Multidimensional Langevin Modeling of Nonoverdamped Dynamics

Schaudinnus, Norbert; Bastian, Björn; Hegger, Rainer; Stock, Gerhard

doi:10.1103/physrevlett.115.050602

Cited by 25 publications

(36 citation statements)

References 25 publications

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“…Indeed, we recently showed for a NaCl/water test system that such a correction can readily be achieved via dissipation-corrected targeted MD simulations. 58 As this approach additionally yields friction profiles, we will aim to use the resulting information to carry our Langevin Dynamics calculations 76 for the prediction of absolute ligand unbinding kinetics. Table S4.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

Estimation of Protein–Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations

Wolf

Amaral

Lowinski

et al. 2019

J. Chem. Inf. Model.

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We here report on non-equilibrium targeted Molecular Dynamics simulations as tool for the estimation of protein-ligand unbinding kinetics. With this method, we furthermore investigate the molecular basis determining unbinding rates, correlating simulations with experimental data from SPR kinetics measurements and X-ray crystallography on two small molecule compound libraries bound to the N-terminal domain of the chaperone Hsp90. Within the investigated libraries, we find ligand conformational changes and protein-ligand nonbonded interactions as discriminators for unbinding rates. Ligands with flexible chemical scaffold may remain longer at the protein target if they need to pass through extended conformations upon unbinding, or if they exhibit strong electrostatic and/or van der Waals interactions with the target. Ligands with rigid chemical scaffold can exhibit longer residence times if they need to perform any kind of conformational change for unbinding, while electrostatic interactions with the protein can facilitate unbinding. Our resultsshow that understanding the unbinding pathway and the protein-ligand interactions along this path is crucial for the prediction of small molecule ligands with defined unbinding kinetics. Supporting InformationFour supporting tables, nine supporting figures and additional references (PDF) SMILES annotations (CSV) Accession CodesThe crystallographic coordinates of novel compounds are deposited in the Protein Data Bank under the accession codes 5LRL (2d) and 5LO1 (2j). Authors will release the atomic coordinates and experimental data upon article publication.

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Section: Conclusion and Future Perspectivementioning

confidence: 99%

Estimation of Protein–Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations

Wolf

Amaral

Lowinski

et al. 2019

J. Chem. Inf. Model.

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“…While a suitable representation of the energy landscape should (at least) reproduce the correct number, energy, and location of the metastable states and barriers, these basic quantities often get lost when the energy landscape is projected on a low-dimensional subspace. 21,22 As a solution of this problem, we have recently suggested to use a combination of systematic dimensionality reduction methods [23][24][25][26][27][28][29] (that identify in a controlled manner adequate system degrees of freedom) and a multidimensional dLE 16,30,31 (that accounts for dimensionality of the collective coordinate). The resulting dLE model is able to quantitatively reproduce dynamical observables (such as time correlation functions and first passage times) that can be directly compared to the original MD data.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover the underlying assumptions of the model (such as the time scale separation) can directly be checked. 15,31 Rather than assuming that some intuitively chosen free energy curve may explain a dynamical process, a multidimensional dLE model thus aims to demonstrate that we get the right answers for the right reasons.…”

Section: Introductionmentioning

confidence: 99%

Global Langevin model of multidimensional biomolecular dynamics

Schaudinnus

Lickert

Biswas

et al. 2016

The Journal of Chemical Physics

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Molecular dynamics simulations of biomolecular processes are often discussed in terms of diffusive motion on a low-dimensional free energy landscape F(𝒙). To provide a theoretical basis for this interpretation, one may invoke the system-bath ansatz á la Zwanzig. That is, by assuming a time scale separation between the slow motion along the system coordinate x and the fast fluctuations of the bath, a memory-free Langevin equation can be derived that describes the system's motion on the free energy landscape F(𝒙), which is damped by a friction field and driven by a stochastic force that is related to the friction via the fluctuation-dissipation theorem. While the theoretical formulation of Zwanzig typically assumes a highly idealized form of the bath Hamiltonian and the system-bath coupling, one would like to extend the approach to realistic data-based biomolecular systems. Here a practical method is proposed to construct an analytically defined global model of structural dynamics. Given a molecular dynamics simulation and adequate collective coordinates, the approach employs an "empirical valence bond"-type model which is suitable to represent multidimensional free energy landscapes as well as an approximate description of the friction field. Adopting alanine dipeptide and a three-dimensional model of heptaalanine as simple examples, the resulting Langevin model is shown to reproduce the results of the underlying all-atom simulations. Because the Langevin equation can also be shown to satisfy the underlying assumptions of the theory (such as a delta-correlated Gaussian-distributed noise), the global model provides a correct, albeit empirical, realization of Zwanzig's formulation. As an application, the model can be used to investigate the dependence of the system on parameter changes and to predict the effect of site-selective mutations on the dynamics.

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“…When we find a time scale separation between the slow motion of the first few components (i.e., the "system") and the fast motion of the remaining components (i.e., the "bath"), the first PCs may serve as a multidimensional reaction coordinate. In this way, the collective variables {x i } may be a) Electronic address: stock@physik.uni-freiburg.de used to construct Langevin [17][18][19][20][21] or Markov state models [22][23][24][25][26][27] of the dynamics. Last but not least, PCs are often used to construct a free energy surface ∆G(x) = −k B T ln P(x), where P is the probability distribution of the MD data along x.…”

Section: Introductionmentioning

confidence: 99%

Contact- and distance-based principal component analysis of protein dynamics

Ernst

Sittel

Stock

2015

The Journal of Chemical Physics

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109

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To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between Cα-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

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Multidimensional Langevin Modeling of Nonoverdamped Dynamics

Cited by 25 publications

References 25 publications

Estimation of Protein–Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations

Estimation of Protein–Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations

Global Langevin model of multidimensional biomolecular dynamics

Contact- and distance-based principal component analysis of protein dynamics

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