Improved protein structure prediction using potentials from deep learning
Improved protein structure prediction using potentials from deep learning
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DOI：
10.1038/s41586-019-1923-7
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发表时间：
2020-01-30
期刊：
影响因子：
64.8
通讯作者：
Hassabis, Demis
中科院分区：
文献类型：
--
作者：
Senior, Andrew W.;Evans, Richard;Hassabis, Demis
蛋白质结构预测可用于根据蛋白质的氨基酸序列确定蛋白质的三维形状(1)。这个问题具有根本性的重要性，因为蛋白质的结构在很大程度上决定了它的功能(2)；然而，蛋白质的结构很难通过实验确定。最近，利用遗传信息取得了相当大的进展。通过分析同源序列中的协变，可以推断哪些氨基酸残基相互接触，这有助于预测蛋白质结构(3)。在这里，我们展示了我们可以训练神经网络来准确预测残基对之间的距离，这比接触预测传达了更多关于结构的信息。利用这些信息，我们构造了一个可以准确描述蛋白质形状的平均力势(4)。我们发现，所产生的势可以通过一个简单的梯度下降算法来优化，以生成结构，而不需要复杂的采样过程。由此产生的系统名为AlphaFold，即使对于同源序列较少的序列也能实现高精度。在最近的蛋白质结构预测关键评估(5)(CASP13)中，AlphaFold为43个自由建模域中的24个创建了高精度结构(模板建模(TM)得分(6)为0.7或更高)，而次好的方法使用采样和联系信息，仅在43个域中的14个域实现了这种准确性。AlphaFold代表了蛋白质结构预测方面的一大进步。我们希望这种提高的准确性能够深入了解蛋白质的功能和故障，特别是在没有实验确定同源蛋白质结构的情况下(7)。
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence(1). This problem is of fundamental importance as the structure of a protein largely determines its function(2); however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures(3). Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force(4) that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction(5) (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores(6) of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined(7).