Grabowski Marek, Macnar Joanna M, Cymborowski Marcin, Cooper David R, Shabalin Ivan G, Gilski Miroslaw, Brzezinski Dariusz, Kowiel Marcin, Dauter Zbigniew, Rupp Bernhard, Wlodawer Alexander, Jaskolski Mariusz, Minor Wladek
Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA.
College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland.
IUCrJ. 2021 Mar 26;8(Pt 3):395-407. doi: 10.1107/S2052252521003018. eCollection 2021 May 1.
As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.
作为全球抗击当前大流行的行动的一部分,在不到一年的时间里,已发表了近10万篇与COVID-19相关的论文,并且有近1000个由严重急性呼吸综合征冠状病毒2(SARS-CoV-2)编码的大分子模型存入了蛋白质数据库。新结构数据的大量涌现催生了多种致力于评估结构数据和模型的正确性及质量的资源。在此,描述了一种使用资源https://covid19.bioreproducibility.org评估大量此类数据的方法,该资源提供了一个模板,可用于应对未来生物医学危机而开展的大规模行动。更广泛地使用所描述的方法可以大幅减少信息噪声,并显著提高生物医学研究的可重复性。