F. Lund-Johansen

Titers of antibodies the receptor-binding domain (RBD) of ancestral SARS-CoV-2 are predictive for levels of neutralizing antibodies to multiple variants

This study shows that anti-RBDwt titers correlate with neutralizing antibody levels against multiple SARS-CoV-2 variants and proposes a method to improve immunomonitoring precision and throughput.

Beyond Spike: Identification of nine highly prevalent SARS-CoV-2-specific CD8 T-cell epitopes in a large Norwegian cohort

The paper identifies nine prevalent SARS-CoV-2-specific CD8 T-cell epitopes in a Norwegian cohort, showing strong correlation between immunoprevalence and immunodominance.

In silico proof of principle of machine learning-based antibody design at unconstrained scale

This paper demonstrates that deep generative models can design high-affinity, developable antibodies at scale using synthetic data and transfer learning.

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for real-world antibody specificity prediction

The paper introduces Absolut!, a software suite generating synthetic antibody-antigen structures to benchmark machine learning methods for antibody specificity prediction.

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

The paper identifies a compact vocabulary of paratope-epitope interaction motifs that enables machine learning prediction of antibody-antigen binding.