Sarah A. Fahlberg

Functional alignment of protein language models via reinforcement learning

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

Neural network extrapolation to distant regions of the protein fitness landscape

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.

Neural networks to learn protein sequence-function relationships from deep mutational scanning data

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.

Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production

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.