Small protein scaffolds, relative to antibodies, can provide superior physiological distribution, stability, production, and ease of conjugation. Though many scaffolds have been tested to generate synthetic ligands, the causes of the varying degrees of success or failure have not been systematically explored. We merged computational and experimental tools to assess the biophysical factors that dictate evolutionary potential to empower rational identification or design of protein scaffolds. We developed and implemented a computational algorithm to efficiently evaluate protein domains on numerous metrics with potential impact on evolution of binding function. In one implementation, we identified all domains in the Protein Data Bank with two accessible loops (diversified paratope) and scored them on the size, shape, orientation, and target accessibility of their paratopes as well as protein size, stability upon mutation, and dependence on disulfide bonds and/or cofactors. Machine learning was used to weigh each metric based on historical scaffold performance. The top scaffold was the 45-residue T7 phage gene protein 2. We diversified the 12 loop amino acids using complementarity bias and length diversity. 108 mutants were displayed on yeast and screened for binders to several protein targets. Directed evolution yielded strong binders (down to 200±100 pM) with no observable nonspecific binding. Circular dichroism showed 2º structure comparable to the wild-type protein and high stability (Tm: 70±4ºC to 80±1ºC for binders; 67±4º for wild-type). We evolved binders to EGFR and MET that effectively label human tumor cells. Improved library design via deep sequencing and stability engineering will be discussed. More broadly, libraries from the top 30 scaffolds were created for comparative analysis of the ability to evolve specific, high affinity binding to numerous targets. We will discuss the relative efficacy of each scaffold as well as the scaffold design metrics that correlate most strongly with performance. Implications for future scaffold design, including synthetic protein topologies, will be addressed as well as other designs.

Rational identification of scaffolds for combinatorial discovery of ligands

249th National Meeting (2015)

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