开放获取数据：人工智能蛋白质结构预测方法的基石。
Open-access data: A cornerstone for artificial intelligence approaches to protein structure prediction.
机构信息
Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; The Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
出版信息
Structure. 2021 Jun 3;29(6):515-520. doi: 10.1016/j.str.2021.04.010. Epub 2021 May 12.
The Protein Data Bank (PDB) was established in 1971 to archive three-dimensional (3D) structures of biological macromolecules as a public good. Fifty years later, the PDB is providing millions of data consumers around the world with open access to more than 175,000 experimentally determined structures of proteins and nucleic acids (DNA, RNA) and their complexes with one another and small-molecule ligands. PDB data users are working, teaching, and learning in fundamental biology, biomedicine, bioengineering, biotechnology, and energy sciences. They also represent the fields of agriculture, chemistry, physics and materials science, mathematics, statistics, computer science, and zoology, and even the social sciences. The enormous wealth of 3D structure data stored in the PDB has underpinned significant advances in our understanding of protein architecture, culminating in recent breakthroughs in protein structure prediction accelerated by artificial intelligence approaches and deep or machine learning methods.
蛋白质数据库（PDB）于 1971 年成立，旨在将生物大分子的三维（3D）结构作为公共产品进行存档。五十年后，PDB 为全球数百万数据使用者提供了开放获取超过 175,000 种蛋白质和核酸（DNA、RNA）及其复合物与小分子配体的实验确定结构的途径。PDB 数据使用者在基础生物学、生物医学、生物工程、生物技术和能源科学领域从事工作、教学和学习。他们还代表农业、化学、物理和材料科学、数学、统计学、计算机科学和动物学，甚至是社会科学领域。储存在 PDB 中的大量 3D 结构数据为我们深入了解蛋白质结构提供了支持，最终促成了近年来在蛋白质结构预测方面的突破，这些突破得益于人工智能方法和深度学习或机器学习方法的加速。
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