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 Monticelli
  • Ricky 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

21 Citations (Scopus)
35 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

Funding

P.B. was supported by the Academy of Finland (Grants 311031 and 342908). F.F.-R. acknowledges Tecnológico Nacional de México, Dirección General de Asuntos del Personal Académico (DGAPA), Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT) 101923, CONACyT Ciencia de Frontera 74884, for financial support and Miztli-Dirección de Cómputo y de Tecnologías de Información y Comunicación (DGTIC) - Universidad Nacional Autónoma de México (UNAM) (Project LANCAD-UNAM-DGTIC-057) facilities for computing time allocation. T.M.F. greatly acknowledges financial support by the Ministry of Economics, Science and Digitalisation of the State of Saxony-Anhalt. R.G.-F., A.P. and F.S.-L. thank Centro de Supercomputación de Galicia for computational support; R.G.-F. thanks Ministerio de Ciencia, Innovación y Universidades for a ”Ramón y Cajal” contract (RYC-2016-20335), and Spanish Agencia Estatal de Investigación (AEI) and the ERDF (RTI2018-098795-A-I00, PDC2022-133402-I00), Xunta de Galicia and the ERDF (ED431F 2020/05, 02_IN606D_2022_2667887 and Centro singular de investigación de Galicia accreditation 2016-2019, ED431G/09); A.P. thanks Spanish Agencia Estatal de Investigación (AEI) and the ERDF (PID2019-111327GB-I00, PDC2022-133402-I00), Xunta de Galicia and the ERDF (ED431B 2022/36, 02_IN606D_2022_2667887); F.S.-L. thanks Axencia Galega de Innovación for his predoctoral contract (02_IN606D_2022_2667887), and Spanish Agencia Estatal de Investigación (AEI) and the ERDF (PID2019-111327GB-I00). N.K. was supported by the Slovak Scientific Grant Agency (VEGA 1/0223/20). M.K. acknowledges CSC — IT Center for Science for computational resources and thanks Finnish Cultural Foundation and the UEF Doctoral Programme for financial support. F.L. was supported by the Deutsche Forschungsgemeinschaft (DFG LO 2821/1–1). M.S.M. was supported by the Trond Mohn Foundation (BFS2017TMT01). L.M. acknowledges funding by the Institut National de la Santé et de la Recherche Médicale (INSERM). N.R. and R.T. acknowledge funding from Norges Forskningsråd (#288008 and #335772) and computational resources provided by Sigma2 - the National Infrastructure for High-Performance Computing and Data Storage in Norway. O.H.S.O, A.M.K, N.R. and L.S.B. acknowledge CSC — IT Center for Science for computational resources and Academy of Finland (grant nos. 315596, 319902 & 345631) for financial support. We acknowledge all the NMRlipids Project contributors for making development of the NMRlipids Databank possible.

Keywords

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

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