen_processing	none	none
Pepsickle	proteasome_cleavage	none	none
NetCleave_I	proteasome_cleavage	none	I
NetCleave_II	endolysosomal_cleavage	none	II
DeepTAP	tap_transport	none	none
ERAMER	erap_trimming	none	I
NetTCR	pMHC_TCR_binding	none	I
Tulip	pMHC_TCR_binding	single_allele	I
BigMHC_IM	immunogenicity	single_allele	I
PRIME	immunogenicity	single_allele	I
DeepImmuno	immunogenicity	single_allele	I
Calis	immunogenicity	none	I

TCR predictors (NetTCR, Tulip)

NetTCR and Tulip predict pMHC:TCR binding — whether a paired αβ T-cell receptor (an mhctools.TCR, described by its CDR loops) recognises a peptide. Both take (peptide, TCR) inputs; Tulip additionally takes the presenting MHC allele.

from mhctools import Tulip, TCR

tcr = TCR(cdr3a="CAGASGNTGKLIF", cdr3b="CASSIRASYEQYF", name="clone1")
predictor = Tulip()                       # needs TULIP_HOME + TULIP_PYTHON
results = predictor.predict(["GILGFVFTL"], [tcr], mhc="HLA-A*02:01")
results[0].preds[0].score                 # higher = more likely binding

TULIP-TCR is GPLv3 and pinned to transformers==4.32.1; mhctools is Apache-2.0 and depends on neither torch nor transformers. The Tulip wrapper therefore vendors none of TULIP — it runs a user-provided checkout out-of-process, in an isolated interpreter, via TULIP's own predict.py. Set two things up first (see scripts/setup_tulip_env.sh, which does both):

• TULIP_HOME — a clone of TULIP-TCR (provides predict.py, src/, tokenizers, and the released model_weights/);
• TULIP_PYTHON — an isolated Python 3.11 interpreter with torch and transformers==4.32.1 (3.11 so tokenizers installs from a prebuilt wheel and needs no Rust toolchain).

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 + processing	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 (class I)	MixMHCpred
MixMHC2pred	presentation (class II)	MixMHC2pred release (has PWMdef/)
IedbNetMHCpan / IedbSMM / IedbNetMHCIIpan	affinity	IEDB web API
RandomBindingPredictor	affinity	(built-in)

MixMHC2pred is a pan-allele class-II presentation predictor and a strong complement to NetMHCIIpan (independently co-best in the Frontiers in Immunology 2024 class-II benchmark). It emits one pMHC_presentation prediction per (peptide, allele): score is the raw MixMHC2pred score (higher = better), percentile_rank is its %Rank (lower = better). It's academic / non-commercial licensed, so mhctools shells out to a user-provided install (download a release, not a bare clone — the release ships the PWMdef/ allele definitions). Alleles may be given in the usual spellings (HLA-DRB1*15:01) or MixMHC2pred's own (DRB1_15_01, DQA1_01_02__DQB1_06_02).

from mhctools import MixMHC2pred

predictor = MixMHC2pred(
    alleles=["HLA-DRB1*15:01", "HLA-DQA1*01:02-DQB1*06:02"],
    program_name="/path/to/MixMHC2pred_unix")   # MixMHC2pred on macOS
results = predictor.predict(["GELIGTLNAAKVPAD"])   # class-II length peptides
results[0].presentation.score

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.

TAP transport

Predictor	Kinds produced	Requires
DeepTAP	TAP transport (tap_transport)	DeepTAP clone (set DEEPTAP_HOME)

TAP (transporter associated with antigen processing) is the step that shuttles cytosolic peptides into the ER for MHC-I loading — a distinct part of the processing pathway from proteasomal cleavage, and otherwise a gap in the predictor set. DeepTAP is a BiGRU that scores each peptide once (allele-independent, like the cleavage predictors), emitting one tap_transport prediction per peptide with an empty allele. score is in 0-1 (higher = stronger TAP binding); in task_type="reg" mode the predicted affinity in nM is also surfaced as value (lower = stronger).

