Computational Systems Biology, University of Kaiserslautern, Paul-Ehrlich-Strasse 23, D-67663 Kaiserslautern, Germany.
Department of Health Technology, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
Biol Chem. 2021 Jul 5;402(8):937-943. doi: 10.1515/hsz-2021-0185. Print 2021 Jul 27.
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.
基质靶向序列（MTS）将蛋白质从细胞质靶向到线粒体。有效的靶向通常依赖于内部基质靶向样序列（iMTS-L），它与 MTS 共享结构特征。预测 iMTS-L 繁琐且需要多个工具和网络服务。我们提出了 iMLP，这是一种用于预测蛋白质序列中 iMTS-L 的深度学习方法。已经训练了一个递归神经网络来预测目标蛋白质序列的 iMTS-L 倾向分布。iMLP 预测器的速度明显快于现有方法。在我们之前关于 iMTS-L 预测的工作的基础上，我们现在提供了一个直观的 iMLP 网络服务，网址是 http://iMLP.bio.uni-kl.de，此外还提供了一个独立的命令行工具供高级用户使用。
