Highly accurate protein structure prediction with AlphaFold.
Highly accurate protein structure prediction with AlphaFold.
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DOI：
10.1038/s41586-021-03819-2
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发表时间：
2021-08
期刊：
影响因子：
64.8
通讯作者：
Hassabis D
中科院分区：
文献类型：
--
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
蛋白质对生命至关重要，了解它们的结构可以促进对它们功能的机械理解。通过巨大的实验努力，已经确定了大约100，000种独特蛋白质的结构，但这只是数十亿已知蛋白质序列的一小部分。确定一个蛋白质结构所需的数月至数年的艰苦努力，使结构覆盖率受到考验。需要精确的计算方法来解决这一差距，并使大规模的结构生物信息学。仅根据蛋白质的氨基酸序列来预测蛋白质的三维结构--“蛋白质折叠问题”的结构预测部分--已经是一个重要的开放式研究问题超过50年。尽管最近取得了进展，但现有的方法远远达不到原子精度，特别是当没有同源结构时。在这里，我们提供了第一个计算方法，可以定期预测蛋白质结构的原子精度，即使在没有类似的结构是已知的情况下。我们在具有挑战性的第14届蛋白质结构预测关键评估（CASP 14）中验证了我们基于神经网络的模型AlphaFold的完全重新设计的版本，在大多数情况下证明了与实验结构竞争的准确性，并大大优于其他方法。AlphaFold最新版本的基础是一种新型的机器学习方法，该方法将有关蛋白质结构的物理和生物学知识，利用多序列比对，融入深度学习算法的设计中。AlphaFold预测蛋白质结构的准确性与实验结构在大多数情况下使用一种新的深度学习架构。
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort, the structures of around 100,000 unique proteins have been determined, but this represents a small fraction of the billions of known protein sequences. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’—has been an important open research problem for more than 50 years. Despite recent progress, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14), demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
14.9
作者：
wwPDB consortium
通讯作者：
wwPDB consortium
影响因子：
4.7
作者：
ElGamacy, Mohammad;Coles, Murray;Lupas, Andrei N.
通讯作者：
Lupas, Andrei N.
影响因子：
48
作者：
Alley, Ethan C.;Khimulya, Grigory;Church, George M.
通讯作者：
Church, George M.
影响因子：
2.9
作者：
Abriata, Luciano A.;Tamo, Giorgio E.;Dal Peraro, Matteo
通讯作者：
Dal Peraro, Matteo