Multiscale molecular simulations to investigate adenylyl cyclase‐based signaling in the brain

Keulen, Siri C. van; Martin, Juliette; Colizzi, Francesco; Frezza, Elisa; Trpevski, Daniel; Dı́az, Nuria; Vidossich, Pietro; Rothlisberger, Ursula; Kotaleski, Jeanette Hellgren; Wade, Rebecca C.; Carloni, Paolo

doi:10.1002/wcms.1623

Cited by 3 publications

(4 citation statements)

References 108 publications

(264 reference statements)

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“…The association rate constants computed by BD were consistent with the ability of the adenylyl cyclase‐based network to detect simultaneous changes in neuromodulatory signals, which is important for learning and memory formation. This study demonstrates the potential of multiscale modeling approaches incorporating subcellular and molecular level methods, including BD, for studying complex biochemical networks 80,81 …”

Section: Applicationsmentioning

confidence: 77%

“…This study demonstrates the potential of multiscale modeling approaches incorporating subcellular and molecular level methods, including BD, for studying complex biochemical networks. 80,81 Further downstream in the cAMP signaling network, the protein kinase A heterotetramer was simulated with the aim of resolving discordant experimental structural data on its regulatory subunit by using MD to generate initial protein structures and then BD to assess the binding of cAMP to them. 82 The simulations revealed a stable R-subunit "Flipback" structure and showed a likely binding mechanism between this structure and cAMP.…”

Section: Molecular Signaling Cascadesmentioning

confidence: 99%

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Brownian dynamics simulations of biomolecular diffusional association processes

Muñiz‐Chicharro

Votapka

Amaro

et al. 2022

WIREs Comput Mol Sci

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Brownian dynamics (BD) is a computational method to simulate molecular diffusion processes. Although the BD method has been developed over several decades and is well established, new methodological developments are improving its accuracy, widening its scope, and increasing its application. In biological applications, BD is used to investigate the diffusive behavior of molecules subject to forces due to intermolecular interactions or interactions with material surfaces. BD can be used to compute rate constants for diffusional association, generate structures of encounter complexes for molecular binding partners, and examine the transport properties of geometrically complex molecules. Often, a series of simulations is performed, for example, for different protein mutants or environmental conditions, so that the effects of the changes on diffusional properties can be estimated. While biomolecules are commonly described at atomic resolution and internal molecular motions are typically neglected, coarse‐graining and the treatment of conformational flexibility are increasingly employed. Software packages for BD simulations of biomolecules are growing in capabilities, with several new packages providing novel features that expand the range of questions that can be addressed. These advances, when used in concert with experiment or other simulation methods, such as molecular dynamics, open new opportunities for application to biochemical and biological systems. Here, we review some of the latest developments in the theory, methods, software, and applications of BD simulations to study biomolecular diffusional association processes and provide a perspective on their future use and application to outstanding challenges in biology, bioengineering, and biomedicine. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Software > Simulation Methods

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Section: Applicationsmentioning

confidence: 77%

Section: Molecular Signaling Cascadesmentioning

confidence: 99%

Brownian dynamics simulations of biomolecular diffusional association processes

Muñiz‐Chicharro

Votapka

Amaro

et al. 2022

WIREs Comput Mol Sci

Self Cite

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“…The MolSieve interface allows, among other things, adjusting the exploratory parameters of each trajectory and provides a widget for comparing sub-sequences. In the same way in which multiscale molecular dynamics simulations have gained in popularity ( Dans et al, 2016 ; Dommer et al, 2023 ; van Keulen et al, 2023 ), analysis and visualization tools for these simulation ensembles must evolve accordingly. To tackle this task one may take inspiration on existing databases of molecular simulations that comprise visualization.…”

Section: Scaling Up To Simulation Ensembles and Data Sharingmentioning

confidence: 99%

From complex data to clear insights: visualizing molecular dynamics trajectories

Belghit,

Spivak,

Dauchez

et al. 2024

Front. Bioinform.

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Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

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“…However, it is sometimes possible to use predictions from a model at finer biological resolution to provide constraints to model parameters at the next level of abstraction ( Boras et al, 2015 ; Stein et al, 2007 ; Wang et al, 2018 ; Xie et al, 2014 ). For example, molecular dynamics simulations which use biomolecular structural data can provide important quantitative or qualitative constraints on kinetic parameters, binding affinities, and their modulation by allosteric interactions in intracellular signaling pathway models ( Bruce et al, 2019b , Bruce et al, 2019a ; Gabdoulline et al, 2003 ; van Keulen et al, 2022 ). Tools exist to facilitate the use of biomolecular structural data in model building ( Stein et al, 2007 ).…”

Section: Framework For Fair Modeling Workflowsmentioning

confidence: 99%

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Eriksson

Bhalla

Blackwell

et al. 2022

eLife

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Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data – such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles – also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock–Cooper–Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

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Multiscale molecular simulations to investigate adenylyl cyclase‐based signaling in the brain

Cited by 3 publications

References 108 publications

Brownian dynamics simulations of biomolecular diffusional association processes

Brownian dynamics simulations of biomolecular diffusional association processes

From complex data to clear insights: visualizing molecular dynamics trajectories

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

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