Resources for computational prediction of intrinsic disorder in proteins
Resources for computational prediction of intrinsic disorder in proteins
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DOI：
10.1016/j.ymeth.2022.03.018
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发表时间：
2022-05-27
期刊：
影响因子：
4.8
通讯作者：
Kurgan，Lukasz
中科院分区：
文献类型：
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作者：
Kurgan，Lukasz
经过 40 多年的研究，内在紊乱预测领域的研究人员开发了 100 多个计算预测器。这篇综述通过强调准确和流行的疾病预测因素并介绍支持疾病预测的收集、解释和应用的广泛实用资源，提供了该领域的整体视角。这些资源包括加快收集多种疾病预测的元网络服务器、预先计算的疾病预测的大型数据库（可以简化预测的收集，特别是针对大型蛋白质数据集）以及现代质量评估工具。后一种方法有助于识别特定蛋白质序列的准确预测，减少与假定疾病的使用相关的不确定性。我们总共回顾了十一个预测因子、四个元网络服务器、三个数据库和两个质量评估工具，所有这些都可以方便地在线获取。我们还对疾病预测和质量评估工具的未来发展提供了展望。这个有用资源的综合工具箱的可用性应该会刺激疾病预测在许多领域的应用的进一步增长，包括合理药物设计、系统医学、结构生物信息学和结构基因组学。
With over 40 years of research, researchers in the intrinsic disorder prediction field developed over 100 computational predictors. This review offers a holistic perspective of this field by highlighting accurate and popular disorder predictors and introducing a wide range of practical resources that support collection, interpretation and application of disorder predictions. These resources include meta webservers that expedite collection of multiple disorder predictions, large databases of pre-computed disorder predictions that ease collection of predictions particularly for large datasets of proteins, and modern quality assessment tools. The latter methods facilitate identification of accurate predictions in a specific protein sequence, reducing uncertainty associated to the use of the putative disorder. Altogether, we review eleven predictors, four meta webservers, three databases and two quality assessment tools, all of which are conveniently available online. We also offer a perspective on future developments of the disorder prediction and the quality assessment tools. The availability of this comprehensive toolbox of useful resources should stimulate further growth in the application of the disorder predictions across many areas including rational drug design, systems medicine, structural bioinformatics and structural genomics.