Cédric R. Weber

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

The paper introduces immuneREF, a method to quantify immune repertoire similarity across health and disease, finding that healthy and diseased repertoires are more similar than previously thought.

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.

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.

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

Mammalian display of CRISPR-introduced trastuzumab mutant libraries trains a network that picks antigen binders out of a hundred-million-variant virtual library; 30 of 30 tested variants bound.

The paper identifies high-dimensional immunogenomic features that predict public and private antibody clones in humans and mice with 80% accuracy using machine learning.