Sarah A. Fahlberg

This paper introduces RLXF, a framework that aligns protein language models with experimental functional goals, improving the generation of high-functioning protein variants.

This study evaluates how different neural network architectures extrapolate beyond training data in protein design, finding simpler models excel in local prediction while complex models explore broader sequence space.

This paper develops a deep learning framework to predict protein function from sequence using deep mutational scanning data and demonstrates its ability to design new proteins with improved properties.

This study uses machine learning to engineer acyl-ACP reductases for improved in vivo fatty alcohol production, achieving over twofold higher titers than natural enzymes.