Target-AMP: Computational prediction of antimicrobial peptides by coupling sequential information with evolutionary profile

抗菌肽 伪氨基酸组成 支持向量机 计算机科学 人工智能 机器学习 随机森林 任务(项目管理) k-最近邻算法 抗菌剂 生物 工程类 微生物学 生物化学 系统工程 二肽
作者
Asad Jan,Maqsood Hayat,Mohammad Wedyan,Ryan Alturki,Foziah Gazzawe,Hashim Ali,Fawaz Khaled Alarfaj
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:151: 106311-106311 被引量:11
标识
DOI:10.1016/j.compbiomed.2022.106311
摘要

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.
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