Meng Fanchi, Uversky Vladimir, Kurgan Lukasz
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Curr Protoc Protein Sci. 2017 Apr 3;88:2.16.1-2.16.14. doi: 10.1002/cpps.28.
Computational prediction of intrinsically disordered proteins (IDPs) is a mature research field. These methods predict disordered residues and regions in an input protein chain. More than 60 predictors of IDPs have been developed. This unit defines computational prediction of intrinsic disorder, summarizes major types of predictors of disorder, and provides details about three accurate and recently released methods. We demonstrate the use of these methods to predict intrinsic disorder for several illustrative proteins, provide insights into how predictions should be interpreted, and quantify and discuss predictive performance. Predictions can be freely and conveniently obtained using webservers. We point to the availability of databases that provide access to annotations of intrinsic disorder determined by structural studies and putative intrinsic disorder pre-computed by computational methods. Lastly, we also summarize experimental methods that can be used to validate computational predictions. © 2017 by John Wiley & Sons, Inc.
内在无序蛋白质（IDP）的计算预测是一个成熟的研究领域。这些方法可预测输入蛋白质链中的无序残基和区域。目前已开发出60多种IDP预测器。本单元定义了内在无序的计算预测，总结了主要的无序预测器类型，并详细介绍了三种准确且近期发布的方法。我们展示了如何使用这些方法预测几种示例蛋白质的内在无序，深入了解应如何解读预测结果，并对预测性能进行量化和讨论。通过网络服务器可以免费且方便地获得预测结果。我们指出了一些数据库的可用性，这些数据库提供了通过结构研究确定的内在无序注释以及通过计算方法预先计算的假定内在无序。最后，我们还总结了可用于验证计算预测的实验方法。© 2017约翰威立国际出版公司
