iMLP, a predictor for internal matrix targeting-like sequences in mitochondrial proteins

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.

Automated summary

iMLP is a deep learning approach for predicting iMTS-Ls in protein sequences, significantly improving speed. This method provides an intuitive web service at http://iMLP.bio.uni-kl.de and a stand-alone command line tool for power users.