Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins. In this paper, a new predictor called ”Gpos-mPLoc”, is developed for identifying the subcellular localization of Gram positive bacterial proteins by fusing the information of gene ontology, as well as the functional domain information and sequential evolution information. Compared with the old Gpos-PLoc, the new predictor is much more powerful and flexible. Particularly, it also has the capacity to deal with multiple-location proteins as indicated by the character ”m” in front of ”PLoc” of its name. For a newly-constructed stringent benchmark dataset in which none of included proteins has > 25% pairwise sequence identity to any other in a same subset (location), the overall jackknife success rate achieved by Gpos-mPLoc was 82.2%, which was about 10% higher than the corresponding rate by the Gpos-PLoc. As a user friendly web-server, Gpos-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/Gpos-multi/.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Shen, Yinan; Tang, Jijun; Guo, Fei: Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC (2019)
- Zhang, Shengli; Duan, Xin: Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC (2018)
- Shatabda, Swakkhar; Saha, Sanjay; Sharma, Alok; Dehzangi, Abdollah: iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features (2017)
- Huang, Chao; Yuan, Jing-Qi: Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions (2013)
- Hu, Yinxia; Li, Tonghua; Sun, Jiangming; Tang, Shengnan; Xiong, Wenwei; Li, Dapeng; Chen, Guanyan; Cong, Peisheng: Predicting Gram-positive bacterial protein subcellular localization based on localization motifs (2012)
- Mei, S.; Wang, F.; Zhou, S.: Gene ontology based transfer learning for protein subcellular localization (2011) ioport
- Georgiou, D. N.; Karakasidis, T. E.; Nieto, Juan J.; Torres, A.: A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets (2010)