Biophysical Characterization Platform Informs Protein Scaffold Evolvability

Evolving specific
molecular recognition function of proteins requires
strategic navigation of a complex mutational landscape. Protein scaffolds
aid evolution via a conserved platform on which a modular paratope
can be evolved to alter binding specificity. Although numerous protein
scaffolds have been discovered, the underlying properties that permit
binding evolution remain unknown. We present an algorithm to predict
a protein scaffold’s ability to evolve novel binding function
based upon computationally calculated biophysical parameters. The
ability of 17 small proteins to evolve binding functionality across
seven discovery campaigns was determined via magnetic activated cell
sorting of 1010 yeast-displayed protein variants. Twenty
topological and biophysical properties were calculated for 787 small
protein scaffolds and reduced into independent components. Regularization
deduced which extracted features best predicted binding functionality,
providing a 4/6 true positive rate, a 9/11 negative predictive value,
and a 4/6 positive predictive value. Model analysis suggests a large,
disconnected paratope will permit evolved binding function. Previous
protein engineering endeavors have suggested that starting with a
highly developable (high producibility, stability, solubility) protein
will offer greater mutational tolerance. Our results support this
connection between developability and evolvability by demonstrating
a relationship between protein production in the soluble fraction
of Escherichia coli and the ability to evolve binding
function upon mutation. We further explain the necessity for initial
developability by observing a decrease in proteolytic stability of
protein mutants that possess binding functionality over nonfunctional
mutants. Future iterations of protein scaffold discovery and evolution
will benefit from a combination of computational prediction and knowledge
of initial developability properties.