SEPPA 2.0--more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen.
Spatial Epitope Prediction server for Protein Antigens (SEPPA) has received lots of feedback since being published in 2009. In this improved version, relative ASA preference of unit patch and consolidated amino acid index were added as further classification parameters in addition to unit-triangle propensity and clustering coefficient which were previously reported. Then logistic regression model was adopted instead of the previous simple additive one. Most importantly, subcellular localization of protein antigen and species of immune host were fully taken account to improve prediction. The result shows that AUC of 0.745 (5-fold cross-validation) is almost the baseline performance with no differentiation like all the other tools. Specifying subcellular localization of protein antigen and species of immune host will generally push the AUC up. Secretory protein immunized to mouse can push AUC to 0.823. In this version, the false positive rate has been largely decreased as well. As the first method which has considered the subcellular localization of protein antigen and species of immune host, SEPPA 2.0 shows obvious advantages over the other popular servers like SEPPA, PEPITO, DiscoTope-2, B-pred, Bpredictor and Epitopia in supporting more specific biological needs. SEPPA 2.0 can be accessed at http://badd.tongji.edu.cn/seppa/. Batch query is also supported.
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Tao
T
Qi
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China zwcao@tongji.edu.cn.
Tianyi
T
Qiu
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Qingchen
Q
Zhang
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China Institute for Advanced Study of Translational Medicine, Tongji University, Shanghai 200092, China.
Kailin
K
Tang
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Yangyang
Y
Fan
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Jingxuan
J
Qiu
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Dingfeng
D
Wu
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Wei
W
Zhang
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Yanan
Y
Chen
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Jun
J
Gao
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Ruixin
R
Zhu
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China Shanghai Center for Bioinformation and Technology, 1278 Keyuan Road, Shanghai 201203, China.
Zhiwei
Z
Cao
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D000818:Animals; D000939:Epitopes / Q000737:chemistry* / Q000276:immunology; D006801:Humans; D020407:Internet; D016015:Logistic Models; D051379:Mice; D011487:Protein Conformation; D011506:Proteins / Q000032:analysis / Q000737:chemistry / Q000276:immunology*; D012984:Software
D000596:Amino Acids; D000939:Epitopes; D011506:Proteins