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

被引:21
作者
Burley, Stephen K. [1 ,2 ,3 ,4 ,5 ]
Berman, Helen M. [1 ,2 ,6 ]
机构
[1] Rutgers State Univ, Inst Quantitat Biomed, Res Collaboratory Struct Bioinformat Prot Data Ba, Piscataway, NJ 08854 USA
[2] Rutgers State Univ, Dept Chem & Chem Biol, Piscataway, NJ 08854 USA
[3] Rutgers State Univ, Rutgers Canc Inst New Jersey, New Brunswick, NJ 08903 USA
[4] Univ Calif San Diego, Res Collaboratory Struct Bioinformat Prot Data Ba, San Diego Supercomp Ctr, La Jolla, CA 92093 USA
[5] Univ Calif San Diego, Skaggs Sch Pharm & Pharmaceut Sci, La Jolla, CA 92093 USA
[6] Univ Southern Calif, Michelson Ctr Convergent Biosci, Bridge Inst, Los Angeles, CA 90089 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
DATA-BANK; MACROMOLECULAR STRUCTURES; VALIDATION; BIOLOGY; TOOLS; DIFFRACTION; IMPACT; NMR;
D O I
10.1016/j.str.2021.04.010
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:515 / 520
页数:6
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