Yu Shan Zhao

The paper develops a large language model to predict SARS-CoV-2-specific TCRs and shows their correlation with antibody levels post-vaccination.

The paper constructs a single-cell GEX-TCR reference atlas and introduces TCR-DeepInsight, a deep-learning framework to characterize disease-associated TCRs.

The paper introduces tFold-TCR, a model for predicting TCR-pMHC structures, achieving higher accuracy and speed than existing methods and creating a large database of these structures.

The paper benchmarks TCR reconstruction methods from scRNA-seq data, finding TRUST4 performs best, while sequencing depth limits accuracy.

This paper introduces tFold-Ab and tFold-Ag for fast and accurate prediction of antibody and antibody-antigen complex structures, showing improved speed and performance over AlphaFold-Multimer.

This paper introduces PALM, a pre-trained generative large language model for de novo antibody CDRH3 design, and A2binder for predicting antigen-antibody binding, showing effectiveness against SARS-CoV-2 variants.