用于预测抗菌肽(ABP)的大多数现有方法大多设计为靶向革兰氏阳性或革兰氏阴性细菌。在这项研究中,我们描述了一种方法,使我们能够预测针对革兰氏阳性的ABPs,革兰氏阴性,和革兰氏可变细菌。首先,我们开发了一种基于比对的方法,使用BLAST来识别ABP,结果敏感性较差.其次,我们采用了基于基序的方法来预测ABP,并获得了高精度和低灵敏度。为了解决灵敏度差的问题,我们开发了使用机器/深度学习技术预测ABP的无对齐方法。在无对齐方法的情况下,我们利用了广泛的肽特征,包括不同类型的成分,末端残基的二元分布,和fastText单词嵌入。在这项研究中,一种五重交叉验证技术已用于在训练数据集上构建机器/深度学习模型。在训练和独立数据集之间没有共同肽的独立数据集上评估这些模型。我们基于机器学习的模型使用末端残基的氨基酸二元图谱开发,对于革兰氏阳性,最大AUC为0.93、0.98和0.94,革兰氏阴性,和革兰氏可变细菌,分别,在一个独立的数据集上。与独立数据集上的现有方法相比,我们的方法比现有方法性能更好。一个用户友好的Web服务器,独立包装和pip包装已被开发,以促进基于肽的治疗。
Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.