The problem of predicting a protein’s 3D structure from its primary amino acid sequence is a longstanding challenge in structural biology. Recently, approaches like alphafold have achieved remarkable performance on this task by combining deep learning techniques with coevolutionary data from multiple sequence alignments of related protein sequences. The use of coevolutionary information is critical to these models’ accuracy, and without it their predictive performance drops considerably. In living cells, however, the 3D structure of a protein is fully determined by its primary sequence and the biophysical laws that cause it to fold into a low-energy configuration. Thus, it should be possible to predict a protein’s structure from only its primary sequence by learning an approximate biophysical energy function. We provide evidence that alphafold has learned such an energy function, and uses coevolution data to solve the global search problem of finding a low-energy conformation. We demonstrate that alphafold’slearned energy function can be used to rank the quality of candidate protein structures with state-of-the-art accuracy, without using any coevolution data. Finally, we explore several applications of this energy function, including the prediction of protein structures without multiple sequence alignments.

中文翻译：

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使用 AlphaFold 的蛋白质模型准确性的最先进估计

从蛋白质的一级氨基酸序列预测蛋白质的 3D 结构是结构生物学中长期存在的挑战。最近，像lpha f这样的方法old 通过将深度学习技术与来自相关蛋白质序列的多序列比对的共同进化数据相结合，在这项任务上取得了显着的表现。共同进化信息的使用对这些模型的准确性至关重要，如果没有它，它们的预测性能会大大下降。然而，在活细胞中，蛋白质的 3D 结构完全由其一级序列和导致其折叠成低能结构的生物物理学定律决定。因此，通过学习近似的生物物理能量函数，应该可以仅从其一级序列预测蛋白质的结构。我们提供证据表明a lpha fold学习了这样一个能量函数，并利用协同进化数据解决了寻找低能构象的全局搜索问题。我们证明，α 折叠的学习能量函数可用于以最先进的精度对候选蛋白质结构的质量进行排名，而无需使用任何协同进化数据。最后，我们探索了这种能量函数的几种应用，包括在没有多序列比对的情况下预测蛋白质结构。