Intrinsically disordered or unstructured proteins (or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination of intrinsic disorder and the exponential increase of unannotated protein sequences, developing complementary computational prediction methods has been an active area of research for several decades. Here, we employed an ensemble of deep Squeeze-and-Excitation residual inception and long short-term memory (LSTM) networks for predicting protein intrinsic disorder with input from evolutionary information and predicted one-dimensional structural properties. The method, called SPOT-Disorder2, offers substantial and consistent improvement not only over our previous technique based on LSTM networks alone, but also over other state-of-the-art techniques in three independent tests with different ratios of disordered to ordered amino acid residues, and for sequences with either rich or limited evolutionary information. More importantly, semi-disordered regions predicted in SPOT-Disorder2 are more accurate in identifying molecular recognition features (MoRFs) than methods directly designed for MoRFs prediction. SPOT-Disorder2 is available as a web server and as a standalone program at https://sparks-lab.org/server/spot-disorder2/.
现在已知相当多的蛋白质并没有内在的结构，这些被称为固有无序（intrinsic disorder）的蛋白质有许多重要的功能，并在许多疾病中起着关键的作用。但是，哪个蛋白质，或者某个蛋白质的哪个区域是固有无序的，并没有什么简单、便宜、快速的实验方法来测定。随着未被注释蛋白质数量的指数性增长，用计算方法来预测成为必要的补充手段。这里，我们采用了深层压缩和激发（deep Squeeze-and-Excitation）和长短期记忆（LSTM）神经网络的集合，利用进化信息和预测的一维结构特性来预测蛋白质的结构区和固有无序区。这种被称为SPOT-Disorder2的方法不仅仅极大地改进了我们先前基于LSTM网络的SPOT-Disorder方法，而且也超过其它目前最好的方法。更重要的是，它还能直接用于预测固有无序区内的与其它分子相互作用的功能区（MoRF），比其它专门预测功能区的方法更准确。SPOT-Disorder2作为网上服务器和独立程序可以在https://sparks-lab.org/server/spot-disorder2/上获得。





PDF全文下载地址:

http://gpb.big.ac.cn/articles/download/753