Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module

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.

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics

Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.

DeepMSPeptide: peptide detectability prediction using deep learning

Summary The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the results and the presence of degenerated peptides. However, considering in the analysis only those peptides that could be detected by mass spectrometry, also called proteotypic peptides, increases the accuracy of the results. Several approaches have been applied to predict peptide detectability based on the physicochemical properties of the peptides. In this manuscript, we present DeepMSPeptide, a bioinformatic tool that uses a deep learning method to predict proteotypic peptides exclusively based on the peptide amino acid sequences. Availability and implementation DeepMSPeptide is available at https://github.com/vsegurar/DeepMSPeptide. Supplementary information Supplementary data are available at Bioinformatics online.

MiTPeptideDB: a proteogenomic resource for the discovery of novel peptides

Motivation The principal lines of research in MS/MS based Proteomics have been directed toward the molecular characterization of the proteins including their biological functions and their implications in human diseases. Recent advances in this field have also allowed the first attempts to apply these techniques to the clinical practice. Nowadays, the main progress in Computational Proteomics is based on the integration of genomic, transcriptomic and proteomic experimental data, what is known as Proteogenomics. This methodology is being especially useful for the discovery of new clinical biomarkers, small open reading frames and microproteins, although their validation is still challenging. Results We detected novel peptides following a proteogenomic workflow based on the MiTranscriptome human assembly and shotgun experiments. The annotation approach generated three custom databases with the corresponding peptides of known and novel transcripts of both protein coding genes and non-coding genes. In addition, we used a peptide detectability filter to improve the computational performance of the proteomic searches, the statistical analysis and the robustness of the results. These innovative additional filters are specially relevant when noisy next generation sequencing experiments are used to generate the databases. This resource, MiTPeptideDB, was validated using 43 cell lines for which RNA-Seq experiments and shotgun experiments were available. Availability and implementation MiTPeptideDB is available at http://bit.ly/MiTPeptideDB. Supplementary information Supplementary data are available at Bioinformatics online.