Meng Qiaozhen, Chen Genlang, Lin Bin, Zheng Shixin, Lin Yulai, Tang Jijun, Guo Fei
IEEE/ACM Trans Comput Biol Bioinform. 2024 Nov-Dec;21(6):2025-2034. doi: 10.1109/TCBB.2024.3439541. Epub 2024 Dec 10.
Due to the broad-spectrum and high-efficiency antibacterial activity, antimicrobial peptides (AMPs) and their functions have been studied in the field of drug discovery. Using biological experiments to detect the AMPs and corresponding activities require a high cost, whereas computational technologies do so for much less. Currently, most computational methods solve the identification of AMPs and their activities as two independent tasks, which ignore the relationship between them. Therefore, the combination and sharing of patterns for two tasks is a crucial problem that needs to be addressed. In this study, we propose a deep learning model, called DMAMP, for detecting AMPs and activities simultaneously, which is benefited from multi-task learning. The first stage is to utilize convolutional neural network models and residual blocks to extract the sharing hidden features from two related tasks. The next stage is to use two fully connected layers to learn the distinct information of two tasks. Meanwhile, the original evolutionary features from the peptide sequence are also fed to the predictor of the second task to complement the forgotten information. The experiments on the independent test dataset demonstrate that our method performs better than the single-task model with 4.28% of Matthews Correlation Coefficient (MCC) on the first task, and achieves 0.2627 of an average MCC which is higher than the single-task model and two existing methods for five activities on the second task. To understand whether features derived from the convolutional layers of models capture the differences between target classes, we visualize these high-dimensional features by projecting into 3D space. In addition, we show that our predictor has the ability to identify peptides that achieve activity against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). We hope that our proposed method can give new insights into the discovery of novel antiviral peptide drugs.
由于抗菌肽（AMPs）具有广谱高效的抗菌活性，其在药物研发领域得到了广泛研究。利用生物学实验检测抗菌肽及其相应活性成本高昂，而计算技术的成本则低得多。目前，大多数计算方法将抗菌肽的识别及其活性视为两个独立的任务，忽略了它们之间的关系。因此，两个任务模式的组合与共享是一个亟待解决的关键问题。在本研究中，我们提出了一种名为DMAMP的深度学习模型，用于同时检测抗菌肽及其活性，该模型受益于多任务学习。第一阶段是利用卷积神经网络模型和残差块从两个相关任务中提取共享的隐藏特征。下一阶段是使用两个全连接层来学习两个任务的不同信息。同时，肽序列的原始进化特征也被输入到第二个任务的预测器中，以补充被遗忘的信息。在独立测试数据集上的实验表明，我们的方法在第一个任务上的马修斯相关系数（MCC）比单任务模型提高了4.28%，在第二个任务的五项活性上平均MCC达到0.2627，高于单任务模型和两种现有方法。为了了解模型卷积层导出的特征是否捕捉到目标类之间的差异，我们将这些高维特征投影到三维空间进行可视化。此外，我们还表明我们的预测器有能力识别对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）具有活性的肽。我们希望我们提出的方法能为新型抗病毒肽药物的发现提供新的见解。
