Browsing by Author "Charron, Nicholas E."

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Coarse graining molecular dynamics with graph neural networks
    (American Institute of Physics, 2020) Husic, Brooke E.; Charron, Nicholas E.; Lemm, Dominik; Wang, Jiang; Pérez, Adrià; Majewski, Maciej; Krämer, Andreas; Chen, Yaoyi; Olsson, Simon; de Fabritiis, Gianni; Noé, Frank; Clementi, Cecilia; Center for Theoretical Biological Physics
    Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features to machine learn the force field. In the present contribution, we build upon the advance of Wang et al. and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems.
  • Thumbnail Image
    Item
    Comparative Study of the Condensing Effects of Ergosterol and Cholesterol
    (Cell Press, 2016) Hung, Wei-Chin; Lee, Ming-Tao; Chung, Hsien; Sun, Yi-Ting; Chen, Hsiung; Charron, Nicholas E.; Huang, Huey W.
    Cholesterol, due to its condensing effect, is considered an important regulator of membrane thickness. Other sterols, due to their structural similarities to cholesterol, are often assumed to have a universal effect on membrane properties similar to the condensing effect of cholesterol, albeit possibly to different degrees. We used x-ray diffraction to investigate this assumption. By the combination of lamellar diffraction and grazing-angle scattering, we measured the membrane thickness and the tilt-angle distribution of the lipid’s hydrocarbon chains. This method is sensitive to phase separation, which is important for examining the miscibility of sterols and phospholipids. Mixtures of ergosterol or cholesterol with dimyristoylphosphatidylcholine, palmitoyloleoylphosphatidylcholine, and dioleoylphosphatidylcholine were systematically studied. We found that mixing ergosterol with phospholipids into a single phase became increasingly difficult with higher sterol concentrations and also with higher concentrations of unsaturated lipid chains. The only condensing effect of ergosterol was found in dimyristoylphosphatidylcholine, although the effect was less than one-third of the effect of cholesterol. Unlike cholesterol, ergosterol could not maintain a fixed electron density profile of the surrounding lipids independent of hydration. In dioleoylphosphatidylcholine and palmitoyloleoylphosphatidylcholine, ergosterol made the membranes thinner, opposite to the effect of cholesterol. In all cases, the tilt-angle variation of the chain diffraction was consistent with the membrane thickness changes measured by lamellar diffraction, i.e., a thickening was always associated with a reduction of chain tilt angles. Our findings do not support the notion that different sterols have a universal behavior that differs only in degree.
  • Thumbnail Image
    Item
    Machine learning coarse-grained potentials of protein thermodynamics
    (Springer Nature, 2023) Majewski, Maciej; Pérez, Adrià; Thölke, Philipp; Doerr, Stefan; Charron, Nicholas E.; Giorgino, Toni; Husic, Brooke E.; Clementi, Cecilia; Noé, Frank; De Fabritiis, Gianni; Center for Theoretical Biological Physics
    A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.
  • Thumbnail Image
    Item
    Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
    (American Chemical Society, 2019) Wang, Jiang; Olsson, Simon; Wehmeyer, Christoph; Pérez, Adrià; Charron, Nicholas E.; de Fabritiis, Gianni; Noé, Frank; Clementi, Cecilia
    Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.