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

2025/08/06 by Julian Beck, Beck, Julian, Sergio Romero‐Romero +2 · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · #Biomolecules (q-bio.BM) #FOS: Biological sciences #Intersection (aeronautics) #Posttranslational modification #Protein engineering #Scaffold #Scaffold protein #Synthetic biology #Tunneling and Rock Mechanics #q-bio.BM

paper · pdf · doi:10.48550/arxiv.2508.05699

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/08/06 · arxiv published 2025/08/06 · arxiv updated 2025/08/25 · openalex created_date 2025/10/15 · openalex updated_date 2026/08/05

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.

Citations

• Computational enzyme design by catalytic motif scaffolding
• Computational design of serine hydrolases
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• Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop
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• Principles of Protein Stability and Their Application in Computational Design

Cited by

• Stability and cooperativity in chimeric de novo TIM barrels via quarter swapping

Discussions

• Julian Beck, Sergio Romero-Romero: Designing de novo TIM Barrels: Insights into Stabilization, Diversification, and Functionalization Strategies https://arxiv.org/abs/2508.05699 https://arxiv.org/pdf/ [bsky, 0 points, 0 comments]

Related

• Diversifying de novo TIM barrels by hallucination
• Stepwise introduction of stabilizing mutations reveals nonlinear additive effects in de novo TIM barrels
• Stability and cooperativity in chimeric de novo TIM barrels via quarter swapping
• Computational enzyme design by catalytic motif scaffolding
• Combinatorial assembly and design of enzymes