Opinion: Protein folds vs. protein folding: Differing questions, different challenges.
Opinion: Protein folds vs. protein folding: Differing questions, different challenges.
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DOI：
10.1073/pnas.2214423119
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发表时间：
2023-01-03
影响因子：
11.1
通讯作者：
Rose, George D.
中科院分区：
文献类型：
--
作者：
Chen, Shi-Jie;Hassan, Mubashir;Jernigan, Robert L.;Jia, Kejue;Kihara, Daisuke;Kloczkowski, Andrzej;Kotelnikov, Sergei;Kozakov, Dima;Liang, Jie;Liwo, Adam;Matysiak, Silvina;Meller, Jarek;Micheletti, Cristian;Mitchell, Julie C.;Mondal, Sayantan;Nussinov, Ruth;Okazaki, Kei-ichi;Padhorny, Dzmitry;Skolnick, Jeffrey;Sosnick, Tobin R.;Stan, George;Vakser, Ilya;Zou, Xiaoqin;Rose, George D.
利用深度学习人工智能(AI)进行蛋白质折叠预测已经改变了蛋白质结构预测领域(1-3)。通过结合物理和几何约束，特别是从蛋白质数据库(4)中提取的模式，这些机器学习算法可以预测原子分辨率或接近原子分辨率的蛋白质结构，并在几秒钟内完成。今天，这些计算方法已经解决了200多万个蛋白质结构，这些结构可以从AlphaFold蛋白质结构数据库(5)(https://alphafold.)中获得虾。交流电。UK/)。这一成就似乎更加不同寻常，因为几乎没有人认为这是可能的，也没有人预见到它的到来。深度学习AI当之无愧地被《科学》杂志评为2021年《年度突破》(6)。显然，深度学习人工智能代表了蛋白质折叠预测的重大进步。但这不是折叠预测。从蛋白质数据库(PDB)中提取的蛋白质模式提供了一个现成的“部件列表”，完全绕过了折叠过程。这些图案是“完全烘焙的”。也就是说，从PDB中的已解决结构中提取的图案是完全预先组织的；任何物理化学组织
Protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction (1–3). By combining physical and geometric constraints—and especially patterns extracted from the Protein Data Bank (4)—these machine learning algorithms can predict protein structures at or near atomic resolution and do so in seconds. Today, these computational methods have now solved more than 200 million protein structures, which are accessible from the AlphaFold Protein Structure Database (5)(https://alphafold. ebi. ac. uk/). This accomplishment seems all the more remarkable because few thought it possible or saw it coming. Deservedly, deep-learning AI was named Science magazine’s 2021 “breakthrough of the year”(6). Clearly, deep-learning AI represents a major advance in protein fold prediction.But this is not folding prediction. Patterns extracted from proteins in the Protein Data Bank (PDB) provide a ready “parts list,” circumventing the folding process entirely. These patterns are “fully baked.” That is, a pattern extracted from a solved structure in the PDB is fully preorganized; any physical–chemical organizing
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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：
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
DOI：
10.1126/science.abn2100
发表时间：
2022-07-22
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
通讯作者：
--
影响因子：
2.9
作者：
Rose, George D.
通讯作者：
Rose, George D.
DOI：
10.1073/pnas.1406845111
发表时间：
2014-08-05
影响因子：
11.1
作者：
Monteith, William B.;Pielak, Gary J.
通讯作者：
Pielak, Gary J.