Gram-positive bacterial protein subcellular localization prediction using features fusion strategy

Prediction of protein subcellular localization is the most challenging field for the researchers because of its importance in different branch of molecular biology and drug discovery. Last two decades, a large number of machine learning approaches have been tested into sequence based features for the prediction of subcellular localization. Single features like amino acid composition (AAC), pseudo amino acid composition (PseAAC) and physiochemical property model (PPM)) contain insufficient information due to their single perspectives. To overcome this problem, the main contribution of our work is to propose two feature fusion representations AACPPM and PAACPPM which can be fused PPM with AAC and PseAAC respectively. Support Vector Machine (SVM) is applied as a classifier on to both single and fused feature representations of Gram-positive bacterial dataset. The actual accuracy of AACPPM is 72.4% which is 2% higher than single feature representations and 6% higher than X. Qu et al [1]. The locative accuracy of both AACPPM and PAACPPM is 73.2% which is also 2% higher than single feature representations.

I. Introduction

As the second largest element of living body protein acts as a worker of living activities and responsible for whole life process. In different field of Molecular Biology like protein function prediction, role of biological process, drug discovery, genome annotation and so on, protein subcellular localization plays a vital role. Due to this importance of protein with correct location researchers feel attraction to predict its localization. A traditional biochemical experiment for protein subcellular localization predictions are expensive and faces uncertainty in time boundary to fulfil the research demands. More likely, the explosion of protein sequences makes it more challenging. This in fact shows that, computational methods are required as an alternative choice for the prediction of protein subcellular localization automatically and more accurately.