Statistical Thermodynamics Based Design Principles into the Temperature Induced Fold Switching of a Metamorphic...

Fold-switching metamorphic protein sequences defy the classical "one sequence - one fold" paradigm. The ability of metamorphic proteins to reversibly switch between distinct folds make them attractive de novo protein engineering candidates since they can function as environment-sensitive molecular switches. However, the underlying thermodynamic design principles that drive their fold-switching behavior is poorly understood. Gaining insights into the molecular driving forces leading to fold switching behavior is crucial for the rational design of new metamorphic proteins based molecular switches, sensors and stimulus responsive nanomaterials. In this study, we perform a detailed thermodynamic analysis of a designed fold-switching protein [Solomon et al. PNAS 2023, 120 (4), e2215418120] that transitions between a 3α fold and α/β fold upon changes in temperature. We use an efficient advanced sampling molecular simulation based free energy calculation approach called Confine-Desolvate-Convert-Solvate-Release (CDCSR), which subjects the protein through the complete range of thermodynamic cycle and deconvolutes the enthalpic and entropic driving forces at each stage of the cycle. We find that while 3α fold is stabilized at low temperatures by enthalpic contributions from favorable water-water and protein-water interactions, the transition to the α/β fold at high temperatures is driven by the gain of entropy from the release of ordered water molecules surrounding the 3α fold. Our study elucidates the molecular driving forces governing temperature-induced fold-switching behavior and provides a rigorous statistical thermodynamic framework that can help in the design and engineering of future synthetic and functional metamorphic proteins. Significance: Recent success in the de novo protein design for molecular function is powered by the transformative AI methods and interpreted using the classical wisdom from the Physics-based modeling approaches. Stimuli-sensitive fold switching proteins that can take multiple shapes based on environmental factors are the next frontier in de novo protein engineering. In this work, we unravel the thermodynamic driving forces behind a recently designed temperature-sensitive artificial protein and provide a physics-based framework to understand the design principles leading to fold switching behavior. Our work complements the ongoing AI methods that are being explored to unravel the hidden evolutionary embeddings inherent in fold-switching proteins and also highlights the importance of thinking in terms of entropy-based design principles for natural systems.