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Immunity-related

📘 Related resources on Github: TCR-BCR-seq-analysis, awesome-vdj

Title Pub.&Year Notes
[Survey] Development and use of machine learning algorithms in vaccine target selection npj vaccines '2024 T cell & B cell

TCR-HLA-peptide Binding Prediction

Intro: T-cell receptor (TCR), peptide, and human leukocyte antigen (HLA) interactions play a crucial role in the adaptive immune response, orchestrating the recognition and targeting of foreign substances, such as pathogens or cancer cells.

HLA-pep binding TCR-HLA-pep binding

HLA-Peptide interaction

Title Pub.&Year Notes
Structure of the complex between human T-cell receptor, viral peptide and HLA-A2 Nature '1996
[Survey] Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools Brief Bioinform '2011
Classification of Human Leukocyte Antigen (HLA) Supertypes Immunoinformatics '2014
Predicting Antigen Presentationwhat Could we Learn From a Million Peptides?
NetMHCpan NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence PLOS ONE '2007
NetMHCpan-4.0 NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data 2017
TransPHLA A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design NMI '2022 code
HLAncPred HLAncPred: a method for predicting promiscuous non-classical HLA binding sites Bioinformatics '2022
Interpreting BERT architecture predictions for peptide presentation by MHC class I proteins
Improved Predictions of MHC-Peptide Binding using Protein Language Models
Peptide-binding specificity prediction using fine-tuned protein structure prediction networks PNAS '2023

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TCR-HLA-peptide interaction

Title Pub.&Year Notes
ImRex Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification
TCRex Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires
DeepTCR DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires
DeepTCR2 Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy
ERGO-II Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction
TCRAI-ICON A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity Science Advanced '2021
pMTnet Deep learning-based prediction of the T cell receptor–antigen binding specificity NMI '2021
PanPep Pan-Peptide Meta Learning for T-cell receptor–antigen binding recognition NMI '2023 👍
TEIM Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning NMI '2023
[Benchmark] Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report ImmunoInformatics '2023
[survey] Can we predict T cell specificity with digital biology and machine learning? Nature reviews '2023
TCRdock Structure-based prediction of T cell receptor: peptide-MHC interactions Elife '2023
TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning Nucleic Acids Research '2023
pMTnet_Omni
[benchmark] Lessons learned from the IMMREP23 TCR-epitope prediction challenge ImmunoInformatics '2024 Kaggle
PISTE Sliding-attention transformer neural architecture for predicting T cell receptor–antigen–human leucocyte antigen binding NMI '2024

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Antibody

Title Pub.&Year Notes
Efficient evolution of human antibodies from general protein language models NBT '2023
PALM-H3 De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model NC '2024
[benchmerk] What has AlphaFold3 learned about antibody and nanobody docking, and what remains unsolved? BioRxiv '2024

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