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[Image: see text] 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.

The ability of vascular-targeted drug carriers (VTCs) to localize and bind to a targeted, diseased endothelium determines their overall clinical utility. Here, we investigate how particle modulus and size determine adhesion of VTCs to the vascular wall under physiological blood flow conditions. In general, deformable microparticles (MPs) outperformed nanoparticles (NPs) in all experimental conditions tested. Our results indicate that MP modulus enhances particle adhesion in a shear-dependent manner. In low shear human blood flow profiles in vitro, low modulus particles showed favorable adhesion, while at high shear, rigid particles showed superior adhesion. This was confirmed in vivo by studying particle adhesion under venous shear profiles in a mouse model of mesenteric inflammation, where MP adhesion was 127% greater (p < 0.0001) for low modulus particles compared to more rigid ones. Mechanistically, we establish that particle collisions with leukocytes drive these trends, rather than differences in particle deformation, localization, or detachment. Overall, this work demonstrates the importance of VTC modulus as a design parameter for enhanced VTC interaction with vascular walls, and thus, contributes important knowledge for development of successful clinical theranostics with applications for many diseases.