Browsing by Author "Husic, Brooke E."

Now showing 1 - 3 of 3
  • 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
    Deeptime: a Python library for machine learning dynamical models from time series data
    (IOP Publishing, 2021) Hoffmann, Moritz; Scherer, Martin; Hempel, Tim; Mardt, Andreas; Silva, Brian de; Husic, Brooke E.; Klus, Stefan; Wu, Hao; Kutz, Nathan; Brunton, Steven L.; Noé, Frank
    Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastable and coherent sets, slow relaxation processes, collective variables, dominant transition pathways or manifolds and channels of probability flow can be of great importance for understanding and characterizing the kinetic, thermodynamic and mechanistic properties of the system. Deeptime is a general purpose Python library offering various tools to estimate dynamical models based on time-series data including conventional linear learning methods, such as Markov state models (MSMs), Hidden Markov Models and Koopman models, as well as kernel and deep learning approaches such as VAMPnets and deep MSMs. The library is largely compatible with scikit-learn, having a range of Estimator classes for these different models, but in contrast to scikit-learn also provides deep Model classes, e.g. in the case of an MSM, which provide a multitude of analysis methods to compute interesting thermodynamic, kinetic and dynamical quantities, such as free energies, relaxation times and transition paths. The library is designed for ease of use but also easily maintainable and extensible code. In this paper we introduce the main features and structure of the deeptime software. Deeptime can be found under https://deeptime-ml.github.io/.
  • 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.