Overview Update: Computational Prediction of Intrinsic Disorder in Proteins
Overview Update: Computational Prediction of Intrinsic Disorder in Proteins
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DOI：
10.1002/cpz1.802
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发表时间：
2023-06-01
期刊：
影响因子：
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通讯作者：
Kurgan，Lukasz
中科院分区：
文献类型：
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作者：
Uversky，Vladimir N.;Kurgan，Lukasz
有超过100种内在障碍的计算预测因子。这些方法直接从蛋白质序列预测氨基酸水平的疾病倾向。倾向可以用于注释假定的无序残基和区域。本单元提供了一个实用和全面的介绍基于序列的内在障碍预测。我们定义内在的障碍，解释格式的计算预测的障碍，并确定和描述几个准确的预测。我们还介绍了最近发布的内在障碍预测数据库，并使用一个说明性的例子来提供如何解释和组合预测的见解。最后，我们总结了可用于验证计算预测的关键实验方法。© 2023 Wiley Periodicals LLC.
There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid‐level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence‐based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.