Yao Yujie, Zhang Daijun, Fan Henghui, Wu Ting, Su Yansen, Bin Yannan
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.
Department of Infectious Diseases & Anhui Province Key Laboratory of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
Probiotics Antimicrob Proteins. 2025 May 21. doi: 10.1007/s12602-025-10575-6.
Antimicrobial peptides (AMPs) demonstrate a broad spectrum of activities against various pathogens, thereby offering a promising strategy to mitigate the urgent challenge of antimicrobial resistance. Recent studies indicate that chemically modified AMPs (cmAMPs), which contain chemically modified amino acids, have the potential to alleviate the adverse effects commonly associated with conventional AMPs. Nevertheless, there remains a notable deficiency in computational methods specifically designed for the analysis and prediction of cmAMPs and their sub-function predictions. In this study, we proposed a two-layer model, termed as iCMAMP, aimed for the identification of cmAMPs and their sub-functional activities. The first layer, referred to as iCMAMP-1L, integrates three categories encompassing seven distinct groups of features, in conjunction with an ensemble method designed at enhancing predictive accuracy for cmAMPs. This ensemble approach effectively extracts relevant insights from a heterogeneous array of features sets while addressing potential dimensionality challenges. On the test dataset, iCMAMP-1L achieved an ACC of 0.934 and an MCC of 0.868, representing improvements of 3.4% and 6.8%, respectively, over AntiMPmod, which is the sole existing method for predicting cmAMPs. A comparative analysis between cmAMPs and their corresponding AMPs revealed that chemical modifications can significantly reduce hemolysis and toxicity associated with AMPs, while the functional characteristics of the peptides are primarily determined by their sequences. The second layer of our model, designated as iCMAMP-2L, employed a multi-label classification approach to predict the sub-functional activities of cmAMPs, with a specific focus on the dipeptide composition-based features. On the test dataset, iCMAMP-2L achieved an Accuracy of 0.390 and an Absolute true of 0.621. The data and Python code used in the iCMAMP model are available at https://github.com/swicher123/iCMAMP/tree/master .
抗菌肽（AMPs）对各种病原体具有广泛的活性，从而为应对抗菌药物耐药性这一紧迫挑战提供了一种有前景的策略。最近的研究表明，含有化学修饰氨基酸的化学修饰抗菌肽（cmAMPs）有可能减轻通常与传统抗菌肽相关的不良反应。然而，专门用于分析和预测cmAMPs及其亚功能预测的计算方法仍然存在显著不足。在本研究中，我们提出了一种两层模型，称为iCMAMP，旨在识别cmAMPs及其亚功能活性。第一层，称为iCMAMP-1L，整合了三类包含七个不同特征组的特征，并结合了一种旨在提高cmAMPs预测准确性的集成方法。这种集成方法有效地从异构特征集数组中提取相关见解，同时解决潜在的维度挑战。在测试数据集上，iCMAMP-1L的ACC为0.934，MCC为0.868，分别比预测cmAMPs的唯一现有方法AntiMPmod提高了3.4%和6.8%。cmAMPs与其相应的AMPs之间的比较分析表明，化学修饰可以显著降低与AMPs相关的溶血和毒性，而肽的功能特征主要由其序列决定。我们模型的第二层，称为iCMAMP-2L，采用多标签分类方法来预测cmAMPs的亚功能活性，特别关注基于二肽组成的特征。在测试数据集上，iCMAMP-2L的准确率为0.390，绝对真值为0.621。iCMAMP模型中使用的数据和Python代码可在https://github.com/swicher123/iCMAMP/tree/master上获取。
