JUWELS Booster – A Supercomputer for Large-Scale AI Research

Kesselheim, Stefan; Herten, Andreas; Krajsek, Kai; Ebert, Jan; Jitsev, Jenia; Cherti, Mehdi; Langguth, Michael; Gong, Bing; Stadtler, Scarlet; Mozaffari, Amirpasha; Cavallaro, Gabriele; Sedona, Rocco; Schug, Alexander; Strube, Alexandre; Kamath, Roshni; Schultz, Martin; Riedel, Morris; Lippert, Thomas

doi:10.1007/978-3-030-90539-2_31

Cited by 8 publications

(2 citation statements)

References 36 publications

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“…The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. ( ) for funding this project by providing computing time Biology 2023, 1, 0 17 of 23 through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC) [ 120 , 121 , 122 ].…”

Section: Acknowledgmentsmentioning

confidence: 99%

SPEADI: Accelerated Analysis of IDP-Ion Interactions from MD-Trajectories

Bruyn

Dorn

Zimmermann

et al. 2023

Biology

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The disordered nature of Intrinsically Disordered Proteins (IDPs) makes their structural ensembles particularly susceptible to changes in chemical environmental conditions, often leading to an alteration of their normal functions. A Radial Distribution Function (RDF) is considered a standard method for characterizing the chemical environment surrounding particles during atomistic simulations, commonly averaged over an entire or part of a trajectory. Given their high structural variability, such averaged information might not be reliable for IDPs. We introduce the Time-Resolved Radial Distribution Function (TRRDF), implemented in our open-source Python package SPEADI, which is able to characterize dynamic environments around IDPs. We use SPEADI to characterize the dynamic distribution of ions around the IDPs Alpha-Synuclein (AS) and Humanin (HN) from Molecular Dynamics (MD) simulations, and some of their selected mutants, showing that local ion–residue interactions play an important role in the structures and behaviors of IDPs.

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Section: Acknowledgmentsmentioning

confidence: 99%

SPEADI: Accelerated Analysis of IDP-Ion Interactions from MD-Trajectories

Bruyn

Dorn

Zimmermann

et al. 2023

Biology

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“…Kesselheim et al applied the Jülich Wizard for European Leadership Science (JUWELS) (https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/ juwels (accessed on 3 February 2023)) supercomputing cluster and booster to perform pretraining of the ResNet-152 DL network. Their goal was to highlight the speed-up achieved using the HPC resources and to eventually perform large-scale transfer learning using the publicly available COVIDx (https://www.kaggle.com/datasets/andyczhao/covidx-cxr2 (accessed on 3 February 2023)) dataset to develop a tool for rapid COVID-19 detection from Chest X-rays (CXRs) [24]. The researchers also discussed using their supercomputing resources to improve the available ML methods for RNA structure prediction.…”

Section: Introductionmentioning

confidence: 99%

Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome

et al. 2023

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Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the “Berlin Definition”. This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines.

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Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution

de Bruyn,

Dorn,

Rossetti

et al. 2024

J. Chem. Inf. Model.

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Serine 129 can be phosphorylated in pathological inclusions formed by the intrinsically disordered protein human αsynuclein (AS), a key player in Parkinson's disease and other synucleinopathies. Here, molecular simulations provide insight into the structural ensemble of phosphorylated AS. The simulations allow us to suggest that phosphorylation significantly impacts the structural content of the physiological AS conformational ensemble in aqueous solution, as the phosphate group is mostly solvated. The hydrophobic region of AS contains β-hairpin structures, which may increase the propensity of the protein to undergo amyloid formation, as seen in the nonphysiological (nonacetylated) form of the protein in a recent molecular simulation study. Our findings are consistent with existing experimental data with the caveat of the observed limitations of the force field for the phosphorylated moiety.

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JUWELS Booster – A Supercomputer for Large-Scale AI Research

Cited by 8 publications

References 36 publications

SPEADI: Accelerated Analysis of IDP-Ion Interactions from MD-Trajectories

SPEADI: Accelerated Analysis of IDP-Ion Interactions from MD-Trajectories

Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome

Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution

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