DMAMP: A deep-learning model for detecting antimicrobial peptides and their multi-activities.

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-06 DOI:10.1109/TCBB.2024.3439541
Qiaozhen Meng, Genlang Chen, Shixin Zheng, Yulai Lin, Bin Liu, Jijun Tang, Fei Guo
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Abstract

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.

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DMAMP:用于检测抗菌肽及其多重活性的深度学习模型。
由于具有广谱高效的抗菌活性,抗菌肽(AMPs)及其功能已被用于药物发现领域的研究。利用生物学实验检测 AMPs 及其相应活性需要高昂的成本,而利用计算技术则只需较低的成本。目前,大多数计算方法将 AMPs 及其活性的识别作为两个独立的任务来解决,忽略了它们之间的关系。因此,两个任务的模式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 AMPs 和活动,该模型得益于多任务学习。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隐藏特征。下一阶段是利用两个全连接层来学习两个任务的不同信息。同时,肽序列中的原始进化特征也会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,我们的方法在第一项任务上的马修斯相关系数(MCC)为 4.28%,优于单任务模型;在第二项任务的五项活动中,平均马修斯相关系数为 0.2627,高于单任务模型和两种现有方法。为了了解从卷积层模型中得出的特征是否捕捉到了目标类别之间的差异,我们将这些高维特征投影到三维空间,使其可视化。此外,我们还展示了我们的预测器能够识别出具有抗严重急性呼吸系统综合症冠状病毒-2(SARS-CoV-2)活性的多肽。我们希望我们提出的方法能为新型抗病毒多肽药物的发现提供新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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