Biophysical characterization platform informs protein scaffold evolvability

Alexander W. Golinski, Patrick V. Holec, Katelynn M. Mischler, Benjamin J. Hackel

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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 10 10 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.

Original languageEnglish (US)
JournalACS Combinatorial Science
StatePublished - Apr 8 2019

Bibliographical note

Publisher Copyright:
© 2019 American Chemical Society.


  • predictive algorithm
  • protein evolvability
  • protein scaffolds


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