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

TL;DR

This paper introduces a new framework combining order parameter fluctuations and complex network analysis to improve the accuracy of kinetic and state identification in molecular simulations and experiments.

Contribution

It presents a novel approach that uses fluctuations around order parameters and network clustering to construct accurate Markov-State-Models without extra data.

Findings

Effective in theoretical models with noisy order parameters

Accurately captures dynamics of a disordered peptide

Improves state and kinetic analysis from time series data

Abstract

Molecular simulations as well as single molecule experiments have been widely analyzed in terms order parameters, the latter representing candidate probes for the relevant degrees of freedom. Notwithstanding this approach is very intuitive, mounting evidence showed that such description is not accurate, leading to ambiguous definitions of states and wrong kinetics. To overcome these limitations a framework making use of order parameter fluctuations in conjunction with complex network analysis is investigated. Derived from recent advances in the analysis of single molecule time traces, this approach takes into account of the fluctuations around each time point to distinguish between states 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-Models of the system under study. Application of the methodology to theoretical models with a noisy order parameter as well as the dynamics of a disordered peptide illustrates the possibility to build accurate descriptions of molecular processes on the sole basis of order parameter time series without using any supplementary information.