Manaz Kaleel1,2, Yandan Zheng3, Jialiang Chen3, Xuanming Feng3, Jeremy C Simpson4,5, Gianluca Pollastri1,2, Catherine Mooney1,3. 1. School of Computer Science. 2. UCD Institute for Discovery, University College Dublin, Dublin, Ireland. 3. Beijing-Dublin International College, Beijing University of Technology, Chaoyang, China. 4. Conway Institute of Biomolecular and Biomedical Research. 5. School of Biology and Environmental Science, University College Dublin, Dublin, Ireland.
Abstract
MOTIVATION: The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. RESULTS: Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75-0.86 outperforming the other state-of-the-art web servers we tested. AVAILABILITY AND IMPLEMENTATION: SCLpred-EMS is freely available for academic users at http://distilldeep.ucd.ie/SCLpred2/. CONTACT: catherine.mooney@ucd.ie.
MOTIVATION: The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. RESULTS: Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75-0.86 outperforming the other state-of-the-art web servers we tested. AVAILABILITY AND IMPLEMENTATION: SCLpred-EMS is freely available for academic users at http://distilldeep.ucd.ie/SCLpred2/. CONTACT: catherine.mooney@ucd.ie.