Open-access data: A cornerstone for artificial intelligence approaches to protein structure prediction.

Open-access data: A cornerstone for artificial intelligence approaches to protein structure prediction.
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DOI:
10.1016/j.str.2021.04.010
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发表时间:
2021-06-03
期刊:
Structure (London, England : 1993)
影响因子:
--
通讯作者:
Berman HM
Berman HM
中科院分区:
其他
文献类型:
--
作者:
Burley SK;Berman HM

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蛋白质数据库(PDB)成立于1971年,旨在将生物大分子的三维(3D)结构存档为公共产品。50年后,PDB为全球数百万数据消费者提供了超过175,000种实验确定的蛋白质和核酸(DNA,RNA)及其相互复合物和小分子配体的结构。PDB数据用户在基础生物学,生物医学,生物工程,生物技术和能源科学方面工作,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,甚至社会科学领域。PDB中存储的大量3D结构数据支持了我们对蛋白质结构的理解取得了重大进展,并在人工智能方法和机器学习方法加速的蛋白质结构预测方面取得了突破。
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 machine learning methods.
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