Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou’s 5-Steps Rule

Abstract

The function of a protein is closely tied to its subcellular location. Identifying the subcellular location of proteins is a crucial step to understand their functions. However, determining the subcellular location of proteins experimentally is time-consuming and costly. Therefore, developing effective computational methods to predict the subcellular positions of proteins is a hotspot in bioinformatics. Though many models have been proposed to improve the prediction accuracy of protein subcellular localization, there are still several shortcomings: (1) numerous methods ignore the multi-site proteins; (2) high dimensional features bring the burden to the construction of the prediction model. In this work, we proposed a method to predict the subcellular location of bacterial proteins with both single and multiple locations. Two features based on evolutionary information are extracted to solve the multi-site prediction problem, of which one is a 190-dimensional feature vector from absolute entropy correlation analysis (AECA-PSSM) and another is a 480-dimensional feature vector extracted using discrete wavelet transform (PSSM-DWT). After combining both proposed features, multi-label linear discriminant analysis (MLDA) is employed to transform the high-dimensional feature space into a lower-dimensional space. Multi-label k-nearest neighbors algorithm (ML-KNN) is utilized to predict the subcellular location of both single-site and multi-site proteins. Experimental results on Gram-positive dataset and Gram-negative dataset show the effectiveness of the proposed method. INDEX TERMS Subcellular location, absolute entropy correlation analysis, discrete wavelet transform, multi-label linear discriminant analysis, multi-label k-nearest neighbors.

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier

“…Second, the protein not only exists in one SCL, but also may exist in two or multiple SCL. The prediction method for a single protein site ignores the situation of two or more subcellular locations ( Du et al , 2020 ). Finally, the high-dimensional space formed by multi-information fusion increases the interference of redundant information on the prediction results ( Yu et al , 2018 ).…”