Peter S. Kim

The paper uses protein language models to predict allosteric residues from single sequences without structural data, outperforming existing methods on benchmark sets.

This study uses the PMD immunofocusing technique to create RBD immunogens that induce broadly neutralizing antibodies against the conserved cryptic face of SARS-CoV-2.

This paper uses a structure-informed language model for unsupervised antibody evolution, achieving significant improvements in neutralization and affinity against viral variants.

Consensus likelihoods from general, unsupervised protein language models evolve seven human antibodies using only a wildtype sequence and <=20 tested variants, improving binding affinity up to 160-fold.

This study shows human TMEM95 binds eggs, identifies a binding region via crystallography, and develops antibodies that impair sperm-egg fusion without blocking binding.

This paper identifies activated interstitial macrophages as a key target of SARS-CoV-2 infection in human lung tissue, showing their role in viral takeover, inflammation, and fibrosis.

This paper introduces evolutionary velocity, a method using protein language models to predict evolutionary order and direction, showing its effectiveness across different timescales and providing new insights into viral-host immune escape and horizontal gene transfer.

This study examines B cell clonal expansion and convergent antibody responses in SARS-CoV-2 patients, identifying shared features with SARS-CoV.