A high-precision hybrid algorithm for predicting eukaryotic protein subcellular localization

Abstract

MotivationSubcellular location plays an essential role in protein synthesis, transport, and secretion, thus it is an important step in understanding the mechanisms of trait-related proteins. Generally, homology methods provide reliable homology-based results with small E-values. We must resort to pattern recognition algorithms (SVM, Fisher discriminant, KNN, random forest, etc.) for proteins that do not share significant homologous domains with known proteins. However, satisfying results are seldom obtained.\n\nResultsHere, a novel hybrid method \"Basic Local Alignment Search Tool+Smith-Waterman+Needleman-Wunsch\" or BLAST+SWNW, has been obtained by integrating a loosened E-value Basic Local Alignment Search Tool (BLAST) with the Smith-Waterman (SW) and Needleman-Wunsch (NW) algorithms, and this method has been introduced to predict protein subcellular localization in eukaryotes. When tested on Dataset I and Dataset II, BLAST+SWNW showed an average accuracy of 97.18% and 99.60%, respectively, surpassing the performance of other algorithms in predicting eukaryotic protein subcellular localization.\n\nAvailability and ImplementationBLAST+SWNW is an open source collaborative initiative available in the GitHub repository (https://github.com/ZHANGDAHAN/BLAST-SWNW-for-SLP or http://202.206.64.158:80/link/72016CAC26E4298B3B7E0EAF42288935)\n\nContactzhaqi1972@163.com; zhangdahan@genetics.ac.cn\n\nSupplementary InformationSupplementary data are available at PLOS Computational Biology online.