Overlay databank unlocks data-driven analyses of biomolecules for all
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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Nature CommunicationsAuthors
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2024Copyright
© The Author(s) 2024
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.
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Nature Publishing GroupISSN Search the Publication Forum
2041-1723Keywords
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https://converis.jyu.fi/converis/portal/detail/Publication/207129903
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Research Council of FinlandFunding program(s)
Academy Project, AoF; Postdoctoral Researcher, AoFAdditional information about 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-UNAMDGTIC-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_266 7887), 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. ...License
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