Yang Jack Y, Yang Mary Qu
Harvard Medical School, Harvard University, Cambridge, MA 02115, USA.
BMC Genomics. 2008 Sep 16;9 Suppl 2(Suppl 2):S8. doi: 10.1186/1471-2164-9-S2-S8.
Many protein regions and some entire proteins have no definite tertiary structure, presenting instead as dynamic, disorder ensembles under different physiochemical circumstances. These proteins and regions are known as Intrinsically Unstructured Proteins (IUP). IUP have been associated with a wide range of protein functions, along with roles in diseases characterized by protein misfolding and aggregation.
Identifying IUP is important task in structural and functional genomics. We exact useful features from sequences and develop machine learning algorithms for the above task. We compare our IUP predictor with PONDRs (mainly neural-network-based predictors), disEMBL (also based on neural networks) and Globplot (based on disorder propensity).
We find that augmenting features derived from physiochemical properties of amino acids (such as hydrophobicity, complexity etc.) and using ensemble method proved beneficial. The IUP predictor is a viable alternative software tool for identifying IUP protein regions and proteins.
许多蛋白质区域以及一些完整蛋白质没有确定的三级结构，而是在不同的物理化学环境下呈现为动态的无序集合体。这些蛋白质和区域被称为内在无序蛋白质（IUP）。IUP与广泛的蛋白质功能相关，同时也在以蛋白质错误折叠和聚集为特征的疾病中发挥作用。
识别IUP是结构和功能基因组学中的一项重要任务。我们从序列中提取有用特征，并为上述任务开发机器学习算法。我们将我们的IUP预测器与PONDRs（主要基于神经网络的预测器）、disEMBL（也基于神经网络）和Globplot（基于无序倾向）进行比较。
我们发现增加从氨基酸物理化学性质（如疏水性、复杂性等）衍生的特征并使用集成方法被证明是有益的。该IUP预测器是识别IUP蛋白质区域和蛋白质的一个可行的替代软件工具。
