This work reviews the computational and experimental approaches that disentangle folding and function at scale, revealing a dark energy component and providing new insights into how biological information flows from sequence to structure to function and back to sequence.
Abstract
The evolutionary fate of proteins is driven by both folding stability and biological function, dual constraints that often conflict, creating frustration and imposing functional costs beyond stability. These costs can be captured by a"dark energy": the difference between the evolutionary energy of protein sequences and their physical folding energy. Recent advances in deep mutational scanning, protein language models, and inverse-folding models have enabled the quantification of dark energy across the protein universe. We review the computational and experimental approaches that disentangle folding and function at scale, revealing a dark energy component and providing new insights into how biological information flows from sequence to structure to function and back to sequence.
Together, these insights position conformational dynamics at the center of understanding and engineering the evolutionary logic of protein function, opening the door to study how proteins are tuned to operate under the nonequilibrium conditions of living cells.
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It is concluded that the early emergence of the Rossmann fold reflects the chemical and physical constraints of protein folding, explaining both its profound antiquity and sustained longevity.
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The (un)folding rates of natural proteins determine their native stability and functional homeostasis, making them important targets for protein engineering and design. From a prediction standpoint, the rates have been a long‐standing puzzle. We have known for decades that folding rates empirically correlate with prope...
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This lattice protein model defines fitness as the equilibrium probability of a fixed active-site motif and uses multicanonical sequence sampling to show how thermal fluctuations turn a local functional requirement into a global structural constraint.
It is shown that short nucleic acids containing Gquadruplex (G4) structure can also catalyze protein folding and uncovers a previously underappreciated role for nucleic acid in proteostasis and offers a new strategy for studying nucleic acid structure-function relationship at residue level.
It is argued that incorporating frustration into computational and experimental strategies will be essential to move beyond purely stability-driven approaches toward the rational engineering of functional proteins.
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A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
