Global analysis of protein folding using massively parallel design, synthesis, and testing

Area of Science:

• Biochemistry
• Structural Biology
• Computational Biology

Background:

• Protein structure is determined by weak interactions, but decoding these forces is complex due to natural proteins evolving for function over stability.
• Understanding the sequence-stability relationship is crucial for protein engineering.

Purpose of the Study:

• To systematically examine how amino acid sequence determines protein folding and stability in novel protein structures.
• To develop a data-driven approach for computational protein design by integrating computational methods with high-throughput experimental validation.

Main Methods:

• Utilized computational protein design, next-generation gene synthesis, and a high-throughput protease susceptibility assay.
• Assessed folding and stability for over 15,000 de novo designed miniproteins, 1,000 natural proteins, 10,000 point mutants, and 30,000 negative control sequences.

Main Results:

• Identified over 2,500 stable designed proteins across four basic folds, providing a dataset for analyzing sequence-stability relationships.
• Achieved a significant increase in design success rate from 6% to 47% through iterative design and experimental feedback.
• Generated stable proteins with novel topologies and revealed subtle stability contributions during optimization.

Conclusions:

• Established a tight feedback cycle between computational design and experimental validation, transforming protein design into a data-driven science.
• Demonstrated the potential to design stable proteins with predictable folding properties in uncharted sequence space.
• The developed methodology offers a powerful platform for advancing protein engineering and understanding fundamental protein biophysics.