TY - JOUR
T1 - Overlay databank unlocks data-driven analyses of biomolecules for all
AU - Kiirikki, Anne M.
AU - Antila, Hanne S.
AU - Bort, Lara S.
AU - Buslaev, Pavel
AU - Favela-Rosales, Fernando
AU - Ferreira, Tiago Mendes
AU - Fuchs, Patrick F.J.
AU - Garcia-Fandino, Rebeca
AU - Gushchin, Ivan
AU - Kav, Batuhan
AU - Kučerka, Norbert
AU - Kula, Patrik
AU - Kurki, Milla
AU - Kuzmin, Alexander
AU - Lalitha, Anusha
AU - Lolicato, Fabio
AU - Madsen, Jesper J.
AU - Miettinen, Markus S.
AU - Mingham, Cedric
AU - Monticelli, Luca
AU - Nencini, Ricky
AU - Nesterenko, Alexey M.
AU - Piggot, Thomas J.
AU - Piñeiro, Ángel
AU - Reuter, Nathalie
AU - Samantray, Suman
AU - Suárez-Lestón, Fabián
AU - Talandashti, Reza
AU - Ollila, O. H.Samuli
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Cell Membrane
KW - Membrane Lipids
KW - Molecular Dynamics Simulation
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85184724502&partnerID=8YFLogxK
U2 - 10.1038/s41467-024-45189-z
DO - 10.1038/s41467-024-45189-z
M3 - Article
C2 - 38326316
AN - SCOPUS:85184724502
SN - 2041-1723
VL - 15
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 1136
ER -