Accurate protein structure predictors use clusters of homologues, which disregard sequence specific effects. In this issue of Structure, Weißenow and colleagues report a deep learning-based tool, EMBER2, that efficiently predicts the distances in a protein structure from its amino acid sequence only. This approach should enable the analysis of mutation effects.

中文翻译：

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不需要同源物：蛋白质语言模型的结构预测

准确的蛋白质结构预测器使用同源物簇，忽略序列特异性效应。在本期Structure中，Weißenow 及其同事报告了一种基于深度学习的工具 EMBER2，该工具仅有效地预测蛋白质结构与其氨基酸序列的距离。这种方法应该能够分析突变效应。