883 citations · 2.1k across the 7 of their papers we have counts for
8 papers
Protein sequence design with deep generative models
Zachary Wu, Kadina E. Johnston, Frances H. Arnold +1
Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge…
Machine learning-assisted directed protein evolution with combinatorial libraries
Zachary Wu, S. B. Jennifer Kan, Russell D. Lewis +2
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate mac…
Machine learning-guided directed evolution for protein engineering
Kevin K. Yang, Zachary Wu, Frances H. Arnold
Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence…
Neutral genetic drift can aid functional protein evolution
Jesse D Bloom, Philip A Romero, Zhongyi Lu +1
BACKGROUND: Many of the mutations accumulated by naturally evolving proteins are neutral in the sense that they do not significantly alter a protein's ability to perform its primar…
Evolution favors protein mutational robustness in sufficiently large populations
Jesse D. Bloom, Zhongyi Lu, David Chen +3
BACKGROUND: An important question is whether evolution favors properties such as mutational robustness or evolvability that do not directly benefit any individual, but can influenc…
Why highly expressed proteins evolve slowly
D. Allan Drummond, Jesse D. Bloom, Christoph Adami +2
Much recent work has explored molecular and population-genetic constraints on the rate of protein sequence evolution. The best predictor of evolutionary rate is expression level, f…