2206A Student Center
Targeting and translocation of proteins to the appropriate subcellular compartments is crucial for cell organization and function. Some 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 for any given protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools is unsatisfactory. We present a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected subcellular localization data for plant proteins from databases and the literature, and extracted different types of features from the training data, including amino acid composition, protein sequence profile, and gene co-expression information. We then trained deep neural networks for predicting plant mitochondrial proteins. Benchmarked on an independent dataset, our method achieves considerable improvements over existing tools in predicting mitochondria-localized proteins in plants. We improved the true positive rate by 10-30% over three of the state-of-the-art tools under similar specificity levels. We also applied our method to predict candidate mitochondrial proteins on the whole proteome of Arabidopsis and potato.