DOI: 10.5061/dryad.s7h44j1k7
title: T-SCAPE: T-cell immunogenicity scoring via cross-domain aided predictive engine

description: T-cell immunogenicity is a critical determinant of safety and efficacy for
 protein therapeutics and vaccines, but prediction is hampered by data
 scarcity. We present T-SCAPE, a multi-domain deep learning framework that
 uses adversarial domain adaptation to integrate diverse immunologically
 relevant data sources, including MHC presentation, peptide-MHC binding
 affinity, TCR-pMHC interaction, source organism information, and T-cell
 activation. Validated through rigorous leakage-controlled benchmarks,
 T-SCAPE shows exceptional performance in predicting T-cell activation for
 specific peptide-MHC pairs. Remarkably, it also accurately predicts the
 anti-drug antibody-inducing potential of therapeutic antibodies without
 MHC inputs, a success attributed to its biologically grounded pretraining.
 Confirmed by extensive case studies and ablation studies, T-SCAPE’s
 flexible architecture also supports broader tasks like molecular binding
 prediction. Its robust performance highlights its potential to advance the
 development of safer and more effective biologics.
description: # T-SCAPE: T-cell immunogenicity scoring via cross-domain aided predictive
 engine **Dataset DOI:** 10.5061/dryad.s7h44j1k7 ## 1. Description of the
 Dataset This dataset serves as the official training, validation, and
 benchmarking repository for **TITANiAN (T-SCAPE)**, a deep learning model
 designed to predict T-cell immunogenicity. The data compiles T-cell
 receptor (TCR) sequences, epitope sequences, and MHC alleles from multiple
 public immunology databases. This repository contains all data files used
 for training and benchmarking. The source code is hosted separately due to
 licensing requirements (see Section 4). ## 2. File Structure and Contents
 The dataset is organized into three zipped archives. ### A. train.zip
 Contains the primary datasets used for model training. *
 `TITANiAN_pretrain_train.csv`: Dataset used for self-supervised
 pre-training. * `TITANiAN_finetune_train.csv`: Dataset used for supervised
 fine-tuning. ### B. valid.zip Contains datasets used for model validation.
 * `TITANiAN_pretrain_valid.csv`: Validation set for the pre-training
 phase. * `TITANiAN_finetune_valid.csv`: Validation set for the fine-tuning
 phase. ### C. test.zip (Benchmark Datasets) Contains independent test sets
 used to benchmark TITANiAN. * `TITANiAN_IM_*.csv`: Immunogenicity
 benchmarks (ADA, Neoantigen, Infectious diseases). * `TITANiAN_MHC_*.csv`:
 MHC binding affinity benchmarks (Class I and II). * `TITANiAN_TCR_*.csv`:
 TCR specificity benchmarks (Activation, Zero-shot). ## 3. Variable
 (Column) Information The following tables describe all variables (column
 headers) found across the CSV files in this dataset. ### 3.1. Common
 Identifiers and Biological Sequences | Column Header | Description | Data
 Type | | :----------------------------- |
 :-------------------------------------------------------------------- |
 :---------------- | | `peptide` / `Epitope` | Amino acid sequence of the
 target antigen/epitope. | String (IUPAC AA) | | `Peptide length` | Length
 of the peptide sequence. | Integer | | `CDR3b` | Amino acid sequence of
 the TCR Beta chain CDR3 region. | String (IUPAC AA) | | `mhc` / `Allele` |
 Major Histocompatibility Complex allele name (e.g., A0201). | String | |
 `pseudo` | Pseudo-sequence representation of the MHC allele amino acid
 sequence. | String (IUPAC AA) | | `Antibody` | Name/ID of the antibody
 (specific to TITANiAN\_IM\_ADA.csv). | String | | `VH` | Variable Heavy
 chain amino acid sequence (Antibody). | String (IUPAC AA) | | `VL` |
 Variable Light chain amino acid sequence (Antibody). | String (IUPAC AA) |
