Target-AMP: Computational prediction of antimicrobial peptides by coupling sequential information with evolutionary profile. (December 2022) - Jan, Asad; Hayat, Maqsood; Wedyan, Mohammad; Alturki, Ryan; Gazzawe, Foziah; Ali, Hashim; Alarfaj, Fawaz Khaled
Abstract: Antimicrobial peptides (AMPs) are gaining a lot of attention as cutting-edge treatments for many infectious disorders. The effectiveness of AMPs against bacteria, fungi, and viruses has persisted for a long period, making them the greatest option for addressing the growing problem of antibiotic resistance. Due to their wide-ranging actions, AMPs have become more prominent, particularly in therapeutic applications. The prediction of AMPs has become a difficult task for academics due to the explosive increase of AMPs documented in databases. Wet-lab investigations to find anti-microbial peptides are exceedingly costly, time-consuming, and even impossible for some species. Therefore, in order to choose the optimal AMPs candidate before to the in-vitro trials, an efficient computational method must be developed. In this study, an effort was made to develop a machine learning-based classification system that is effective, accurate, and can distinguish between anti-microbial peptides. The position-specific-scoring-matrix (PSSM), Pseudo Amino acid composition, di-peptide composition, and combination of these three were utilized in the suggested scheme to extract salient aspects from AMPs sequences. The classification techniques K-nearest neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) were employed. On the independent dataset and training dataset, the accuracy levels achieved by the suggested predictor (Target-AMP) are 97.07% and 95.71%, respectively. The results show that, when compared to other techniques currently used in the literature, our Target-AMP had the best success rate. Highlights: Intelligent Computational model is proposed for Antimicrobial peptides. DPC, PseAAC and PSSM techniques are used as feature extraction schemes. Hybrid space is designed by combining all spaces. Various classification algorithms are analyzed. SVM obtained high performance.