Identification of slow molecular order parameters for Markov model construction.

By G. Pérez-Hernández, Fabian Paul, Toni Giorgino, G. de Fabritiis, Frank No'e. 2013

1,019 citations recorded by the source corpus.

A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes via (i) identification of the structural changes involved in these processes and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either generic internal coordinates or a user-defined set of parameters. Using a variational formulation of conformational dynamics, it is shown that an existing method-the time-lagged independent component analysis-provides the optional solution to this problem. In addition, optimal indicators-order parameters indicating the progress of the slow transitions and thus may serve as reaction coordinates-are readily identified. We demonstrate that the slow subspace is well suited to construct accurate kinetic models of two sets of molecular dynamics simulations, the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue intrinsically disordered peptide kinase inducible domain (KID). The identified optimal indicators reveal the structural changes associated with the slow processes of the molecular system under analysis.

Subfields: Molecular Biology

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Citations in the corpus

Variational Approach for Learning Markov Processes from Time Series Data (2017, methodology): “…the variational approach of conformation dynamics (VAC) (Noé and Nüske 2013; Nüske et al. 2014), time-lagged independent component analysis (TICA) (Perez-Hernandez et al. 2013; Schwantes and Pande 2013), dynamic mode decomposition (DMD) (Rowley et al. 2009; Schmid 2010; Tu et al. 2014), extended…”
Markov state models of biomolecular conformational dynamics (2014, methodology): “[21 ].”
AlphaDesign: A de novo protein design framework based on AlphaFold (2021, neutral): “…methods such as molecular dynamics (MD) simulations have provided a route to characterise the dynamics, thermodynamics and kinetics of conformational transitions [29, 30], ab initio folding [31], disordered transitions [32, 33] as well as protein-protein [34, 35] and protein-ligand [36] binding.”