Abstract
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.
Highlights
Proteomics is a vital technology in the field of high-throughput experiments
The advancement of mass spectrometry (MS) analysis is of critical significance to provide reliable results at the proteomics level, whereas several problems remain with the existing technology
This study introduces a novel integrated learning network framework based on the capsule network (CapsNet) and the convolutional block attention module (CBAM) to predict the detectability of peptides
Summary
Proteomics is a vital technology in the field of high-throughput experiments. To be specific, protein detection and quantification are vital for gaining more insights into cell biology and human disease [1]. Zimmer et al [11] developed an algorithm by complying with the deep fully connected feed-forward neural network, thereby achieving the informed selection of synthetic prototypic peptides to effectively design targeted proteomics quantification assays They adopted a BioFSharp toolbox to convert a set peptide chain into a feature vector with 45 entries, representing a numerical footprint of physicochemical properties of peptides as the input of the neural network. This study introduces a novel integrated learning network framework based on the capsule network (CapsNet) and the convolutional block attention module (CBAM) to predict the detectability of peptides It builds the residue conical coordinate (RCC) feature [16] by complying with the physicochemical properties of residues and combines the statistical information to improve feature extraction. The experimentally achieved results verify the effectiveness of this method
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