:Predicting the Phosphorylation Sites in Human Protein
:Identification of hormone-binding protein
:Identification of apolipoproteins
:Prediction of cell-penetrating peptides
:Identification of immunoglobulins
: Predicting anticancer peptide
: Predicting the antioxidant proteins
: Predicting the cancerlectins
: Predicting the types of J-proteins
The web-server was developed to identify the types of J-proteins by using reduced amino acid alphabet. The overall accuracy was over 94% by using jackknife cross-validation.
iHSP-PseRAAAC : Identifying the heat shock protein families
using pseudo reduced amino acid alphabet composition
: Prediction of subGolgi locations of proteins
: Prediction of submitochondria locations of proteins
: Prediction of subchloroplast locations of proteins
: Discriminating acidic enzymes from alkaline enzymes
: Identification of thermophilic proteins
The web-server ThermoPred was developed to identify thermophilic proteins based on the sequence information. The 93.8% thermophilic proteins and 92.7% non-thermophilic proteins can be correctly predicted by use of jackknife cross-validation.
: Identifying the subfamilies of voltage-gated potassium channe
: Predicting the types of conotoxins in targeting ion channels
The web-server iCTX-Type was developed to predict the types of conotoxins in targeting ion channels. The overall accuracy of 91.07% was achieved by using jackknife cross-validation.
: Identification of ion channels and their types
: Prediction of bacterial cell wall lyase
The web-server Lypred was developed to predict Bacterial Cell Wall Lyase via pseudo amino acid composition. The average accuracy of 84.82% with the auROC of 0.926 was achieved in jackknife cross-validation.
: Prediction of bacteriophage enzymes and hydrolases
: Prediction of bacteriophage proteins located in host cell
: Identification of phage virion proteins
Mycosub: Predicting subcellular localization of mycobacterial proteins
The web-server MycoSub was used to predict the subcellular localizations of mycobacterial proteins based on optimal tripeptide compositions. We achieved overall accuracy of 89.71% with average accuracy of 81.12% in jackknife cross-validation.
MycoSec: Identification of mycobacterial secretory proteins
The web-server MycoSec was developed to predict secretory proteins in mycobacterium tuberculosis with pseudo amino acid composition. The averaged accuracy of 87.18% with the AUC of 0.93 was achieved in jackknife cross-validation.
MycoMemSVM: Identification of mycobacterial membrane proteins and their types