Computational modeling of regulated protein conformational transitions.
TL;DR
This mini-review summarizes recent computational advances that resolve and, increasingly, design biased ensembles across three mechanisms: ligand- and allosteric-driven redistribution of signaling proteins, force-gated unfolding and refolding in mechanosensitive scaffolds, and disorder↔order reweighting in intrinsically disordered regions.
Abstract
Conformational transitions are not accidents; they are the currency of regulation. Cells actively spend energy through adenosine triphosphate (ATP) turnover, mechanical work, targeted post-translational modification to bias proteins toward specific metastable states in space and time, rather than stabilizing a single 'active' structure. This mini-review summarizes recent computational advances that resolve and, increasingly, design these biased ensembles across three mechanisms: (i) ligand- and allosteric-driven redistribution of signaling proteins, (ii) force-gated unfolding and refolding in mechanosensitive scaffolds, and (iii) disorder↔order reweighting in intrinsically disordered regions. We further outline emerging deep-learning frameworks that aim not only to observe such transitions, but also to program them, suggesting that rational control of ensemble occupancy is becoming an achievable design target.