We provide an overview of the main approaches to validate candidate epitopes experimentally.
What Tools Are Available to Predict Peptide-MHC Binding Affinity?
The Importance of pMHC Binding Affinity
The major histocompatibility complex (MHC) plays a pivotal role in immune defense by recognizing and binding to foreign peptides and presenting them to T cells. MHC class I molecules present peptides to CD8+ cytotoxic T cells, while MHC class II molecules present peptides to CD4+ helper T cells. The specificity and strength of these peptide-MHC (pMHC) interactions determine the efficacy of the resulting immune response.
Read more about the main factors contributing to peptide-MHC binding affinity.
Understanding and predicting pMHC binding affinity and stability is essential in the development of vaccines, cancer immunotherapy, and treatment for autoimmune diseases. Being able to identify peptides that bind strongly to the MHC complex ensures that peptides selected for the development of vaccines or immunotherapies will interact effectively with MHC molecules and elicit the desired immune response.
On the other hand, ensuring that therapeutic proteins do not induce an undesired immune response is critical for drug safety. Bioengineered proteins may contain neoepitopes that could be recognized as foreign by the immune system. Understanding which epitopes could be presented by MHC II molecules, leading to CD4+ T cell activation and the production of anti-drug antibodies, could be important to mitigate potential immunogenicity of new drugs.
Tools to Assess pMHC Binding Affinity
pMHC binding affinity and immunogenicity can be assessed experimentally but given the large variety of antigens and MHC alleles, testing all possible combinations in vitro is often not feasible. Therefore, numerous tools have been developed to facilitate this prediction.
Here’s an overview of the most popular tools for predicting pMHC binding affinity:
Tool |
Functionality |
Covered species |
Allele type(#of covered alleles) |
Input data format |
Web interface/download |
Ease of use |
Good to know |
| NetMHC 4.0 | Binding of peptides to MHC class I molecules (binding affinity, peptide ranking, binding level) |
Human | HLA-A HLA-B HLA-C HLA-E (81) |
Plain text format (FASTA or PEPTIDE) One letter amino acid code |
Web interface and download | User-friendly interface | Handles large data sets Works for peptides of any length Customizable, e.g., specify peptide length, set threshold for strong and weak binders |
| Mouse Primate Pig Cow |
H-2 Patr, Mamu, Gogo SLA BoLA (41) |
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| NetMHCpan 4.1 | Pan-specific binding of peptides to MHC class I molecules of known sequence (binding affinity, peptide ranking, binding level, binding score) |
Human | HLA-A HLA-B HLA-C HLA-E |
Plain text format (FASTA or PEPTIDE) One letter amino acid code |
Web interface and download | User-friendly interface | Works for peptides of any length Customizable, e.g., set threshold for strong and weak binders, include Eluted ligand likelihood |
| Mouse | H-2 | ||||||
| Primates | Patr, Mamu, Gogo | ||||||
| Pig | SLA | ||||||
| Cow | BoLA | ||||||
| Horse | Eqca | ||||||
| NetMHCII 2.3 | Binding of peptides to MHC class II molecules (binding affinity, binding level, binding score) |
Human | HLA-DR (25) HLA-DQ (20) HLA-DP (9) |
Plain text format (FASTA) One letter amino acid code |
Web interface and download | Straightforward interface | Customizable, e.g., set threshold value |
| Mouse | H-2 (7) | ||||||
| NetMHCIIpan 4.3 | Pan-specific binding of peptides to MHC class II molecules of known sequence (binding affinity, peptide ranking, binding level, binding score) |
Human | HLA-DR HLA-DQ HLA-DP |
Plain text format (FASTA or PEPTIDE) One letter amino acid code |
Web interface and download | Straightforward interface | Customizable, e.g., set threshold for strong and weak binders, include Eluted ligand likelihood |
| Mouse | H-2 | ||||||
| Cow | BoLA-DRB3 | ||||||
| SYFPEITHI | Motif-based epitope prediction for peptides binding to MHC class I and II molecules (method based on binding scores) |
Human | HLA-A HLA-B HLA-DR |
One letter amino acid code | Web interface and download | Moderately easy to use | Coverage of a variety of MHC class I and II alleles Estimated epitope prediction reliability for MHC class II approx. 50% |
| Mouse | H-2 | ||||||
| Rat | RT1 | ||||||
| MHCflurry 2.0 | Pan-allele binding of peptides to MHC class I molecules (binding affinity, peptide ranking, binding score) |
Human and other species | (14,000) | Command line arguments or CSV file | Download | Only for trained users, difficult to operate | Based on Python |
| IEDB (immune Epitope Database) | Suite of tools for prediction of binding affinities and scores for MHC class I and II, MHC calls I processing and MHC I immunogenicity prediction | Human | HLA-A HLA-B HLA-C HLA-E HLA-G HLA-DR HLA-DQ HLA-DP |
Plain text format (FASTA) | Web interface and download | Comprehensive, straightforward interface, but the large number of options might be overwhelming | Customizable: include Eluted ligand likelihood |
| Mouse | H-2 | ||||||
| Primate | Patr Mamu Gogo |
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| Pig | SLA | ||||||
| Cow | BoLA | ||||||
| Horse | Eqca | ||||||
| Dog | DLA |
The traditional NetMHC tools (a suite of tools available from DTU, Technical University of Denmark) rely heavily on experimental data, which can be challenging when experimental data is limited for certain alleles. In contrast, the newer NetMHCpan tools are trained on datasets to recognize features that are shared across different alleles, so they are able to generalize across different MHC alleles and not only rely on allele-specific data. These pan tools are often more effective for predicting pMHC binding of novel or rare alleles for which limited experimental data may be available.
At Immudex, we have been using NetMHC for many years to predict pMHC binding affinity before producing custom MHC Monomers or MHC Dextramer® reagents. We evaluate each unique peptide's ability to bind to the requested MHC allele(s) using in silico approaches.
How Do You Validate pMHC Binding?
Whatever tool you choose to predict pMHC binding, you will need to validate the epitope experimentally.
At Immudex we offer a pMHC Binding Screening Service which combines an accurate prediction with experimental validation to ensure reliable identification of strong binders. Find out how this service helps you assess the immunogenicity of your peptides.
Future Outlook for pMHC Binding Prediction Tools
The existing prediction tools and algorithms are being continually developed and with the increased use of Artificial Intelligence¹, new and improved pMHC binding prediction tools can be expected over the coming years.
For example, a new algorithm based on machine learning has recently been developed by Ardigen, called ARDisplay-I, which focuses not on pMHC binding, but rather on actual presentation of the peptides. ARDisplay-I predicts the probability of presentation of a given peptide on the cell surface by MHC class I molecules and was able to outperform both NetMHCpan v4.1 and MHCflurry².
References
¹. Borole, P., Rajan, A. Building trust in deep learning-based immune response predictors with interpretable explanations. Commun Biol 7, 279 (2024). https://doi.org/10.1038/s42003-024-05968-2
². Mecklenbräuker et al., Identification of tumor-specific MHC ligands through improved biochemical isolation and incorporation of machine learning. bioRxiv 2024. https://www.biorxiv.org/content/10.1101/2023.06.08.544182v1
Related Resources
Factors Contributing to Immunogenicity
What factors play a role in immunogenicity and pMHC complex binding affinity?
MHC Dextramer®
Explore our range of reagents for validating epitopes by pMHC multimer screening.
pMHC Binding Screening Service
We offer a service to assess the ability of your candidate peptides to bind to MHC alleles.
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