The model predicts peptide presentation on the cell surface via a given HLA class I molecule.
It was introduced in the paper Identification of tumor-specific MHC ligands through improved biochemical isolation and incorporation of machine learning by Shima Mecklenbräuker, Piotr Skoczylas, Paweł Biernat, Badeel Zaghla, Bartłomiej Król-Józaga, Maciej Jasiński, Victor Murcia Pienkowski, Anna Sanecka-Duin, Oliver Popp, Rafał Szatanek, Philipp Mertins, Jan Kaczmarczyk, Agnieszka Blum, and Martin G. Klatt.
Model Details
The peptide-HLA (pHLA) presentation is a major mechanism by which our immune
system can recognize abnormal cells (e.g. altered by cancer or viral infections).
ARDisplay-I predicts whether a given peptide will be displayed on the cell surface
via a given HLA class I molecule. Such a presentation event enables
immunosurveillance, and if the antigen is recognized as non-self, this can trigger
an immune response.
The pHLA presentation itself is a complex multi-stage process composed of
antigen processing followed by its attachment to a particular HLA molecule and
the transportation of the whole pHLA complex to the cell surface. Within
each human cell, proteins are constantly degraded into short peptides or amino
acids. During this process, some protein fragments, typically 8-11 amino-acid
long, may bind to a specific HLA molecule and subsequently be transported to the
cell surface. The predictions from our model encompass the entire processing
and presentation pathway.
Please note, that in most application scenarios, the model requires additional
post-processing steps and appropriate filtering. Moreover, if your data is not
standard (ie. contains neoepitopes, peptides originating from alternative
splicing, virus epitopes, dark antigens, etc.), you might need additional
domain knowledge and/or a model fine-tuned to your needs. If necessary, feel free
to contact us for support.
The model was developed at Ardigen as part of Immunology
platform. Free access to the regular version is available via the Hugging Face platform
for non-commercial academic use only (see License). For commercial use and
the Pro model versions, we encourage you to contact us at ardisplay@ardigen.com.
The model takes peptide-HLA (pHLA) pairs as input and returns a presentation score in the
range between [0, 1]. It can be used to select peptides with the highest
probability of being presented by specific HLA molecules, find protein fragments
with a high presentation probability, find multiple HLAs presenting a given peptide,
or scan an entire protein for presented subsequences.
Limitations
Supports a pre-defined set of HLAs.
Does not work on peptides containing ambiguous amino acids, like X, or J.
Assumes short peptides on input, limited to between 8 and 11 amino acids.
Replaces selenocysteine (U) with cysteine (C) before running inference.
Metrics
Ardigen's ARDisplay-I with over 2 times higher Average Precision
Our model enables the prediction of HLA-I presented peptides with over 2 times higher Average Precision than the current state-of-the-art (solutions from netMHCpan and MHCflurry).
The study cohort includes the multiple myeloma cell lines JJN3 and LP-1 as well as the lymphoblastic leukemia cell line Nalm-6. Data consists of the MS results generated by Dr. Philipp Mertins, Martin Klatt, M.D., et al. and describes more than 32,000 HLA ligands presented on the cell surface of one of the three cell lines expressing together 17 distinct HLA class I alleles.
Comparison of precision-recall (PR) curves
Comparison of precision-recall (PR) curves.
Our model achieves higher results at each point of the
PR curve. The regions with standard deviation do not
overlap, which indicates a high statistical significance
of the performance difference between the methods.
Positive predictive values (PPV)
Positive predictive values (PPV) with four selected thresholds, i.e., top-10, 20, 50, & 100 pHLA
pairs selected by each method. For example, PPV (top 10)
is the expected fraction of presented pHLA pairs among
the top 10 pHLAs ranked by the respective model.
You can visit our interactive demo and try the model there.
Alternatively, you can run the model on your machine from Python as a CLI
tool by following the sections below.
from transformers import pipeline
pipe = pipeline(model="ardigen/ardisplay-i", trust_remote_code=True)
data = ["A01:02,AAAAAAAA", "A01:02,CCCCCCCCCC"]
result = pipe(data)
print(result)
The peptides passed to the model need to have a length between 8 and 11 AAs and cannot
contain ambiguous amino acid descriptors, like X, B, Z, J, etc.
CLI
You can also install the model as a CLI tool for usage in bioinformatics
pipelines with the following command (assuming you have python3 and pip
installed)
This will install the ardisplay-i-cli tool which takes a text file with a list
of HLA,peptide pairs and outputs a .csv file. See ardisplay-i-cli --help for
the details.
