MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants.

ABSTRACT: Targeting and translocation of proteins to the appropriate subcellular compartments are crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial prot

SUBMITTER: Zhang N 

PROVIDER: S-EPMC5974146 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature