Charting molecular free-energy landscapes with an atlas of collective variables

Hashemian, Behrooz; Millán, Daniel; Arroyo, Marino

doi:10.1063/1.4966262

Cited by 5 publications

(3 citation statements)

References 60 publications

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“…Poor convergence or hysteresis are also problems associated with inadequate collective variables (Awasthi and Hub, 2016). With the aim to find better collective variables, several dimensionality reduction techniques that project data from biomolecular trajectories have been proposed (Tribello and Gasparotto, 2019;Wehmeyer and Noé, 2018;Hashemian et al, 2016). Particularly interesting are some innovative tools using machine learning (Doerr et al, 2021; FIGURE 1.…”

Section: Enhanced Sampling and Collective Variablesmentioning

confidence: 99%

Enhanced sampling for lipid-protein interactions during membrane dynamics

Masone¹

2023

Biocell

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The inflexible concept of membrane curvature as an independent property of lipid structures is today obsolete.Lipid bilayers behave as many-body entities with emergent properties that depend on their interactions with the environment. In particular, proteins exert crucial actions on lipid molecules that ultimately condition the collective properties of the membranes. In this review, the potential of enhanced molecular dynamics to address cell-biology problems is discussed. The cases of membrane deformation, membrane fusion, and the fusion pore are analyzed from the perspective of the dimensionality reduction by collective variables. Coupled lipid-protein interactions as fundamental determinants of large membrane remodeling events are also commented. Finally, novel strategies merging cell biology and physics are considered as future lines of research.

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Section: Enhanced Sampling and Collective Variablesmentioning

confidence: 99%

Enhanced sampling for lipid-protein interactions during membrane dynamics

Masone¹

2023

Biocell

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“…Focused ion beam (FIB), as a well-established technique in the semiconductor industry and material science, is emerging to be a highly reliable method for the sample preparation when jointed with cryo-ET [42]. Cryo-FIB has effectively improved the image quality by preventing artifacts from knife-cuts such as compression in the cutting direction, curved sections and crevasses [43]. The advantages of cryo-FIB have been extended by its affiliation with fluorescence microscopy, that is the correlated light and electron microscopy (CLEM), to target fluorescently-labelled proteins in cryo-FIB milled sample.…”

Section: The Advantages and Recent Optimization Of Cryo-etmentioning

confidence: 99%

Cryo-ET Workflow for Understanding Ion Channels Localization on the Nodes of Ranvier

Wang¹

2020

JBM

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The localization of ion channels on myelinated axon is closely related with the saltatory conduction of action potential (AP). Abnormal changes in these channels contribute to multiple mental diseases. The development of cryo-Electron Tomography (cryo-ET) has provided a promising prospect for peering into ion channels in their native environment at high resolution. Previous achievements are reviewed here on cryo-ET. Accordingly, a cryo-ET workflow is designed for understanding ion channels localization in myelinated axon, especially nodes of Ranvier, which are significant for the saltatory conduction involved in the propagation of high-speed AP. The workflow is divided into six parts: the preparation of neural cultures with myelin, antibodies and immunofluorescence staining, frozen-hydrated sample preparation, cryo-ET imaging, cryo-correlative light and electron microscopy (cryo-CLEM) imaging, three-dimensional (3D) reconstruction and refinement. The purpose is to conceive a possible solution for the problems related to ion channel compounds including localization, conformation dynamics, accessory structures of ion channel and transient regulatory factors, and thus provide insights into treating neurological diseases caused by abnormal ion channels activity.

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“…Finally, as LPCA produces a set of local coarse variables for each boundary point, book-keeping becomes increasingly complicated, especially as the entire exploration algorithm repeats LPCA for each expansion of the explored region. See [82] for an approach on handling the local charts.…”

Section: Local Principal Component Analysismentioning

confidence: 99%

An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients

Georgiou

Bello-Rivas

Gear

et al. 2017

Entropy

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In recent work, we have illustrated the construction of an exploration geometry on free energy surfaces: the adaptive computer-assisted discovery of an approximate low-dimensional manifold on which the effective dynamics of the system evolves. Constructing such an exploration geometry involves geometry-biased sampling (through both appropriately-initialized unbiased molecular dynamics and through restraining potentials) and, machine learning techniques to organize the intrinsic geometry of the data resulting from the sampling (in particular, diffusion maps, possibly enhanced through the appropriate Mahalanobis-type metric). In this contribution, we detail a method for exploring the conformational space of a stochastic gradient system whose effective free energy surface depends on a smaller number of degrees of freedom than the dimension of the phase space. Our approach comprises two steps. First, we study the local geometry of the free energy landscape using diffusion maps on samples computed through stochastic dynamics. This allows us to automatically identify the relevant coarse variables. Next, we use the information garnered in the previous step to construct a new set of initial conditions for subsequent trajectories. These initial conditions are computed so as to explore the accessible conformational space more efficiently than by continuing the previous, unbiased simulations. We showcase this method on a representative test system.

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Charting molecular free-energy landscapes with an atlas of collective variables

Cited by 5 publications

References 60 publications

Enhanced sampling for lipid-protein interactions during membrane dynamics

Enhanced sampling for lipid-protein interactions during membrane dynamics

Cryo-ET Workflow for Understanding Ion Channels Localization on the Nodes of Ranvier

An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients

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