 | `Reference` / `IEDB reference` | Literature or Database reference ID
 (e.g., DOI or IEDB ID). | String | ### 3.2. Target Labels and Experimental
 Measurements | Column Header | Description | Interpretation Key / Unit | |
 :----------------- |
 :--------------------------------------------------------- |
 :------------------------------ | | `label` | Binary classification label
 for immunogenicity or binding. | **0**: Negative**1**: Positive | | `task`
 | Identifier for the specific sub-task or data partition. | Integer ID | |
 `Measurement type` | Type of experimental assay used. | `binary`, `ic50` |
 | `Immunogenicity` | Measured Anti-Drug Antibody (ADA) levels. |
 **0-100**: Normalized ADA value | ### 3.3. Model Predictions (TITANiAN
 & Benchmarks) | Column Header / Pattern | Description | Interpretation
 Key | | :------------------------------------ |
 :------------------------------------------------------------- |
 :----------------------------------------------------- | | `TITANiAN-*` |
 Prediction score generated by the TITANiAN model. | **0.0 - 1.0**:
 Probability (Higher = More Immunogenic) | | `TITANiAN-*-baseline` |
 Prediction score from the baseline version of our model. | **0.0 - 1.0** |
 | `TITANiAN-*-ablation_...` | Prediction scores from ablation studies. |
 **0.0 - 1.0** | | `NetMHCpan *NetMHCIIpan *` | Predictions from NetMHC
 family tools (Class I & II). | **Score / Rank** | | `SMM` /
 `SMM-align` / `SMMPMBEC` | Predictions from SMM-based tools. | **Score /
 IC50 / Rank** | | `NN-align` / `ANN *` | Predictions from Neural Network
 alignment tools. | **Score / Affinity** | | `MHCflurry *` | Predictions
 from MHCflurry. | **Affinity / Score** | | `MHCnuggets *` | Predictions
 from MHCnuggets. | **Score / Affinity** | | `PickPocket`, `ARB`,
 `Tepitope` | Predictions from older MHC binding tools. | **Score** | |
 `MixMHCpred-*`, `PRIME-*` | Predictions from MixMHCpred and PRIME tools. |
 **Score / Rank** | | `BigMHC_*`, `TransPHLA` | Predictions from BigMHC and
 TransPHLA. | **Score** | | `HLAthena_*` | Predictions from HLAthena. |
 **Score / Rank** | | `DLpTCR`, `ERGO2`, `PanPep`, `pMTnet` | Predictions
 from TCR-peptide binding tools. | **Score** | | `AbNatiV`, `T20`,
 `Z-score` | Antibody-specific immunogenicity scores. | **Score / Metric**
 | | `IgReconstruct`, `AbLSTM` | Antibody-specific prediction models. |
 **Score** | | `Hu-mAb`, `MG Score` | Humanization and humanness scores for
 antibodies. | **Score** | | `Germline content` | Percentage of germline
 sequence identity. | **Percentage / Score** | | `OASis identity (*)` |
 Sequence identity metrics from OASis (loose, strict, etc.). | **Score /
 Percentage** | | `Non-human MHCII binders (*)` | Predicted non-human MHCII
 binding sites (loose, strict, etc.). | **Count / Score** | ### 3.4.
 Metadata | Column Header | Description | | :------------ |
 :----------------------------------------------------- | | `Date` | Date
 of data entry or retrieval. | | `rand` | Random seed or randomization
 index used for splitting. | | `Species` | Source species (e.g., Human,
 Mouse). | ## 4. Access Information and Software * **GitHub Repository:**
 [https://github.com/seoklab/T-SCAPE](https://github.com/seoklab/T-SCAPE) *
 **Webserver:**
 [https://galaxy.seoklab.org/design/t-scape/](https://galaxy.seoklab.org/design/t-scape/) **Note on Code Availability:** The source code and model weights for T-SCAPE are available at the GitHub repository linked above. Due to a pending patent, the software is not included in this Dryad data package. ## 5. Data Sources Data was derived from the following public databases: IEDB, VDJdb, MCPAS-TCR, ImmuneCODE, TBAdb, 10X Genomics, OAS, Uniprot, PRIME, MHCBN, BigMHC, Biophi, Panpep