Markov state models serve as a powerful tool for probing the transition from static to dynamic conformations in enzymes: A new direction for next-generation enzyme design.

Markov state models (MSMs) condense long enzyme molecular trajectories into transition probability matrices that reveal metastable conformations, thermodynamics, and kinetics across timescales.

• Why it matters: Enzyme function depends on dynamic conformational fluctuations, but common experimental methods often capture static structures, losing dynamic information and biasing structure–function understanding.
• What they did: The work reviews MSMs as a bridge from dynamic conformational states to enzyme functions and provides a beginner tutorial using lid dynamics in Candida antarctica lipase B.
• The result: MSMs are positioned as an enabling framework for studying folding, loop and pocket dynamics, allostery, and enzyme–ligand binding, and the tutorial aims to help address enzyme-engineering challenges like the activity–stability trade-off.

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