DeepTAP ships its weights in-repo and is Apache-2.0, but pins an old pytorch-lightning, so mhctools shells out to DeepTAP's own CLI in a user-provided checkout, run by a user-provided interpreter (the checkpoints load fine under modern Lightning too). Set DEEPTAP_HOME to the clone and, if the current interpreter lacks torch, DEEPTAP_PYTHON to one that has it.

from mhctools import DeepTAP

predictor = DeepTAP(task_type="cla")       # resolves DEEPTAP_HOME / ~/DeepTAP
results = predictor.predict(["SIINFEKL", "AEASAAAAY"])
results[1].tap_transport.score             # 0-1, higher = stronger TAP binding

⚠️ DeepTAP's evaluation is self-reported, and no independent TAP benchmark exists for any tool (true of the whole TAP field). Treat the score as a useful pathway signal for prioritization, not a validated oracle.

ERAP1 trimming

Predictor	Kinds produced	Requires
ERAMER	ERAP1 trimming (erap_trimming)	ERAMER clone with PWM.xlsx (set ERAMER_HOME) + openpyxl

ERAP1 trims the N-termini of 9–16mer precursor peptides in the ER down to the 8–10mers MHC-I presents — the step between TAP transport and MHC loading, and otherwise the last empty stage in the pathway. ERAMER scores a precursor by averaging a per-length position-weight-matrix specificity over each residue trimmed off as it is cut toward a target epitope length (allele-independent, one erap_trimming prediction per peptide; score roughly −1…1, higher = more likely trimmed).

ERAMER is GPLv3 and its PWM ships in a GPL-licensed PWM.xlsx, so mhctools vendors neither: this is a clean-room Python-3 reimplementation of the (Python-2.7) tool's trimming-cascade average that loads the PWM from a user-provided ERAMER checkout at runtime. Point at the clone with ERAMER_HOME.

from mhctools import ERAMER

predictor = ERAMER(epitope_length=8)       # resolves ERAMER_HOME / ~/ERAMER
results = predictor.predict(["GGGGGVVVVVVAAAEE"])   # a 9-16mer precursor
results[0].erap_trimming.score

⚠️ ERAMER's evaluation is self-reported and ERAP1 trimming is an intrinsically noisy signal; treat the score as a pathway prior, not a validated oracle.

Immunogenicity

Predictor	Kinds produced	Requires
Calis	immunogenicity	nothing — self-contained
BigMHC_IM	immunogenicity	BigMHC clone (set BIGMHC_DIR)
PRIME	immunogenicity	PRIME clone + MixMHCpred
DeepImmuno	immunogenicity	DeepImmuno clone (set DEEPIMMUNO_HOME)

Calis is the classic sequence-only IEDB class-I immunogenicity model (Calis et al. 2013): a fixed per-amino-acid log-enrichment scale weighted by per-position importance, with the anchor positions (P1/P2/C-terminus) masked out. It needs no external install and no downloaded weights — the ~30 published parameters (from the open-access CC-BY paper) are built in — so it is a fast, dependency-free, allele-independent baseline. It emits one immunogenicity prediction per peptide (empty allele); score > 0 leans immunogenic.

from mhctools import Calis

predictor = Calis()
results = predictor.predict(["GILGFVFTL", "NLVPMVATV"])
results[0].immunogenicity.score            # 0.30484 (higher = more immunogenic)

PRIME predicts CD8+ T-cell immunogenicity of class-I peptides by combining MHC-I binding (via MixMHCpred, which it calls internally) with a TCR-recognition propensity model. It emits one immunogenicity prediction per (peptide, allele): score is the PRIME score (higher = more immunogenic) and percentile_rank is the PRIME %Rank (lower = better). PRIME is academic / non-commercial licensed, so mhctools shells out to a user-provided install rather than vendoring it.

