improved prediction of protein structural features by integrated deep learning

improved prediction of protein structural features by integrated deep learning
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通过集成深度学习改进蛋白质结构特征的预测

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通讯作者：
P. Marcatili
P. Marcatili
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作者：
Michael Schantz;1. Klausen;Martin Closter;2. Jespersen;Henrik Nielsen;Kamilla K Jensen;V. Jurtz;C. Sønderby;Morten Otto;Alexander Sommer;Ole Winther;Morten Nielsen;Bent Petersen;P. Marcatili

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从一级序列预测蛋白质局部结构特征的能力对于在缺乏实验结构信息的情况下揭示其功能至关重要。两个主要因素影响潜在预测工具的效用：它们的准确性必须能够提取感兴趣蛋白质的可靠结构信息，并且它们的运行时间必须低，以跟上以不断增加的速度生成的测序数据。
The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unravelling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed.