Author(s)

Yu-Tao Shao¹, Xin-Xin Liu¹, Zhe Lu¹, Kuo-Chen Chou^1,2

¹Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China.
²Gordon Life Science Institute, Boston, MA, USA.

Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of plant protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based plant protein subcellular localization predictor called “pLoc_Deep-mPlant” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 95% and its local accuracy is about 90% - 100%. Both have substantially exceeded the other existing state-of-the-art predictors. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mPlant/, by which the majority of experimental scientists can easily obtain their desired data without the need to go through the mathematical details.

Pandemic Coronavirus, Multi-Label System, Plant Proteins, Learning at Deeper Level, Five-Steps Rule, PseAAC

Shao, Y. , Liu, X. , Lu, Z. and Chou, K. (2020) pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning. Natural Science, 12, 237-247. doi: 10.4236/ns.2020.125021.