HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations

Anderson, Jon; Gläser, Jens

doi:10.1016/j.commatsci.2019.109363

Cited by 452 publications

(347 citation statements)

References 46 publications

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“…The descriptive power of modeling has advanced rapidly in recent years as a result of computing power increases, availability of easy-to-use general-purpose simulation toolkits (e.g., HOOMD-blue), 45 and improvements in algorithms and model assumptions. To date, the most successful applications of self-assembly simulations are structure prediction (local order, mesophases, crystallographic order) and resolving particle dynamics.…”

Section: Theoretical and Computational Insights In Self-assemblymentioning

confidence: 99%

Functional materials and devices by self-assembly

Talapin

Engel

2020

MRS Bull.

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Section: Theoretical and Computational Insights In Self-assemblymentioning

confidence: 99%

Functional materials and devices by self-assembly

Talapin

Engel

2020

MRS Bull.

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“…The temperature is controlled using the Langevin thermostat in the low friction limit. All the simulations are conducted with the HOOMD-Blue package to take advantage of its capabilities to speed-up calculations using the graphics processing units (GPUs) (52). For more details about the methodology and the analysis, we refer the readers to our previous work (33,53).…”

Section: Simulation Strategy For Sampling Protein-rna Multicomponentmentioning

confidence: 99%

Sequence dependent co-phase separation of RNA-protein mixtures elucidated using molecular simulations

Regy

Dignon

Zheng

et al. 2020

Preprint

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ABSTRACTRibonucleoprotein (RNP) granules are membraneless organelles (MLOs) which majorly consist of RNA and RNA-binding proteins and are formed via liquid-liquid phase separation (LLPS). Experimental studies investigating the drivers of LLPS have shown that intrinsically disordered proteins (IDPs) and nucleic acids like RNA play a key role in modulating protein phase separation. There is currently a dearth of modelling techniques which allow one to delve deeper into how RNA plays its role as a modulator/promoter of LLPS in cells using computational methods. Here we present a coarse-grained RNA model developed to fill this gap, which together with our recently developed HPS model for protein LLPS, allows us to capture the factors driving RNA-protein co-phase separation. We explore the capabilities of the modelling framework with the LAF-1 RGG/RNA system which has been well studied in experiments and also with the HPS model previously. Further taking advantage of the fact that the HPS model maintains sequence specificity we explore the role of charge patterning on controlling RNA incorporation into condensates. With increased charge patterning we observe formation of structured or patterned condensates which suggests the possible roles of RNA in not only shifting the phase boundaries but also introducing microscopic organization in MLOs.

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“…The HOOMD-TF package pairs the TensorFlow ML library (Abadi et al, 2015) with the HOOMD-blue simulation engine (Anderson, Glaser, & Glotzer, 2020) to allow for flexible online ML and tensor calculations during HOOMD-blue simulations. Since both TensorFlow and HOOMD-blue are GPU-accelerated, HOOMD-TF was designed with a GPU-GPU communication scheme that minimizes latency between GPU memory to preserve execution speed.…”

Section: Resultsmentioning

confidence: 99%