Department of Mathematical Sciences, School of Science, Zhejiang Sci-Tech University, Xuelin St., Hangzhou 310018, China.
Institutes of Biomedical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai 200032, China.
Int J Mol Sci. 2021 Nov 8;22(21):12080. doi: 10.3390/ijms222112080.
According to proteomics technology, as impacted by the complexity of sampling in the experimental process, several problems remain with the reproducibility of mass spectrometry experiments, and the peptide identification and quantitative results continue to be random. Predicting the detectability exhibited by peptides can optimize the mentioned results to be more accurate, so such a prediction is of high research significance. This study builds a novel method to predict the detectability of peptides by complying with the capsule network (CapsNet) and the convolutional block attention module (CBAM). First, the residue conical coordinate (RCC), the amino acid composition (AAC), the dipeptide composition (DPC), and the sequence embedding code (SEC) are extracted as the peptide chain features. Subsequently, these features are divided into the biological feature and sequence feature, and separately inputted into the neural network of CapsNet. Moreover, the attention module CBAM is added to the network to assign weights to channels and spaces, as an attempt to enhance the feature learning and improve the network training effect. To verify the effectiveness of the proposed method, it is compared with some other popular methods. As revealed from the experimentally achieved results, the proposed method outperforms those methods in most performance assessments.
根据蛋白质组学技术，由于实验过程中采样的复杂性，质谱实验的重现性仍然存在一些问题，肽的鉴定和定量结果仍然是随机的。预测肽的可检测性可以优化上述结果，使其更加准确，因此这种预测具有很高的研究意义。本研究通过遵从胶囊网络（CapsNet）和卷积块注意模块（CBAM），建立了一种新的预测肽可检测性的方法。首先，提取残基圆锥坐标（RCC）、氨基酸组成（AAC）、二肽组成（DPC）和序列嵌入码（SEC）作为肽链特征。然后，这些特征被分为生物特征和序列特征，并分别输入到 CapsNet 的神经网络中。此外，在网络中添加注意力模块 CBAM，为通道和空间分配权重，尝试增强特征学习，提高网络训练效果。为了验证所提出方法的有效性，将其与一些其他流行的方法进行了比较。实验结果表明，该方法在大多数性能评估中优于其他方法。
