Computational modeling of regulated protein conformational transitions

Area of Science:

• Biochemistry and Biophysics
• Computational Biology
• Structural Biology

Background:

• Cellular regulation depends on dynamic protein conformational transitions, not static structures.
• Cells expend energy (adenosine triphosphate turnover) to bias proteins toward specific metastable states.
• Understanding and controlling these states is crucial for biological function.

Purpose of the Study:

• To review recent computational advances in resolving and designing protein conformational ensembles.
• To highlight three key mechanisms: ligand/allosteric control, mechanosensitive unfolding/refolding, and intrinsically disordered region dynamics.
• To explore emerging deep-learning frameworks for programming protein transitions.

Main Methods:

• Computational simulations to resolve protein conformational ensembles.
• Analysis of ligand- and allosteric-driven protein redistribution.
• Investigation of force-gated unfolding/refolding in mechanosensitive proteins.
• Study of disorder-order transitions in intrinsically disordered regions.
• Application of deep learning for observing and programming protein dynamics.

Main Results:

• Computational methods can now resolve and design biased protein ensembles.
• Three distinct mechanisms of ensemble control have been identified and studied.
• Deep learning shows promise in observing and programming protein conformational transitions.

Conclusions:

• Protein conformational transitions are actively regulated and essential for cellular function.
• Computational approaches, including deep learning, are advancing the rational design of protein ensembles.
• Controlling ensemble occupancy is emerging as a feasible design target for biological systems.