Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University.
Institutes of Physical Science and Information Technology, Anhui University.
Brief Bioinform. 2023 Sep 22;24(6). doi: 10.1093/bib/bbad353.
Antiviral peptides (AVPs) are widely found in animals and plants, with high specificity and strong sensitivity to drug-resistant viruses. However, due to the great heterogeneity of different viruses, most of the AVPs have specific antiviral activities. Therefore, it is necessary to identify the specific activities of AVPs on virus types. Most existing studies only identify AVPs, with only a few studies identifying subclasses by training multiple binary classifiers. We develop a two-stage prediction tool named FFMAVP that can simultaneously predict AVPs and their subclasses. In the first stage, we identify whether a peptide is AVP or not. In the second stage, we predict the six virus families and eight species specifically targeted by AVPs based on two multiclass tasks. Specifically, the feature extraction module in the two-stage task of FFMAVP adopts the same neural network structure, in which one branch extracts features based on amino acid feature descriptors and the other branch extracts sequence features. Then, the two types of features are fused for the following task. Considering the correlation between the two tasks of the second stage, a multitask learning model is constructed to improve the effectiveness of the two multiclass tasks. In addition, to improve the effectiveness of the second stage, the network parameters trained through the first-stage data are used to initialize the network parameters in the second stage. As a demonstration, the cross-validation results, independent test results and visualization results show that FFMAVP achieves great advantages in both stages.
抗病毒肽 (AVP) 在动植物中广泛存在，具有针对耐药病毒的高特异性和强敏感性。然而，由于不同病毒的巨大异质性，大多数 AVP 具有特定的抗病毒活性。因此，有必要确定 AVP 对病毒类型的特定活性。大多数现有研究仅识别 AVP，只有少数研究通过训练多个二进制分类器来识别子类。我们开发了一种名为 FFMAVP 的两阶段预测工具，可同时预测 AVP 及其子类。在第一阶段，我们确定肽是否为 AVP。在第二阶段，我们基于两个多类任务预测 AVP 专门针对的六个病毒科和八个物种。具体来说，FFMAVP 两阶段任务中的特征提取模块采用相同的神经网络结构，其中一个分支基于氨基酸特征描述符提取特征，另一个分支提取序列特征。然后，将两种类型的特征融合用于以下任务。考虑到第二阶段两个任务之间的相关性，构建了一个多任务学习模型，以提高两个多类任务的有效性。此外，为了提高第二阶段的效果，通过第一阶段数据训练的网络参数用于初始化第二阶段的网络参数。作为演示，交叉验证结果、独立测试结果和可视化结果表明，FFMAVP 在两个阶段都具有很大的优势。
