MU-LOC is a machine learning method for protein mitochondrial targeting prediction in plants. MU-LOC is trained using both deep neural network (DNN) and support vector machine (SVM) on a comprehensive and high-quality training set.
Different types of features are extracted from protein sequences, including amino acid composition, sequence position specific scoring matrix, and gene co-expression information.
This site is an online web service of MU-LOC, with an Amazon Web Services (AWS) implementation, a standalone version of the software, and all the datasets we have collected and generated.
Please input or upload your plant proteins below in the FASTA format, select
estimated specificity, and MU-LOC predictor (DNN or SVM). For a single protein, the FASTA header (the ">" line) can be omitted.
Please note that MU-LOC does not accept proteins shorter than 22 amino acids.
For online submission, due to the computational cost of running PSI-BLAST, we limit at most 200 protein sequences per submission. Also, since PSI-BLAST could take a while for some proteins, you can provide an email address where the prediction results will be sent to once the job is finished.
Here is a step-by-step instruction on how to use MU-LOC web service.
For a proteome wide prediction, please consider running MU-LOC on your local machine (download here) or Amazon Web Services (see instructions).
MU-LOC relies on some other tools and packages. Installing these dependencies could be complicated. As an alternative, we provide an implementation of MU-LOC on Amazon Web Services (AWS), where all the dependencies, environment variables, configuration files, etc., have been successfully set. You can run MU-LOC for proteome-wide predictions without worrying about installing any tools or setting paths for any data.
MU-LOC is available as an Amazon Machine Image (AMI: ami-12f37d6a) in the region of US West (Oregon). This page describes a detailed step-by-step instruction on how to launch this AMI and use MU-LOC as a cloud service.
Ning Zhang, R. Shyama Prasad Rao, Fernanda Salvato, Jesper F. Havelund, Ian Max Møller, Jay J. Thelen, and Dong Xu. MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants. Front Plant Sci. 2018 May 23;9:634. doi: 10.3389/fpls.2018.00634. PubMed PMID: 29875778.
If you have any questions or comments, feel free to contact Ning Zhang (firstname.lastname@example.org).