ImmunoStruct

A multimodal neural network framework for immunogenicity prediction from peptide-MHC sequence, structure, and biochemical properties

Citation

@article{givechian2024immunostruct,
  title={ImmunoStruct: Integration of protein sequence, structure, and biochemical properties for immunogenicity prediction and interpretation},
  author={Givechian, Kevin Bijan and Rocha, Joao Felipe and Yang, Edward and Liu, Chen and Greene, Kerrie and Ying, Rex and Caron, Etienne and Iwasaki, Akiko and Krishnaswamy, Smita},
  journal={bioRxiv},
  pages={2024--11},
  year={2024},
  publisher={Cold Spring Harbor Laboratory}
}

A multimodal network that integrates diverse information

A novel cancer-wildtype contrastive learning

Abstract

Epitope-based vaccines are promising therapeutic modalities for infectious diseases and cancer, but identifying immunogenic epitopes is challenging. The vast majority of prediction methods only use amino acid sequence information, and do not incorporate wide-scale structure data and biochemical properties across each peptide-MHC. We present ImmunoStruct, a deep-learning model that integrates sequence, structural, and biochemical information to predict multi-allele class-I peptide-MHC immunogenicity. By leveraging a multimodal dataset of $\sim$27,000 peptide-MHCs, we demonstrate that ImmunoStruct improves immunogenicity prediction performance and interpretability beyond existing methods, across infectious disease epitopes and cancer neoepitopes. We further show strong alignment with \textit{in vitro} assay results for a set of SARS-CoV-2 epitopes, as well as strong performance in peptide-MHC-based cancer patient survival prediction. Overall, this work also presents a new architecture that incorporates equivariant graph processing and multimodal data integration for the long standing task in immunotherapy.

Usage

Data preparation

Under the data/ folder, you should have the following files.

• cedar_data_final_with_mprop1_mprop2_v2.txt
• complete_score_Mprops_1_2_smoothed_sasa_v2.txt
• HLA_27_seqs_csv.csv

Besides, you should have the following folders.

• graph_pyg_Cancer
• graph_pyg_IEDB

These PyG graph files can be generated using immunostruct/preprocessing/cancer_graph_construction_new_KBG.py from the corresponding AlphaFold folders.

Training and testing

You first need to login to your wandb account on the website: https://wandb.ai/home, and create a project with the name matching the wandb project string in the corresponding training file (look for wandb.init).

Then, you can run the training scripts with your wandb username (YOUR_WANDB_USERNAME).

Experiments on IEDB infectious diseases.

TO BE UPDATED!!!!

python train_PropIEDB_PropCancer_ImmunoCancer.py --full-sequence --sequence-loss --model HybridModelv2 --wandb-username $YOUR_WANDB_USERNAME
python train_PropIEDB_PropCancer_ImmunoCancer.py --full-sequence --sequence-loss --model HybridModel --wandb-username $YOUR_WANDB_USERNAME
python train_PropIEDB_PropCancer_ImmunoCancer.py --full-sequence --sequence-loss --model SequenceFpModel --wandb-username $YOUR_WANDB_USERNAME
python train_PropIEDB_PropCancer_ImmunoCancer.py --full-sequence --sequence-loss --model SequenceModel --wandb-username $YOUR_WANDB_USERNAME
python train_PropIEDB_PropCancer_ImmunoCancer.py --full-sequence --model StructureModel --wandb-username $YOUR_WANDB_USERNAME

Experiments on human cancer neoepitopes.

TO BE UPDATED!!!!

Environment

conda create --name immuno python=3.10 -c anaconda -c conda-forge
conda activate immuno
conda install cudatoolkit=11.2 wandb pydantic -c conda-forge
conda install scikit-image pillow matplotlib seaborn tqdm -c anaconda
python -m pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118
python -m pip install dgl -f https://data.dgl.ai/wheels/torch-2.1/cu118/repo.html
python -m pip install torchdata==0.7.1
python -m pip install torch-scatter==2.1.2+pt21cu118 torch-sparse==0.6.18+pt21cu118 torch-cluster==1.6.3+pt21cu118 torch-spline-conv==1.2.2+pt21cu118 torch_geometric==2.5.3 numpy==1.26.3 -f https://data.pyg.org/whl/torch-2.1.2+cu118.html
python -m pip install graphein[extras]
python -m pip install lifelines

python -m pip install -U phate
python -m pip install multiscale-phate

Debug

1. ImportError: $some_path/libstdc++.so.6: version GLIBCXX_3.4.29' not found.You can try adding your immuno conda environment path toLD_LIBRARY_PATH`.

   export LD_LIBRARY_PATH=$PATH_TO_CONDA_ENV:$LD_LIBRARY_PATH
   

   In my case, it would be:

   export LD_LIBRARY_PATH=/home/cl2482/.conda/envs/immuno/lib:$LD_LIBRARY_PATH