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      Overlay databank unlocks data-driven analyses of biomolecules for all

      research-article
      1 , 2 , 3 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , , 11 , 12 , 13 , 14 , 15 , , 16 , 17 , 18 , 19 , 20 , 2 , 21 , 22 , 23 , 24 , 25 , 1 , 26 , 21 , 22 , 27 , 28 , 21 , 22 , 11 , 29 , 10 , 28 , 30 , 21 , 22 , 1 , 31 ,
      Nature Communications
      Nature Publishing Group UK
      Membrane biophysics, Computational chemistry, Databases, Computational platforms and environments, Biological physics

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          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.

          Abstract

          In this work, the authors report NMR lipids Databank to promote decentralised sharing of biomolecular molecular dynamics (MD) simulation data with an overlay design. Programmatic access enables analyses of rare phenomena and advances the training of machine learning models.

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          Most cited references68

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            The FAIR Guiding Principles for scientific data management and stewardship

            There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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              Accurate prediction of protein structures and interactions using a 3-track neural network

              DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction assessment conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
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                Author and article information

                Contributors
                samuli.ollila@helsinki.fi
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                7 February 2024
                7 February 2024
                2024
                : 15
                : 1136
                Affiliations
                [1 ]University of Helsinki, Institute of Biotechnology, ( https://ror.org/040af2s02) Helsinki, Finland
                [2 ]Department of Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces, ( https://ror.org/00pwgnh47) 14424 Potsdam, Germany
                [3 ]Department of Biomedicine, University of Bergen, ( https://ror.org/03zga2b32) 5020 Bergen, Norway
                [4 ]University of Potsdam, Institute of Physics and Astronomy, ( https://ror.org/03bnmw459) 14476 Potsdam-Golm, Germany
                [5 ]Nanoscience Center and Department of Chemistry, University of Jyväskylä, ( https://ror.org/05n3dz165) 40014 Jyväskylä, Finland
                [6 ]GRID grid.484694.3, ISNI 0000 0004 5988 7021, Departamento de Ciencias Básicas, , Tecnológico Nacional de México - ITS Zacatecas Occidente, ; Sombrerete, 99102 Zacatecas Mexico
                [7 ]NMR group - Institute for Physics, Martin Luther University Halle-Wittenberg, ( https://ror.org/05gqaka33) 06120 Halle (Saale), Germany
                [8 ]GRID grid.463975.a, Sorbonne Université, Ecole Normale Supérieure, , PSL University, CNRS, Laboratoire des Biomolécules (LBM), ; F-75005 Paris, France
                [9 ]Université Paris Cité, ( https://ror.org/05f82e368) F-75006 Paris, France
                [10 ]GRID grid.11794.3a, ISNI 0000000109410645, Center for Research in Biological Chemistry and Molecular Materials (CiQUS), , Universidade de Santiago de Compostela, ; E-15782 Santiago de Compostela, Spain
                [11 ]Institute of Biological Information Processing: Structural Biochemistry (IBI-7), Forschungszentrum Jülich, ( https://ror.org/02nv7yv05) 52428 Jülich, Germany
                [12 ]ariadne.ai GmbH (Germany), Häusserstraße 3, 69115 Heidelberg, Germany
                [13 ]Department of Physical Chemistry of Drugs, Faculty of Pharmacy, Comenius University Bratislava, ( https://ror.org/0587ef340) 832 32 Bratislava, Slovakia
                [14 ]Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, ( https://ror.org/04nfjn472) Flemingovo nám. 542/2, CZ-16610 Prague, Czech Republic
                [15 ]School of Pharmacy, University of Eastern Finland, ( https://ror.org/00cyydd11) 70211 Kuopio, Finland
                [16 ]GRID grid.121334.6, ISNI 0000 0001 2097 0141, Institut Charles Gerhardt Montpellier (UMR CNRS 5253), , Université Montpellier, ; Place Eugène Bataillon, 34095 Montpellier, Cedex 05 France
                [17 ]Heidelberg University Biochemistry Center, ( https://ror.org/038t36y30) 69120 Heidelberg, Germany
                [18 ]Department of Physics, University of Helsinki, ( https://ror.org/040af2s02) FI-00014 Helsinki, Finland
                [19 ]Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, ( https://ror.org/032db5x82) 33612 Tampa, FL USA
                [20 ]Center for Global Health and Infectious Diseases Research, Global and Planetary Health, College of Public Health, University of South Florida, ( https://ror.org/032db5x82) 33612 Tampa, FL USA
                [21 ]Department of Chemistry, University of Bergen, ( https://ror.org/03zga2b32) 5007 Bergen, Norway
                [22 ]Department of Informatics, Computational Biology Unit, University of Bergen, ( https://ror.org/03zga2b32) 5008 Bergen, Norway
                [23 ]GRID grid.11500.35, ISNI 0000 0000 8919 8412, Hochschule Mannheim, , University of Applied Sciences, ; 68163 Mannheim, Germany
                [24 ]GRID grid.25697.3f, ISNI 0000 0001 2172 4233, University of Lyon, CNRS, Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), ; F-69007 Lyon, France
                [25 ]Institut National de la Santé et de la Recherche Médicale (INSERM), ( https://ror.org/02vjkv261) Lyon, France
                [26 ]Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, ( https://ror.org/040af2s02) 00014 Helsinki, Finland
                [27 ]Chemistry, University of Southampton, ( https://ror.org/01ryk1543) Highfield, SO17 1BJ Southampton UK
                [28 ]Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, ( https://ror.org/030eybx10) E-15782 Santiago de Compostela, Spain
                [29 ]Institute of Biotechnology, RWTH Aachen University, ( https://ror.org/04xfq0f34) Worringerweg 3, 52074 Aachen, Germany
                [30 ]MD.USE Innovations S.L., Edificio Emprendia, 15782 Santiago de Compostela, Spain
                [31 ]VTT Technical Research Centre of Finland, ( https://ror.org/04b181w54) Espoo, Finland
                Author information
                http://orcid.org/0000-0002-0687-6594
                http://orcid.org/0000-0001-7117-994X
                http://orcid.org/0000-0003-4990-373X
                http://orcid.org/0000-0001-8311-6057
                http://orcid.org/0000-0003-3945-3691
                http://orcid.org/0000-0002-2817-661X
                http://orcid.org/0000-0001-7537-0549
                http://orcid.org/0000-0003-1411-9080
                http://orcid.org/0000-0002-3999-4722
                http://orcid.org/0000-0002-6352-4595
                http://orcid.org/0000-0003-3361-9582
                http://orcid.org/0000-0003-0843-667X
                http://orcid.org/0000-0002-8728-1006
                Article
                45189
                10.1038/s41467-024-45189-z
                10850068
                38326316
                bef75312-be0f-4761-b886-fed15b81db6a
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 June 2023
                : 17 January 2024
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                membrane biophysics,computational chemistry,databases,computational platforms and environments,biological physics

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