PSCL: Predicting Protein Subcellular Localization Based on Optimal Functional Domains

Kai      Wang; Le-Le      Hu; Xiao-He      Shi; Ying-Song      Dong; Hai-Peng      Li; Tie-Qiao      Wen

Abstract

It is well known that protein subcellular localizations are closely related to their functions. Although many computational methods and tools are available from Internet, it is still necessary to develop new algorithms in this filed to gain a better understanding of the complex mechanism of plant subcellular localization. Here, we provide a new web server named PSCL for plant protein subcellular localization prediction by employing optimized functional domains. After feature optimization, 848 optimal functional domains from InterPro were obtained to represent each protein. By calculating the distances to each of the seven categories, PSCL showing the possibilities of a protein located into each of those categories in ascending order. Toward our dataset, PSCL achieved a first-order predicted accuracy of 75.7% by jackknife test. Gene Ontology enrichment analysis showing that catalytic activity, cellular process and metabolic process are strongly correlated with the localization of plant proteins. Finally, PSCL, a Linux Operate System based web interface for the predictor was designed and is accessible for public use at http://pscl.biosino.org/.

Keywords: Incremental Feature Selection (IFS), Jackknife Test, Minimum Redundancy Maximum Relevance (mRMR), Nearest Neighbor Algorithm, optimal functional domains, protein subcellular localization, eukaryotic cell, gene ontology, K-nearest neighbor, GPCR, PSCL, Sequence Coding, mRMR, PLD, Euk-mPLoc