Journal article
IMLP, a predictor for internal matrix targeting-like sequences in mitochondrial proteins
University of Kaiserslautern1
Department of Health Technology, Technical University of Denmark2
Bioinformatics, Department of Health Technology, Technical University of Denmark3
Bayesian Modeling & Molecular Evolution, Bioinformatics, Department of Health Technology, Technical University of Denmark4
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.
Language: | English |
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Year: | 2021 |
Pages: | 937-943 |
ISSN: | 14374315 and 14316730 |
Types: | Journal article |
DOI: | 10.1515/hsz-2021-0185 |
ORCIDs: | 0000-0002-2198-5262 , 0000-0002-7832-7658 , 0000-0003-2081-4506 , 0000-0003-3925-6778 and Nielsen, Henrik |