Karson M Chrispens

The paper benchmarks AI models for predicting mutational effects and drug-target affinity using deep mutational scanning data, finding that current models fail to capture inhibitor-dependent fitness landscapes.

The paper uses deep mutational scanning to analyze MET kinase domain mutations and their resistance to 11 inhibitors, identifying resistance and sensitizing mutations and improving prediction with a protein language model.

The paper introduces MAGMA-seq, a technology for quantitative analysis of antibody sequence-function relationships, demonstrating its application in mapping antibody development pathways and identifying binding determinants.