Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/115606
Title: Overlay databank unlocks data-driven analyses of biomolecules for all
Author(s): Kiirikki, Anne M.
Antila, Hanne S.
Bort, Lara S.
Buslaev, Pavel
Favela-Rosales, Fernando
Ferreira, Tiago M.Look up in the Integrated Authority File of the German National Library
Fuchs, Patrick F. J.
Garcia-Fandino, Rebeca
Gushchin, Ivan
Kav, BatuhanLook up in the Integrated Authority File of the German National Library
Kučerka, Norbert
Kula, Patrik
Kurki, Milla
Kuzmin, Alexander
Issue Date: 2024
Type: Article
Language: English
Abstract: Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank—a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.
URI: https://opendata.uni-halle.de//handle/1981185920/117561
http://dx.doi.org/10.25673/115606
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Journal Title: Nature Communications
Publisher: Nature Publishing Group UK
Publisher Place: [London]
Volume: 15
Issue: 1
Original Publication: 10.1038/s41467-024-45189-z
Appears in Collections:Open Access Publikationen der MLU

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