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Molecular simulations as well as single molecule experiments have been widelyanalyzed in terms order parameters, the latter representing candidate probesfor the relevant degrees of freedom. Notwithstanding this approach is veryintuitive, mounting evidence showed that such description is not accurate,leading to ambiguous definitions of states and wrong kinetics. To overcomethese limitations a framework making use of order parameter fluctuations inconjunction with complex network analysis is investigated. Derived from recentadvances in the analysis of single molecule time traces, this approach takesinto account of the fluctuations around each time point to distinguish betweenstates that have similar values of the order parameter but different dynamics.Snapshots with similar fluctuations are used as nodes of a transition network,the clusterization of which into states provides accurate Markov-State-Modelsof the system under study. Application of the methodology to theoretical modelswith a noisy order parameter as well as the dynamics of a disordered peptideillustrates the possibility to build accurate descriptions of molecularprocesses on the sole basis of order parameter time series without using anysupplementary information.

Accounting for the kinetics in order parameter analysis: lessons from theoretical models and a disordered peptide