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      Limiting open science? Three approaches to bottom-up governance of dual-use research of concern

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          ABSTRACT

          Governing dual-use research of concern (DURC) in the life sciences has become difficult owing to the diversification of scientific domains, digitalization of potential threats, and the proliferation of actors. This paper proposes three approaches to realize bottom-up governance of DURC from laboratory operation to institutional decision-making levels. First, a technological approach can predict and monitor the dual-use nature of the research target pathogens and their information. Second, an interactive approach is proposed in which diverse stakeholders proactively discuss and examine dual-use issues through research practice. Third, a personnel approach can identify the right persons involved in DURC. These approaches suggest that, going beyond self-governance by researchers, collaborative and networked governance involving diverse actors should become essential. This mode of governance can also be seen in light of the management of research use. Therefore, program design by funding agencies and publication screening by journal publishers continuously contribute to governance at the meso-level. Bottom-up governance may be realized by using an appropriately integrated design of these three approaches at the micro-level, such as dual-use prediction and monitoring, stakeholder dialogue, and background checks. Given that the term ‘open science’ has been promoted to the research community as part of top-down governance, paying due attention on site to research subjects, research practices, and persons involved in research will provide an opportunity to develop a more socially conscious open science.

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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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            Developing a framework for responsible innovation

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              Science for the post-normal age

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                Author and article information

                Journal
                Pathog Glob Health
                Pathog Glob Health
                Pathogens and Global Health
                Taylor & Francis
                2047-7724
                2047-7732
                4 October 2023
                2024
                4 October 2023
                : 118
                : 4
                : 285-294
                Affiliations
                [a ]Innovation System Research Center, Kwansei Gakuin University; , Hyogo, Japan
                [b ]National Defense Medical College; , Saitama, Japan
                [c ]Graduate School of Science, Hokkaido University; , Hokkaido, Japan
                [d ]Center for Clinical and Translational Research, Kyushu University Hospital; , Fukuoka, Japan
                [e ]Center for Advanced Biomedical Sciences, School of Advanced Science and Engineering, Waseda University; , Tokyo, Japan
                [f ]Management Department of Biosafety, Laboratory Animal, and Pathogen Bank, National Institute of Infectious Diseases; , Tokyo, Japan
                [g ]Uehiro Research Division for iPS Cell Ethics, Center for iPS Cell Research and Application, Kyoto University; , Kyoto, Japan
                Author notes
                CONTACT Go Yoshizawa gy20@ 123456jcom.home.ne.jp Innovation System Research Center, Kwansei Gakuin University; , Hyogo, Japan
                Author information
                https://orcid.org/0000-0002-1436-8493
                Article
                2265626
                10.1080/20477724.2023.2265626
                11234915
                37791645
                eb6d9101-43cc-45d3-8764-5b2658eac03c
                © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

                History
                Page count
                Figures: 1, References: 80, Pages: 10
                Categories
                Review Article
                Review Articles

                Infectious disease & Microbiology
                biosecurity,enhanced potentially pandemic pathogens (eppps),preprint screening,genomic data management,gain-of-function (gof),responsible research and innovation (rri)

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