Ebrahimi Tarki Fatemeh, Zarrabi Mahboobeh, Abdiali Ahya, Sharbatdar Mahkame
Department of Biotechnology, Faculty of Biological Sciences, Alzahra University, Tehran, Iran.
Department of Microbiology, Faculty of Biological Sciences, Alzahra University, Tehran, Iran.
Iran J Pharm Res. 2023 Sep 30;22(1):e138704. doi: 10.5812/ijpr-138704. eCollection 2023 Jan-Dec.
The rise of antibiotic resistance has become a major concern, signaling the end of the golden age of antibiotics. Bacterial biofilms, which exhibit high resistance to antibiotics, significantly contribute to the emergence of antibiotic resistance. Therefore, there is an urgent need to discover new therapeutic agents with specific characteristics to effectively combat biofilm-related infections. Studies have shown the promising potential of peptides as antimicrobial agents.
This study aimed to establish a cost-effective and streamlined computational method for predicting the antibiofilm effects of peptides. This method can assist in addressing the intricate challenge of designing peptides with strong antibiofilm properties, a task that can be both challenging and costly.
A positive library, consisting of peptide sequences with antibiofilm activity exceeding 50%, was assembled, along with a negative library containing quorum-sensing peptides. For each peptide sequence, feature vectors were calculated, while considering the primary structure, the order of amino acids, their physicochemical properties, and their distributions. Multiple supervised learning algorithms were used to classify peptides with significant antibiofilm effects for subsequent experimental evaluations.
The computational approach exhibited high accuracy in predicting the antibiofilm effects of peptides, with accuracy, precision, Matthew's correlation coefficient (MCC), and F1 score of 99%, 99%, 0.97, and 0.99, respectively. The performance level of this computational approach was comparable to that of previous methods. This study introduced a novel approach by combining the feature space with high antibiofilm activity.
In this study, a reliable and cost-effective method was developed for predicting the antibiofilm effects of peptides using a computational approach. This approach allows for the identification of peptide sequences with substantial antibiofilm activities for further experimental investigations. Accessible source codes and raw data of this study can be found online (hiABF), providing easy access and enabling future updates.
抗生素耐药性的上升已成为一个主要问题，标志着抗生素黄金时代的结束。对抗生素具有高度耐药性的细菌生物膜，是抗生素耐药性产生的重要原因。因此，迫切需要发现具有特定特性的新型治疗药物，以有效对抗与生物膜相关的感染。研究表明，肽作为抗菌剂具有广阔的应用前景。
本研究旨在建立一种经济高效且简化的计算方法，用于预测肽的抗生物膜效果。该方法有助于应对设计具有强大抗生物膜特性的肽这一复杂挑战，这一任务既具有挑战性又成本高昂。
构建了一个阳性文库，其中包含抗生物膜活性超过50%的肽序列，以及一个包含群体感应肽的阴性文库。对于每个肽序列，在考虑其一级结构、氨基酸顺序、理化性质及其分布的同时计算特征向量。使用多种监督学习算法对具有显著抗生物膜效果的肽进行分类，以便后续进行实验评估。
该计算方法在预测肽的抗生物膜效果方面表现出高精度，准确率、精确率、马修斯相关系数（MCC）和F1分数分别为99%、99%、0.97和0.99。该计算方法的性能水平与先前方法相当。本研究通过将特征空间与高抗生物膜活性相结合，引入了一种新方法。
在本研究中，开发了一种可靠且经济高效的计算方法，用于预测肽的抗生物膜效果。该方法能够识别具有显著抗生物膜活性的肽序列，以便进行进一步的实验研究。本研究的可访问源代码和原始数据可在线获取（hiABF），方便获取并便于未来更新。
