Predicting Cell Wall Lytic Enzymes Using Combined Features - Jing, Xiao-Yang; Li, Feng-Min (Frontiers in Bioengineering and Biotechnology, 2021)
As the overuse of antibiotics, people worried that the existing antibiotics will be ineffective against these pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed, the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC) and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC and Acc were 99.35%, 99.02%, 0.98, 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.