A1 Journal article (refereed)
Overlay databank unlocks data-driven analyses of biomolecules for all (2024)

Kiirikki, A. M., Antila, H. S., Bort, L. S., Buslaev, P., Favela-Rosales, F., Ferreira, T. M., Fuchs, P. F. J., Garcia-Fandino, R., Gushchin, I., Kav, B., Kučerka, N., Kula, P., Kurki, M., Kuzmin, A., Lalitha, A., Lolicato, F., Madsen, J. J., Miettinen, M. S., Mingham, C., . . . Ollila, O. H. S. (2024). Overlay databank unlocks data-driven analyses of biomolecules for all. Nature Communications, 15, Article 1136. https://doi.org/10.1038/s41467-024-45189-z

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Publication details

All authors or editors: Kiirikki, Anne M.; Antila, Hanne S.; Bort, Lara S.; Buslaev, Pavel; Favela-Rosales, Fernando; Ferreira, Tiago Mendes; Fuchs, Patrick F. J.; Garcia-Fandino, Rebeca; Gushchin, Ivan; Kav, Batuhan; et al.

Journal or series: Nature Communications

eISSN: 2041-1723

Publication year: 2024

Publication date: 07/02/2024

Volume: 15

Article number: 1136

Publisher: Nature Publishing Group

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1038/s41467-024-45189-z

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/93483

Web address where publication is available: https://chemrxiv.org/engage/chemrxiv/article-details/656dc63929a13c4d47a02aa6

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 datadriven 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.

Keywords: databases; computational chemistry; biophysics; artificial intelligence

Free keywords: biological physics; computational chemistry; computational platforms and environments; databases; membrane biophysics

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Preliminary JUFO rating: 3