Protein engineering through machine-learning-guided directed evolution enables the
optimization of protein functions. Machine-learning approaches predict how sequence maps …

Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and
powerful computational resources, impacting many fields, including protein structural …

Protein engineering has enormous academic and industrial potential. However, it is limited
by the lack of experimental assays that are consistent with the design goal and sufficiently …

Engineered proteins offer the potential to solve many problems in biomedicine, energy, and
materials science, but creating designs that succeed is difficult in practice. A significant …

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

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs)
are tremendous, the design and discovery of new candidates remain a time and cost …

The functions of proteins and RNAs are defined by the collective interactions of many
residues, and yet most statistical models of biological sequences consider sites nearly …

Understanding the relationship between amino acid sequence and protein function is a long-
standing challenge with far-reaching scientific and translational implications. State-of-the-art …

Successful biologics must satisfy multiple properties including activity and particular
physicochemical features that are globally defined as developability. These multiple …

Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …