Novel and improved biocatalysts are increasingly sourced from libraries via experimental
screening. The success of such campaigns is crucially dependent on the number of …

Evolvability is the ability of a biological system to produce phenotypic variation that is both
heritable and adaptive. It has long been the subject of anecdotal observations and …

Abstract Machine learning-based models of protein fitness typically learn from either
unlabeled, evolutionarily related sequences or variant sequences with experimentally …

Predicting the effects of mutations in proteins is critical to many applications, from
understanding genetic disease to designing novel proteins to address our most pressing …

The effective design of combinatorial libraries to balance fitness and diversity facilitates the
engineering of useful enzyme functions, particularly those that are poorly characterized or …

Prediction methods inputting embeddings from protein language models have reached or
even surpassed state-of-the-art performance on many protein prediction tasks. In natural …

Microbial consortia exhibit complex functional properties in contexts ranging from soils to
bioreactors to human hosts. Understanding how community composition determines …

To reduce experimental effort associated with directed protein evolution and to explore the
sequence space encoded by mutating multiple positions simultaneously, we incorporate …

Protein engineering often targets binding pockets or active sites which are enriched in
epistasis—nonadditive interactions between amino acid substitutions—and where the …

Generative modeling for protein engineering is key to solving fundamental problems in
synthetic biology, medicine, and material science. We pose protein engineering as an …