Abstract
<jats:p>Comparative molecular dynamics studies of protein variants are commonly based on equilibrium descriptors such as conformational populations or free-energy landscapes. However, these quantities do not fully characterize how conformational transitions are organized within a shared state space. Here, we present a transferable computational framework that integrates a common conformational representation, directional probabilitycurrent analysis, reversible Markov State Models (MSMs), and Transition Path Theory (TPT) to compare mutation-dependent conformational dynamics using identical state definitions. As a proof of concept, the framework was applied to a panel of eight BRAF kinase-domain systems comprising the wild type and seven disease-associated variants. All trajectories were projected onto a shared principal-component space, discretized into a common three-state model, and analyzed using complementary descriptors including state populations, directional probability currents, dynamic fingerprints, and reactive pathway decomposition. Robustness was evaluated by replica bootstrap resampling, lag-time sensitivity analysis, permutation testing, and Chapman–Kolmogorov validation. The analyses demonstrate that equilibrium state populations, directional probability currents, and TPT reactive pathways provide complementary and mutually non-redundant descriptions of conformational dynamics within a common state space. Variants exhibiting similar state occupancies frequently displayed distinct directional-current networks and reactive-pathway organization, illustrating that these descriptors capture different aspects of the same underlying dynamics. At the present proof-of-concept sampling depth, differences between individual variants are interpreted descriptively rather than as statistically resolved separations. These results establish the methodological feasibility of integrating shared conformational state models, probability-current analysis, reversible MSMs, and TPT into a unified comparative framework. More generally, the approach provides a transferable strategy for investigating mutation-dependent conformational dynamics beyond conventional equilibrium structural analyses.</jats:p>