Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

Abstract

There are a lot of bacteria in the environment, and Gram-positive bacteria are the most common ones. Some Gram-positive bacteria are very harmful to the human body, so it is significant to predict Gram-positive bacterial protein subcellular location. And identification of Gram-positive bacterial protein subcellular location is important for developing effective drugs. In this paper, a new Gram-positive bacterial protein subcellular location dataset was established. The amino acid composition, the gene ontology annotation information, the hydropathy dipeptide composition information, the amino acid dipeptide composition information, and the autocovariance average chemical shift information were selected as characteristic parameters, then these parameters were combined. The locations of Gram-positive bacterial proteins were predicted by the Support Vector Machine (SVM) algorithm, and the overall accuracy (OA) reached 86.1% under the Jackknife test. The overall accuracy (OA) in our predictive model was higher than those in existing methods. This improved method may be helpful for protein function prediction.

Predicting Cell Wall Lytic Enzymes Using Combined Features

“…In order to evaluate the predictive capability and reliability of our model, the sensitivity (Sn), specificity (Sp), Matthew’s correlation coefficient (MCC), and accuracy (Acc) ( Bustamam et al, 2019 ; Cheng, 2019 ; Cheng et al, 2019 ; Feng et al, 2019 ; Malebary et al, 2019 ; Chen et al, 2020 ; Li and Gao, 2020 ; Wang et al, 2020b ) were measured and defined by:…”