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mhctools

mhctools is a Python library for running MHC binding, presentation, immunogenicity, and antigen-processing predictors. It provides a common interface to tools such as NetMHCpan and MHCflurry, with results you can inspect in Python or export as a pandas DataFrame.

Choose what to predict

Question Predictors
Class I binding affinity NetMHCpan or MHCflurry
Class I presentation NetMHCpan, MHCflurry, MixMHCpred, BigMHC (EL), CapHLA
Class II binding or presentation NetMHCIIpan; MixMHC2pred for presentation
Peptide-MHC complex stability NetMHCstabpan
Proteasomal cleavage Pepsickle or NetChop
Class II cleavage NetCleave (class II)
TAP transport DeepTAP
ERAP1 trimming ERAMER
T-cell immunogenicity Calis, PRIME, BigMHC (IM), DeepImmuno; TLimmuno2 for class II
Recognition by a specific TCR NetTCR, Tulip, MixTCRpred
Free-peptide half-life PeptiVerse, PlifePred2
Per-bond peptidase evidence Peptidase activity

The selection guide explains input, installation, and license constraints. Read each model's guide for its output and validation limits.

Quickstart

Install mhctools and download the MHCflurry model weights:

pip install mhctools
mhctools fetch mhcflurry
from mhctools import MHCflurry

predictor = MHCflurry(alleles=["HLA-A*02:01"])
results = predictor.predict(["SIINFEKL", "GILGFVFTL"])
df = predictor.predict_dataframe(["SIINFEKL", "GILGFVFTL"])

The getting started guide explains the results and shows how to use another predictor.

Start here

  • Getting started: install the library and make your first prediction.
  • User guide: supported predictors, inputs, and examples.
  • Recipes: scan proteins, run multiple samples, and annotate tables.
  • API reference: Python classes and functions.

Choose and evaluate models

For installation help, see troubleshooting.

Processing and vaccine analysis

The antigen-processing guide covers proteasomes, peptidases, transport, and trimming, with model recommendations and batch assessments. Related workflows include vaccine reports and assay-aware benchmarks.