Matrix targeting sequences (MTSs) direct proteins from the cytosol into mitochondria. Efficient targeting often relies on internal matrix targeting-like sequences (iMTS-Ls) which share structural features with MTSs. Predicting iMTS-Ls was tedious and required multiple tools and webservices. We present iMLP, a deep learning approach for the prediction of iMTS-Ls in protein sequences. A recurrent neural network has been trained to predict iMTS-L propensity profiles for protein sequences of interest. The iMLP predictor considerably exceeds the speed of existing approaches. Expanding on our previous work on iMTS-L prediction, we now serve an intuitive iMLP webservice available at http://iMLP.bio.uni-kl.de and a stand-alone command line tool for power user in addition.

中文翻译：

---

iMLP，线粒体蛋白内部基质靶向样序列的预测因子

基质靶向序列 (MTS) 将细胞质中的蛋白质引导到线粒体中。有效的靶向通常依赖于与 MTS 共享结构特征的内部矩阵靶向序列 (iMTS-Ls)。预测 iMTS-L 很繁琐，需要多种工具和 Web 服务。我们提出了 iMLP，这是一种用于预测蛋白质序列中 iMTS-Ls 的深度学习方法。已经训练了一个循环神经网络来预测感兴趣的蛋白质序列的 iMTS-L 倾向谱。iMLP 预测器大大超过了现有方法的速度。扩展我们之前关于 iMTS-L 预测的工作，我们现在提供直观的 iMLP Web 服务，可在http://iMLP.bio.uni-kl.de另外还有一个供高级用户使用的独立命令行工具。