Biophysical Characterization Platform Informs Protein Scaffold Evolvability.

Area of Science:

• Protein engineering
• Computational biology
• Molecular evolution

Background:

• Evolving protein molecular recognition requires navigating complex mutational landscapes.
• Protein scaffolds offer a conserved platform for modular paratope evolution to alter binding specificity.
• Properties enabling protein scaffold binding evolution are currently unknown.

Purpose of the Study:

• To develop an algorithm predicting protein scaffold evolvability for novel binding functions.
• To identify key biophysical parameters governing the evolution of protein binding specificity.
• To establish a link between protein developability and evolvability.

Main Methods:

• Computationally calculated 20 topological and biophysical properties for 787 small protein scaffolds.
• Used magnetic activated cell sorting (MACS) of 10^10 yeast-displayed variants across seven campaigns to assess binding evolution.
• Applied regularization techniques to identify predictive features for binding functionality.

Main Results:

• Developed a predictive model with a 4/6 true positive rate, 9/11 negative predictive value, and 4/6 positive predictive value.
• Identified a large, disconnected paratope as a key feature enabling evolved binding function.
• Demonstrated a correlation between protein production in the soluble fraction of E. coli (developability) and the ability to evolve binding function.

Conclusions:

• Computational prediction of biophysical parameters can guide protein scaffold selection for evolvability.
• Protein developability, including producibility and stability, is crucial for successful binding evolution.
• Future protein scaffold discovery should integrate computational prediction with an assessment of initial developability properties.