A Sort-Seq Approach to the Development of Single Fluorescent Protein Biosensors

Motivated
by the growing importance of single fluorescent protein
biosensors (SFPBs) in biological research and the difficulty in rationally
engineering these tools, we sought to increase the rate at which SFPB
designs can be optimized. SFPBs generally consist of three components:
a circularly permuted fluorescent protein, a ligand-binding domain,
and linkers connecting the two domains. In the absence of predictive
methods for biosensor engineering, most designs combining these three
components will fail to produce allosteric coupling between ligand
binding and fluorescence emission. While methods to construct diverse
libraries with variation in the site of GFP insertion and linker sequences
have been developed, the remaining bottleneck is the ability to test
these libraries for functional biosensors. We address this challenge
by applying a massively parallel assay termed “sort-seq,”
which combines binned fluorescence-activated cell sorting, next-generation
sequencing, and maximum likelihood estimation to quantify the brightness
and dynamic range for many biosensor variants in parallel. We applied
this method to two common biosensor optimization tasks: the choice
of insertion site and optimization of linker sequences. The sort-seq
assay applied to a maltose-binding protein domain-insertion library
not only identified previously described high-dynamic-range variants
but also discovered new functional insertion sites with diverse properties.
A sort-seq assay performed on a pyruvate biosensor linker library
expressed in mammalian cell culture identified linker variants with
substantially improved dynamic range. Machine learning models trained
on the resulting data can predict dynamic range from linker sequences.
This high-throughput approach will accelerate the design and optimization
of SFPBs, expanding the biosensor toolbox.