SCLAP: An Adaptive Boosting Method for Predicting Subchloroplast Localization of Plant Proteins

Abstract

Chloroplasts are organelles found in plant system and other photosynthetic eukaryotes. Since a large number of essential pathways take place in this organelle, proteins in the chloroplast are considered vital. Therefore, knowledge about the subchloroplast localization of the chloroplast proteins will provide precise information in understanding its interaction within the chloroplast. To address this, an AdaBoost-based prediction system to predict the subchloroplast localization of chloroplast proteins (SCLAP) was developed. It integrates three different sequence-based features for prediction, beside the addition of similarity-based module for significant improvement in prediction performance. SCLAP achieved an overall accuracy of 89.3% in jackknife cross-validation test against the benchmark dataset, which was considered highest among existing tools and equals the SubIdent, and 85.9% accuracy in new error-free dataset. Evaluation of SCLAP with the independent dataset, five-fold cross-validation, and their corresponding receiver operator characteristic curve analysis demonstrated the SCLAP's efficient performance. SCLAP is the webserver implementation of our algorithm written in PERL. The server can be used to predict the subchloroplast localization of chloroplast proteins ( http://sclap.bicpu.edu.in/predict.php ).