Overlay databank unlocks data-driven analyses of biomolecules for all

Anne M. Kiirikki, Hanne S. Antila, Lara S. Bort, Pavel Buslaev, Fernando Favela-Rosales, Tiago Mendes Ferreira, Patrick F.J. Fuchs, Rebeca Garcia-Fandino, Ivan Gushchin, Batuhan Kav, Norbert Kučerka, Patrik Kula, Milla Kurki, Alexander Kuzmin, Anusha Lalitha, Fabio Lolicato, Jesper J. Madsen, Markus S. Miettinen, Cedric Mingham, Luca MonticelliRicky Nencini, Alexey M. Nesterenko, Thomas J. Piggot, Ángel Piñeiro, Nathalie Reuter, Suman Samantray, Fabián Suárez-Lestón, Reza Talandashti, O. H.Samuli Ollila*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Article number1136
JournalNature Communications
Volume15
Issue number1
DOIs
Publication statusPublished - Dec 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Artificial Intelligence
  • Cell Membrane
  • Membrane Lipids
  • Molecular Dynamics Simulation
  • Machine Learning

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