Drift-diffusion (DrDiff) framework determines kinetics and thermodynamics of two-state folding trajectory and tunes diffusion models
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2019-09-21
Autores
Freitas, Frederico Campos
Lima, Angelica Nakagawa
Contessoto, Vinicius de Godoi [UNESP]
Whitford, Paul C.
Oliveira, Ronaldo Junio de
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Amer Inst Physics
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The stochastic drift-diffusion (DrDiff) theory is an approach used to characterize the dynamical properties of simulation data. With new features in transition times analyses, the framework characterized the thermodynamic free-energy profile [F(Q)], the folding time (tau(f)), and transition path time (tau(TP)) by determining the coordinate-dependent drift-velocity [v(Q)] and diffusion [D(Q)] coefficients from trajectory time traces. In order to explore the DrDiff approach and to tune it with two other methods (Bayesian analysis and fep1D algorithm), a numerical integration of the Langevin equation with known D(Q) and F(Q) was performed and the inputted coefficients were recovered with success by the diffusion models. DrDiff was also applied to investigate the prion protein (PrP) kinetics and thermodynamics by analyzing folding/unfolding simulations. The protein structure-based model, the well-known Go over bar -model, was employed in a coarse-grained C-alpha level to generate long constant-temperature time series. PrP was chosen due to recent experimental single-molecule studies in D and tau(TP) that stressed the importance and the difficulty of probing these quantities and the rare transition state events related to prion misfolding and aggregation. The PrP thermodynamic double-well F(Q) profile, the X shape of tau(f)(T), and the linear shape of tau(TP)(T) were predicted with v(Q) and D(Q) obtained by the DrDiff algorithm. With the advance of single-molecule techniques, the DrDiff framework might be a useful ally for determining kinetic and thermodynamic properties by analyzing time observables of biomolecular systems. The code is freely available at .
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Journal Of Chemical Physics. Melville: Amer Inst Physics, v. 151, n. 11, 13 p., 2019.