Beichen Gao

Synthetic coevolution reveals adaptive mutational trajectories of neutralizing antibodies and SARS-CoV-2

This study uses synthetic coevolution to explore mutational trajectories between neutralizing antibodies and SARS-CoV-2 variants, revealing antagonistic and compensatory mutations.

Predicting TCR antigen specificity at proteome-scale with synthetic immune cells and machine learning

The paper presents a synthetic immune cell platform integrated with machine learning to predict TCR specificity to peptide-HLA antigens across the human proteome.

Predictive profiling of SARS-CoV-2 variants by deep mutational learning

This paper develops deep mutational learning to predict the impact of SARS-CoV-2 spike protein mutations on ACE2 binding and antibody escape, identifying potential variants and supporting vaccine and therapeutic development.