Designing de novo TIM Barrels: Insights into Stabilization, Diversification, and Functionalization Strategies

TL;DR

This review discusses recent advances and ongoing challenges in designing de novo TIM barrels, focusing on stabilization, diversification, and functionalization strategies using computational and experimental methods.

Contribution

It provides a comprehensive overview of the state-of-the-art in TIM barrel design, emphasizing the integration of AI and physical methods for functional enzyme creation.

Findings

Significant progress in stabilizing de novo TIM barrels.

Recent breakthroughs in creating functionalized variants.

Limitations remain in achieving natural-like active sites.

Abstract

The TIM-barrel fold is one of the most versatile and ubiquitous protein folds in nature, hosting a wide variety of catalytic activities and functions while serving as a model system in protein biochemistry and engineering. This review explores its role as a key fold model in protein design, particularly in addressing challenges in stabilization and functionalization. We discuss historical and recent advances in de novo TIM barrel design from the landmark creation of sTIM11 to the development of the diversified variants, with a special focus on deepening our understanding of the determinants that modulate the sequence-structure-function relationships of this architecture. Also, we examine why the diversification of de novo TIM barrels towards functionalization remains a major challenge, given the absence of natural-like active site features. Current approaches have focused on incorporating structural extensions, modifying loops, and using cutting-edge AI-based strategies to create scaffolds with tailored characteristics. Despite significant advances, achieving enzymatically active de novo TIM barrels has been proven difficult, with only recent breakthroughs demonstrating functionalized designs. We discuss the limitations of stepwise functionalization approaches and support an integrated approach that simultaneously optimizes scaffold structure and active site shape, using both physical- and AI-driven methods. By combining computational and experimental insights, we highlight the TIM barrel as a powerful template for custom enzyme design and as a model system to explore the intersection of protein biochemistry, biophysics, and design.