AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.
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AlphaFold蛋白质结构数据库：通过高精度模型大规模扩展蛋白质序列空间的结构覆盖范围。
DOI：
10.1093/nar/gkab1061
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发表时间：
2022-01-07
影响因子：
14.9
通讯作者：
Velankar S
中科院分区：
文献类型：
--
作者：
Varadi M;Anyango S;Deshpande M;Nair S;Natassia C;Yordanova G;Yuan D;Stroe O;Wood G;Laydon A;Žídek A;Green T;Tunyasuvunakool K;Petersen S;Jumper J;Clancy E;Green R;Vora A;Lutfi M;Figurnov M;Cowie A;Hobbs N;Kohli P;Kleywegt G;Birney E;Hassabis D;Velankar S
AlphaFold蛋白质结构数据库（AlphaFold DB，https://alphafold.ebi.ac.uk）是一个开放的，广泛的高精度蛋白质结构预测数据库。由DeepMind的AlphaFold v2.0提供支持，它使已知蛋白质序列空间的结构覆盖范围得到了前所未有的扩展。AlphaFold DB提供对预测原子坐标、每残基和成对模型置信度估计值以及预测对齐误差的编程访问和交互式可视化。AlphaFold DB的初始版本包含21个模型生物蛋白质组中超过360，000个预测结构，这些结构将很快扩展到涵盖UniRef90数据集中的大多数（超过1亿个）代表性序列。AlphaFold蛋白质结构数据库（AlphaFold DB，https://alphafold.ebi.ac.uk）是一个广泛的，高度精确的蛋白质结构模型的公共数据库。这些模型是AlphaFold2的产品，AlphaFold2是DeepMind开发的人工智能算法。AlphaFold使科学家能够研究前所未有数量的蛋白质结构。我们在这里描述的数据库提供了对这些预测模型及其准确性信息的访问。第一版AlphaFold DB包含21种生物必需物种的超过360，000个模型。
The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set. The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an extensive, public database of highly accurate protein structure models. The models are the products of AlphaFold2, an Artificial Intelligence algorithm developed by DeepMind. AlphaFold enabled scientists to investigate an unprecedented number of protein structures. The database we describe here provides access to these predicted models and information on their accuracy. The first version of AlphaFold DB contains over 360,000 models of 21 biologically essential species.
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影响因子：
14.9
作者：
Blum M;Chang HY;Chuguransky S;Grego T;Kandasaamy S;Mitchell A;Nuka G;Paysan-Lafosse T;Qureshi M;Raj S;Richardson L;Salazar GA;Williams L;Bork P;Bridge A;Gough J;Haft DH;Letunic I;Marchler-Bauer A;Mi H;Natale DA;Necci M;Orengo CA;Pandurangan AP;Rivoire C;Sigrist CJA;Sillitoe I;Thanki N;Thomas PD;Tosatto SCE;Wu CH;Bateman A;Finn RD
通讯作者：
Finn RD
影响因子：
14.9
作者：
Mistry J;Chuguransky S;Williams L;Qureshi M;Salazar GA;Sonnhammer ELL;Tosatto SCE;Paladin L;Raj S;Richardson LJ;Finn RD;Bateman A
通讯作者：
Bateman A
影响因子：
64.8
作者：
Tunyasuvunakool K;Adler J;Wu Z;Green T;Zielinski M;Žídek A;Bridgland A;Cowie A;Meyer C;Laydon A;Velankar S;Kleywegt GJ;Bateman A;Evans R;Pritzel A;Figurnov M;Ronneberger O;Bates R;Kohl SAA;Potapenko A;Ballard AJ;Romera-Paredes B;Nikolov S;Jain R;Clancy E;Reiman D;Petersen S;Senior AW;Kavukcuoglu K;Birney E;Kohli P;Jumper J;Hassabis D
通讯作者：
Hassabis D
影响因子：
16.6
作者：
Hiranuma N;Park H;Baek M;Anishchenko I;Dauparas J;Baker D
通讯作者：
Baker D
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D