Dana S. Matthews

This study evaluates ML architectures for protein fitness prediction using simulated and empirical landscapes, identifying landscape ruggedness as a key determinant of accuracy.

This study uses ancestral sequence reconstruction to explore PET-degrading cutinase sequence space, revealing diverse evolutionary pathways and the effectiveness of LASE in mapping fitness landscapes.

The paper introduces a graph-based measure of protein fitness landscape ruggedness and shows it concentrates at regions where dominant constraints change.

This paper introduces LASE, a method using ancestral sequence reconstruction to train protein language models with higher accuracy and faster computation than large models.