## 4 Discussion
### The Deep Learning Boost, a New Feature in PEAKS, Enhances Protein Identification in DDA Workflows  
The principle of protein identification by comparing LC-MS/MS results with a database was first demonstrated using Mascot Server. Since its introduction in 1998, Mascot has become a reference standard in proteomics, with the original article published in Electrophoresis [28] cited 6620 times. This is a distinctive indicator that this database search is still very popular for the research community, but in the late 2010s, machine learning (ML) and deep learning (DL) algorithms garnered significant attention for peptide identification in complex LC-MS/MS data [49; 50; 51; 52]. DL, a subfield of ML, is based on neural networks and differs from traditional ML methods by employing more layers in the network, requiring larger datasets, and enabling much faster feature extraction from raw data [53]. Unlike traditional database searching, ML/DL-based peptide identification predicts peptide fragmentation spectra directly from amino acid sequences. The key advantage of these modern methods is their significant improvement in the reliability and quality of the workflows [30; 49; 50].  
Bioinformatics Solutions Inc. has led advancements in integrating machine and deep learning algorithms in proteomics. The recently released software version PEAKS Studio  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**N** & **Sample** & **LC** & **MS/MS** & **Column Length** & **Gradient Length** & **Analysis Tool (Search Parameters)** & **Number of Proteins** & **Ref.** \\ \hline
5. & Human plasma1 & Ultimate plasma & Orbitance 3000 RSLC & Fusion nano system & PepMap RSLC C18 (50 cm/2 μm) & 157 min & Mascot, 1\% FDR & 442 & [48] \\ \hline
6. & Human plasma2 & Ultimate plasma & Ultimate 3000 RSLC & QTOF & PepMap RSLC C18 (15 cm/2 μm) & 48 or 90 min & Mascot, 1\% FDR & 68–126 (187 total) & This study \\ \hline
7. & Albumin-depleted plasma & Ultimate 3000 RSLC & QTOF & PepMap RSLC C18 (15 cm/2 μm) & 48 or 90 min & Mascot, 1\% FDR & 85–140 (212 total) & This study \\ \hline \hline \end{tabular}  
* 14 Abundant proteins were depleted from the sample; 2 non-depleted plasma.  
\end{table}
Table 1: _Cont._11 introduced a new feature called deep learning boost for DDA analysis, which allows increasing the number of identifications by more than 10% ([[https://www.bioinfor.com](https://www.bioinfor.com)]([https://www.bioinfor.com](https://www.bioinfor.com)), access date 8 November 2024). Deep learning models in PEAKS are trained on large-scale proteomics data and they are vendor- and application-specific (HLA peptides vs. general proteomics data). What this means is that there are specially trained models for Bruker, Agilent, Sciex, or Thermo data, and also general- or immunopeptide-trained models for each vendor [54]. Deep learning will predict peptide detectability, MS2 spectra (peptide fragments with intensities), peptide retention times, and ion mobilities if applicable (CCS for timsTOF/CV for FAIMS). Based on the internally calculated correlation between the predicted and empirical properties (including delta RT and MS2 correlation shown in the results table), PEAKS will re-score the PSM \(-\)10 log\({}_{10}\)(P) scores.  
The deep learning boost feature increased protein identifications in this study even more than the software developers stated, especially for samples derived from CHO (20-26%) and _E. coli_ (18-28%). One reason for the higher efficiency of PEAKS compared to Mascot is its use of deep learning algorithms for peptide identification, leveraging raw data directly. However, when deep learning boost modes are disabled, Mascot performs comparably to PEAKS in terms of identification efficiency.