Publications

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1687 Publications visible to you, out of a total of 1687

Abstract

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Authors: Javier Morán-Fraile, Alexander Holas, Friedrich K. Röpke, Rüdiger Pakmor, Fabian R. N. Schneider

Date Published: 1st Mar 2024

Publication Type: Journal

Abstract

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Authors: V. A. Bronner, F. R. N. Schneider, Ph. Podsiadlowski, F. K. Röpke

Date Published: 1st Mar 2024

Publication Type: Journal

Abstract

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Authors: M. Golebiewski, G. Bader, P. Gleeson, T. E. Gorochowski, S. M. Keating, M. Konig, C. J. Myers, D. P. Nickerson, B. Sommer, D. Waltemath, F. Schreiber

Date Published: 1st Mar 2024

Publication Type: Journal

Abstract (Expand)

INTRODUCTION: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. METHODS: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. RESULTS: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DISCUSSION: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Authors: A. Niarakis, M. Ostaszewski, A. Mazein, I. Kuperstein, M. Kutmon, M. E. Gillespie, A. Funahashi, M. L. Acencio, A. Hemedan, M. Aichem, K. Klein, T. Czauderna, F. Burtscher, T. G. Yamada, Y. Hiki, N. F. Hiroi, F. Hu, N. Pham, F. Ehrhart, E. L. Willighagen, A. Valdeolivas, A. Dugourd, F. Messina, M. Esteban-Medina, M. Pena-Chilet, K. Rian, S. Soliman, S. S. Aghamiri, B. L. Puniya, A. Naldi, T. Helikar, V. Singh, M. F. Fernandez, V. Bermudez, E. Tsirvouli, A. Montagud, V. Noel, M. Ponce-de-Leon, D. Maier, A. Bauch, B. M. Gyori, J. A. Bachman, A. Luna, J. Pinero, L. I. Furlong, I. Balaur, A. Rougny, Y. Jarosz, R. W. Overall, R. Phair, L. Perfetto, L. Matthews, D. A. B. Rex, M. Orlic-Milacic, L. C. M. Gomez, B. De Meulder, J. M. Ravel, B. Jassal, V. Satagopam, G. Wu, M. Golebiewski, P. Gawron, L. Calzone, J. S. Beckmann, C. T. Evelo, P. D'Eustachio, F. Schreiber, J. Saez-Rodriguez, J. Dopazo, M. Kuiper, A. Valencia, O. Wolkenhauer, H. Kitano, E. Barillot, C. Auffray, R. Balling, R. Schneider

Date Published: 29th Feb 2024

Publication Type: Journal

Abstract

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Authors: Robin Ruff, Patrick Reiser, Jan Stühmer, Pascal Friederich

Date Published: 20th Feb 2024

Publication Type: Journal

Abstract (Expand)

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Authors: Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn’ova, Pascal Friederich, Frauke Gräter

Date Published: 14th Feb 2024

Publication Type: Journal

Abstract (Expand)

Abstract The Legacy Survey of Space and Time at the Vera C. Rubin Observatory is poised to observe thousands of quasars using the Deep Drilling Fields (DDF) across six broadband filters over a decade.and filters over a decade. Understanding quasar accretion disk (AD) time delays is pivotal for probing the physics of these distant objects. Pozo Nuñez et al. has recently demonstrated the feasibility of recovering AD time delays with accuracies ranging from 5% to 20%, depending on the quasar’s redshift and time sampling intervals. Here we reassess the potential for measuring AD time delays under the current DDF observing cadence, which is placeholder until a final cadence is decided. We find that contrary to prior expectations, achieving reliable AD time delay measurements for quasars is significantly more challenging, if not unfeasible, due to the limitations imposed by the current observational strategies.

Authors: F. Pozo Nuñez, B. Czerny, S. Panda, A. Kovacevic, W. Brandt, K. Horne

Date Published: 14th Feb 2024

Publication Type: Journal

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