A Webserver for ER Protein Prediction
The endoplasmic reticulum plays an important role in protein folding and processing of newly synthesized proteins. It also provides the pace for the degradation of the mis-folded or incorrectly folded proteins. Besides it acts as multifunctional organelle, which control a wide range of signaling mechanism important for cellular processes. So in context of endoplasmic reticulum, it is one of the most important sub-cellular compartments and hence prediction of endoplasmic reticular proteins is one of the major challenges in the field of bioinformatics. This study describes a novel method, ERPred, developed for the prediction of endoplasmic reticular proteins with very high accuracy. First we use amino acid composition, pseudo-amino acid composition and dipeptide composition as SVM input and found maximum accuracy 73.34%, 74.85% and 72.28% respectively. The accuracy of prediction was further increase when we use split amino acid composition (C-terminal and remaining residues, N-terminal and remaining residues and C-terminal, N-terminal and remaining residues) and found 71.07%, 81.19% and 81.42% respectively. When we use C-terminal, N-terminal and remaining residues as SVM input, we found better accuracy than other prediction features. So in this study, we developed split amino acid composition (C-terminal, N-terminal and remaining) and support vector machine based novel method to predict endoplasmic reticular proteins and we found maximum accuracy 81.42% with 0.42 MCC value.
Please cite: Kumar R, Kumari B, Kumar M. Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine. PeerJ. 2017;5:e3561. doi:10.7717/peerj.3561