Protein function has commonly been assessed by mutational analysis, but this approach traditionally has been limited to no more than hundreds of mutants of a single protein. By combining a library of protein mutants presented in a protein display format, a selection for protein activity, and high throughput DNA sequencing, we can now analyze hundreds of thousands of mutants of a protein in a single experiment. This approach, termed "deep mutational scanning," is based on the principle that the number of times the DNA encoding a given unique mutant is sequenced corresponds to the abundance of this DNA within a library. Thus, mutants that have enhanced function increase in abundance after selection and are sequenced more often. Mutants that have diminished function decrease in abundance and are sequenced less often or not at all.

Deep mutational scanning can be applied to many protein activities. We would be interested in collaborations that seek to apply this approach to biochemical activities other than protein binding and to in vivo assessments of protein properties. We would also like to work on novel computational approaches that use these high throughput DNA data sets to model such aspects of protein function as catalysis, interaction, stability, folding, and inhibition.