Neural Network Based Prediction of Conformational Free Energies : a New Route toward Coarse-Grained Simulation Models

Lemke, Tobias; Peter, Christine

Neural Network Based Prediction of Conformational Free Energies : a New Route toward Coarse-Grained Simulation Models

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Datum

2017

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Journal of Chemical Theory and Computation : JCTC. 2017, 13(12), pp. 6213-6221. ISSN 1549-9618. eISSN 1549-9626. Available under: doi: 10.1021/acs.jctc.7b00864

Zusammenfassung

Coarse-grained (CG) simulation models have become very popular tools to study complex molecular systems with great computational efficiency on length and time scales that are inaccessible to simulations at atomistic resolution. In so-called bottom-up coarse-graining strategies, the interactions in the CG model are devised such that an accurate representation of an atomistic sampling of configurational phase space is achieved. This means the coarse-graining methods use the underlying multibody potential of mean force (i.e., free-energy surface) derived from the atomistic simulation as parametrization target. Here, we present a new method where a neural network (NN) is used to extract high-dimensional free energy surfaces (FES) from molecular dynamics (MD) simulation trajectories. These FES are used for simulations on a CG level of resolution. The method is applied to simulating homo-oligo-peptides (oligo-glutamic-acid (oligo-glu) and oligo-aspartic-acid (oligo-asp)) of different lengths. We show that the NN not only is able to correctly describe the free-energy surface for oligomer lengths that it was trained on but also is able to predict the conformational sampling of longer chains.

Fachgebiet (DDC)

540 Chemie

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ISO 690

LEMKE, Tobias, Christine PETER, 2017. Neural Network Based Prediction of Conformational Free Energies : a New Route toward Coarse-Grained Simulation Models. In: Journal of Chemical Theory and Computation : JCTC. 2017, 13(12), pp. 6213-6221. ISSN 1549-9618. eISSN 1549-9626. Available under: doi: 10.1021/acs.jctc.7b00864

BibTex

@article{Lemke2017-12-12Neura-41117,
  year={2017},
  doi={10.1021/acs.jctc.7b00864},
  title={Neural Network Based Prediction of Conformational Free Energies : a New Route toward Coarse-Grained Simulation Models},
  number={12},
  volume={13},
  issn={1549-9618},
  journal={Journal of Chemical Theory and Computation : JCTC},
  pages={6213--6221},
  author={Lemke, Tobias and Peter, Christine}
}

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