from mhctools import PRIME

predictor = PRIME(
    alleles=["HLA-A*02:01", "HLA-B*07:02"],
    program_name="PRIME",                    # or an absolute path
    mixmhcpred_path="/path/to/MixMHCpred")    # optional if MixMHCpred is on PATH
results = predictor.predict(["GILGFVFTL", "NLVPMVATV"])
results[0].immunogenicity.score

DeepImmuno predicts class-I CD8+ immunogenicity from the peptide and its HLA-A/B/C allele with a small CNN (Li et al. 2021). It scores 9- and 10-mers only and supports a fixed set of ~62 alleles, snapping anything else to the nearest it knows. It emits one immunogenicity prediction per (peptide, allele); score is in 0–1 (higher = more immunogenic). DeepImmuno ships its weights in-repo and is MIT-licensed, but its script loads them with an old Keras 2 / TensorFlow stack, so mhctools shells out to DeepImmuno's own CLI in a user-provided checkout. Point at the clone with DEEPIMMUNO_HOME, and set DEEPIMMUNO_PYTHON to an interpreter that has TensorFlow (with Keras 2, or newer TensorFlow plus the tf-keras shim — the wrapper sets TF_USE_LEGACY_KERAS=1 for the subprocess).

from mhctools import DeepImmuno

predictor = DeepImmuno(alleles=["HLA-A*02:01"])   # resolves DEEPIMMUNO_HOME / ~/DeepImmuno
results = predictor.predict(["NLVPMVATV", "GILGFVFTL"])
results[0].immunogenicity.score                   # 0.9568 (higher = more immunogenic)

⚠️ Every current CD8 immunogenicity predictor — PRIME, BigMHC_IM, and DeepImmuno included — ranks well in the characterized regime but generalizes poorly to truly novel neoepitopes; independent benchmarks put the field near AUC 0.5–0.65 on unseen tumor neoepitopes (ITSNdb ~0.52–0.60, ICERFIRE ~0.56, IMPROVE ~0.60). In the one neutral head-to-head that scored both (NeoaPred, Bioinformatics 2024), BigMHC_IM edged PRIME on cancer neoepitopes, while PRIME tends to do better on viral / infectious-disease epitopes — its training positives are mostly viral and cancer-testis antigens, with only ~129 (v1) / ~596 (v2) true immunogenic neoepitopes. PRIME's higher self-reported numbers are partly attributable to documented train/test overlap (IMPROVE flagged ~70% overlap with its evaluation set). Use these scores to prioritize, not as ground truth.

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

Annotate an existing table with predictor scores (predict-table)

Downstream evaluation workflows often start from an annotated benchmark table (with columns like sample_id, hit, peptide, and per-row genotype/allele info) and just need external predictor scores appended. mhctools predict-table reads a CSV, runs each requested predictor once, and appends one score column per predictor — choosing the best allele per row — while preserving every input column:

mhctools predict-table \
    --input benchmark.csv.bz2 \
    --peptide-column peptide \
    --alleles-column hla \
    --predictor netmhcpan42-ba:netmhcpan4.2.ba:affinity \
    --predictor netmhcpan42-el:netmhcpan4.2.el:score \
    --out benchmark.with_scores.csv.bz2

Each --predictor spec is NAME[:OUTPUT_COLUMN[:FIELD]], where FIELD is affinity, score, or percentile_rank (lower is better for affinity and percentile_rank; higher for score). Rows may hold several alleles per cell (whitespace-, comma-, or semicolon-separated); the best one per peptide is chosen and recorded in a <OUTPUT_COLUMN>_best_allele provenance column. Pass --predictor-info info.csv to also write a sidecar describing each column's score_field and higher_is_better.

The same thing from Python (I/O-free, works on any DataFrame):

from mhctools import annotate_table, AnnotationSpec, NetMHCpan42_BA

annotated = annotate_table(
    df,
    [AnnotationSpec(
        predictor=lambda alleles: NetMHCpan42_BA(alleles=alleles),
        output_column="netmhcpan4.2.ba",
        field="affinity")],
    peptide_column="peptide",
    allele_column="hla")

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