TLimmuno2

Transfer learning-based MHC-II immunogenicity prediction for CD4+ T cell epitopes

Input

Peptide Sequences

HLA assignment mode

HLA allele

Per-sequence HLA alleles

Include percentile ranking

30 credits

Output

Configure inputs to begin

Set options on the left, then click “Submit job” — or start from an example.

Single-HLA MHC-II peptide screen

Per-sequence HLA assignment

What is TLimmuno2?

TLimmuno2 predicts whether a peptide presented by an MHC class II molecule will trigger a CD4+ T cell immune response. Where most immunogenicity tools focus on MHC class I (CD8+ T cells), TLimmuno2 addresses the less-covered class II pathway, which is central to helper T cell activation, vaccine design, and cancer neoantigen identification.

The model uses transfer learning: an LSTM network is first trained on over 100,000 peptide-MHC binding affinity measurements, then fine-tuned on immunogenicity data. This two-stage approach compensates for the limited amount of experimentally validated immunogenicity data available for MHC-II epitopes.

How does TLimmuno2 work?

Two-stage prediction

TLimmuno2 runs two neural networks in sequence:

Binding affinity model (BAmodel): An LSTM trained on 107,008 binding measurements from NetMHCIIpan across 71 MHC-II molecules. Rather than using the final binding prediction, TLimmuno2 extracts intermediate features from this model's penultimate layer, capturing learned representations of peptide-MHC interaction patterns.
Immunogenicity model: A second LSTM that takes three inputs — the BLOSUM62-encoded peptide, the encoded MHC pseudosequence, and the binding affinity features from stage one — and predicts the probability that the complex will elicit a T cell response.

Sequence encoding

Both peptides and MHC pseudosequences are encoded using the BLOSUM62 substitution matrix, which captures biochemical similarity between amino acids. Peptides are padded to 21 residues and MHC pseudosequences to 34 residues, producing fixed-size matrices that the LSTM layers can process.

Percentile ranking

Raw immunogenicity scores lack context without a reference distribution. When percentile ranking is enabled, TLimmuno2 scores approximately 90,000 random human peptides (sampled across lengths 13–21) against the same HLA allele and reports where the query peptide falls in that distribution. A percentile rank of 0.95 means the peptide scores higher than 95% of background peptides for that allele.

How to use TLimmuno2 online

ProteinIQ provides cloud-hosted access to the native TLimmuno2 model and pseudosequence table. The current published dataset contains 5,640 allele keys spanning human DRB and HLA-DP entries plus mouse H-2 and bovine BoLA alleles. No installation or Python environment required.

Inputs

Input	Description
`Peptide Sequences`	One or more peptide sequences in FASTA format or one per line. TLimmuno2 accepts peptides from 9–21 amino acids.
`CSV/TSV upload`	Optional native two-column file with peptide in column 1 and HLA allele in column 2, matching the format of `Python/data/example.csv` in the original repository.

Settings

Setting	Description
`HLA assignment mode`	`Single HLA for all peptides` applies one allele to every input sequence. `One HLA per peptide` allows specifying a different allele for each sequence.
`HLA allele`	Used when assignment mode is set to single. Exact native keys work directly (for example `DRB1_0101`, `HLA-DPA10103-DPB10201`, `H-2-IAb`), and common aliases like `DRB101:01` or `DPA101:03-DPB1*02:01` are resolved automatically.
`Per-sequence HLA alleles`	One allele per line, matching the order of input peptides. Required when using per-sequence mode. The same exact-key and alias rules apply.
`Include percentile ranking`	Ranks each peptide against ~90,000 background peptides per HLA allele. This is the default and adds roughly 3 minutes per unique HLA allele.

Output columns

Column	Description
`pep`	Input peptide sequence.
`HLA`	Exact TLimmuno2 HLA key used for scoring.
`sequence`	native MHC pseudosequence associated with that HLA key.
`prediction`	Predicted probability of triggering a CD4+ T cell response (0–1). Higher scores indicate greater immunogenic potential.
`Rank`	Position relative to background peptides for the same HLA allele (0–1).

Interpreting results

The immunogenicity score is a continuous probability. A peptide scoring 0.8 is not necessarily four times as immunogenic as one scoring 0.2 — the score reflects model confidence, not magnitude of immune response.

Score range	Interpretation
> 0.7	Strong predicted immunogenicity
0.4–0.7	Moderate — experimental validation recommended
< 0.4	Low predicted immunogenicity

Rank provides additional context. A peptide with a moderate score of 0.5 but a high rank (> 0.9) is still notable — it scores above 90% of random peptides for that allele, suggesting genuine immunogenic potential even if the absolute score appears modest.

MHC class I vs class II

MHC class I molecules present intracellular peptides (typically 8–11 residues) to CD8+ cytotoxic T cells. MHC class II molecules present extracellular peptides (typically 13–25 residues) to CD4+ helper T cells. The two pathways involve different antigen processing machinery, binding groove geometry, and downstream immune effects.

TLimmuno2 specifically models the class II pathway. For MHC class I immunogenicity prediction, DeepImmuno covers 20 HLA-A/B/C alleles with a CNN-based approach.

Limitations

Predictions are for immunogenicity (T cell activation), not binding affinity alone. High binding does not guarantee immunogenicity, and the model accounts for factors beyond binding.
The MHC-II binding groove is open-ended, but the released TLimmuno2 model only accepts peptides up to 21 residues. Longer peptides must be trimmed before submission.
Percentile ranking is computationally expensive. Each unique HLA allele requires scoring ~90,000 background peptides through both models, adding several minutes per allele.
The model was trained primarily on human data. Mouse H-2 and bovine BoLA keys are present in the pseudosequence database, but the publication does not independently benchmark them.

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