De Novo Protein Sequencing: From Experimental Design to Data Interpretation
De novo protein sequencing does not require a reference genome or database and directly determines the primary structure of proteins (amino acid sequence). Although significant progress has been made in recent years, several challenges persist, including the accuracy of sequence analysis, the difficulties associated with sequencing complex samples, the impact of post-translational modifications (PTMs), and computational complexity. This paper explores the primary challenges of de novo protein sequencing and introduces corresponding solutions.
Key Challenges in De Novo Protein Sequencing
1. Limited Accuracy of Sequence Analysis
De novo protein sequencing relies on the b/y ion signal intensities derived from MS/MS data. However, in practice, ion signals may be incomplete, resulting in reduced sequence coverage. Additionally, certain amino acids, such as isoleucine and leucine, have the same mass in mass spectrometry, complicating the distinction between these residues.
2. Challenges in Sequencing Complex Samples
Biological samples typically consist of multiple proteins, presenting a highly complex protein composition and wide dynamic range. The detection of low-abundance proteins is often obscured by the presence of high-abundance proteins, which reduces the sensitivity of de novo sequencing. Additionally, sample degradation or modifications during proteomics experiments further complicate the analysis.
3. Interference from Post-Translational Modifications (PTMs)
PTMs (such as phosphorylation, glycosylation, and acetylation) can alter peptide fragmentation patterns in mass spectrometry, affecting the distribution of b/y ions. For instance, phosphorylation can result in the loss of specific ions, making it difficult for conventional de novo sequencing algorithms to accurately identify the complete sequence.
4. High Computational Complexity
De novo protein sequencing relies on bioinformatics algorithms for sequence determination. However, with the widespread use of high-resolution mass spectrometers, the volume of data grows exponentially, making it challenging for traditional computational methods to process large MS/MS datasets effectively. Moreover, de novo sequencing algorithms must account for ion interference, signal noise, and post-translational modifications, further increasing computational complexity.
Solutions
1. Improving Mass Spectrometry Data Quality
(1) Optimizing Fragmentation Techniques: A combination of multiple fragmentation methods, such as High-Energy Collisional Dissociation (HCD) and Electron Transfer Dissociation (ETD), is employed to enhance the detection of specific ion signals and increase sequence coverage.
(2) Improving Sample Preparation: Different enzyme digestion strategies (e.g., combinations of Lys-C, Asp-N, trypsin, etc.) are used to improve the detectability of protein sequences.
(3) Enhancing Data Acquisition Modes: A strategy combining Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) is adopted to increase the sensitivity of low-abundance peptide detection.
2. Strategies for Complex Sample Separation
(1) Multidimensional Liquid Chromatography (LC): The integration of High-Performance Liquid Chromatography (HPLC) and nano-liquid chromatography (nanoLC) enhances peptide separation and minimizes the impact of high-abundance proteins.
(2) Protein Enrichment Techniques: For low-abundance proteins, methods such as immunoaffinity enrichment and metal affinity enrichment (e.g., phosphorylation site enrichment) are applied to improve the detection probability of target proteins.
3. Optimization of PTM Analysis
(1) Specific Modification Enrichment Strategies: Techniques such as antibody affinity purification and Hydrophilic Interaction Liquid Chromatography (HILIC) are used to enrich peptides with specific modifications, thereby enhancing the detection of modified proteins.
(2) Multilevel Mass Spectrometry Fragmentation: The use of MS3 or ETD-HCD combinations improves the accuracy of post-translational modification (PTM) site identification.
4. Optimization of Computational Methods
(1) Deep Learning and Artificial Intelligence (AI): Neural network-based de novo sequencing algorithms (e.g., DeepNovo) are developed, utilizing large-scale data for model training to enhance the accuracy of sequence interpretation.
(2) Efficient Parallel Computing: GPU acceleration is employed to speed up the processing of large-scale MS/MS data.
(3) Multi-Omics Integration: Integrating transcriptomic and proteomic data is used to collaboratively optimize the results of de novo sequencing.
Although de novo protein sequencing offers unique advantages in the analysis of unknown proteins, its technical process still faces several challenges. These challenges encompass experimental design, mass spectrometry data acquisition, and computational analysis, requiring interdisciplinary technological integration and innovative algorithms. The core challenge in de novo protein sequencing lies in the balance between technical limitations and biological complexity. Current solutions exhibit an interdisciplinary trend: innovations in mass spectrometry hardware (e.g., UVPD), single-molecule sequencing technologies (nanopore/fluorescence), and artificial intelligence algorithms (CNN/quantum computing) together form a technological ecosystem. MtoZ Biolabs offers high-quality de novo sequencing services for proteomics researchers, gaining widespread recognition for its contributions.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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