Bacteria-Specific Features Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods

We developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physio-chemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient anti-bacterial drug therapies.本研究开发了一种全新的计算方法，可依托靶向大肠杆菌（E. coli）的特定多肽关联数据集，训练多款监督式机器学习模型。本研究采用套索回归（LASSO regression）与支持向量机（Support Vector Machine）技术，从超过1500项理化描述符中筛选出最为关键的特征，以此将多肽划分为抗大肠杆菌抗菌肽或对大肠杆菌无效的多肽。随后，本研究采用支持向量分类器、逻辑回归与随机森林三种方法，对活性与非活性抗菌肽（Antimicrobial Peptides，AMPs）开展分类任务。本项计算研究可为设计更高效的抗菌药物治疗方案提供科学参考依据。