Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolutionary analysis via machine learning to have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences where templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structures, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement.

中文翻译：

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通过将机器学习与基于物理的精炼相结合，可以得到高精度的蛋白质结构。

长期以来，蛋白质结构预测可作为确定实验结构的替代方法，尤其是通过基于相关序列模板的同源性建模进行预测。最近，基于距离限制的模型通过机器学习进行了协同进化分析，从而大大扩展了无需模板即可预测序列结构的能力。一种这样的方法，AlphaFold，在有模板但不直接使用此类信息的序列上也表现良好。在这里，我们展示了将AlphaFold的基于机器学习的模型与通过分子动力学模拟进行的基于物理的最新改进相结合，可以进一步改善预测，从而胜过在最近一轮的CASP中测试的任何其他预测方法。生成的模型具有高度准确的全局和局部结构，