Alexander Yermanos

The paper uses deep mutational learning to analyze serum antibody escape to SARS-CoV-2 variants, finding individualized escape profiles and conserved binding signatures.

This paper introduces CALM, a model that predicts antibody-antigen binding from sequences using contrastive learning, achieving 7% top-1 retrieval on a test set.

AntibodyForests quantifies inter- and intra-antibody repertoire evolution using protein language models and structure analysis.

The paper characterizes clonal dynamics of antibody-secreting cells in bone marrow following influenza infection, finding that IgG ASCs preferentially target nucleoprotein regardless of infection type.

This study shows that protein language model pseudolikelihoods correlate with B cell selection features like SHM and isotype usage, but not binding affinity, in vivo.

The paper introduces a bioinformatics pipeline to identify known and putative immune inhibitory receptors as therapeutic targets using machine learning and transcriptomics data.

The paper identifies and validates two novel human genomic sites for safe and predictable gene expression in therapeutic applications.

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.

This study examines how different computational methods affect the analysis of B cell evolution in murine antibody repertoire sequencing data post-viral infection.

immuneSIM simulates B- and T-cell receptor repertoires with tunable features to benchmark immunoinformatics tools.