Predicting Sub-cellular Location of Proteins Based on Hierarchical Clustering and Hidden Markov Models

Abstract

Sub-cellular localization prediction is an important step for inferring protein functions. Several strategies have been developed in the recent years to solve this problem, from alignment-based solutions to feature-based solutions. However, under some identity thesholds, these kind of approaches fail to detect homologous sequences, achieving predictions with low specificity and sensitivity. Here, a novel methodology is proposed for classifying proteins with low identity levels. This approach implements a simple, yet powerful assumption that employs hierarchical clustering and hidden Markov models, obtaining high performance on the prediction of four different sub-cellular localizations.