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BOC Sciences offers comprehensive solutions for amino acid analysis. We assist in studying the biological functions of proteins by determining the peptide amino acid sequence through de novo peptide sequencing. Based on tandem mass spectrometry, de novo sequencing is a valuable method for obtaining peptide and protein amino acid sequences without prior knowledge.
Tandem mass spectrometry (MS/MS) is a powerful method for retrieving peptide amino acid sequences. Typically, in proteomics, protein samples are digested into shorter peptides more suitable for MS/MS analysis using proteases. De novo peptide sequencing involves determining the amino acid sequence in a peptide solely based on mass spectrometry data, without relying on existing databases or prior knowledge of protein sequences, and is often used to obtain sequence information from new or unknown proteins. The basic principle of de novo peptide sequencing revolves around interpreting MS/MS spectra generated from peptide fragmentation. When a peptide undergoes fragmentation within the mass spectrometer, it produces a series of fragment ions whose masses provide clues about the original peptide sequence. By analyzing the mass-to-charge ratios and intensities of these fragment ions, the peptide sequence can be reconstructed.
In de novo peptide sequencing method, each amino acid is inferred by calculating the ion mass difference of fragmented peptides. As manual characterization of peptides using de novo sequencing can be time-consuming and challenging, various algorithms have been developed to distinguish signal ion peaks from noise peaks and predict the correct peptide sequence. These include deep learning (DL) and encoder-decoder architectures such as convolutional neural networks (CNNs) for encoding mass spectra while using recurrent neural networks (RNNs) as decoders to predict amino acids in the peptide sequence one by one.
De novo peptide sequencing entails a systematic procedure that involves several steps, from data acquisition to sequence interpretation. BOC Sciences provides full-service de novo sequencing, and the following is a typical workflow:
1. Mass Spectrometry
Peptides are subjected to ionization and fragmentation within a mass spectrometer, resulting in the generation of MS/MS spectra. Raw MS/MS data undergo preprocessing steps such as noise filtering and baseline correction to enhance data quality.
2. Spectrum Interpretation
Peak Picking: Relevant peaks corresponding to fragment ions are selected from the MS/MS spectra.
Deconvolution: Overlapping peaks are deconvoluted to simplify the spectrum.
Fragment Ion Identification: Fragment ions are assigned to their corresponding peptide bonds based on mass differences.
3. Sequence Reconstruction
Sequence Tag Generation: Short peptide subsequences, known as sequence tags, are identified from the spectrum.
Sequence Assembly: Sequence tags are combined to reconstruct the full peptide sequence.
Validation: The proposed sequence is validated against the experimental spectrum to ensure consistency.
4. Post-processing and Validation
Error Estimation: Confidence scores or probability estimates are computed to assess the reliability of the sequenced peptide.
Post-translational Modification (PTM) Analysis: PTMs such as phosphorylation or glycosylation are considered during sequence interpretation.
Database Comparison (Optional): The sequenced peptide may be compared against existing databases for validation or additional information.
1. Can de novo peptide sequencing identify post-translational modifications (PTMs)?
Yes, de novo peptide sequencing can identify PTMs by analyzing the mass shifts of fragment ions relative to the unmodified peptide. However, accurate PTM identification requires comprehensive spectral interpretation and may necessitate additional validation steps.
2. Can de novo peptide sequencing be combined with other proteomic techniques?
Yes, de novo peptide sequencing is often integrated with other proteomic techniques such as database searching, protein digestion, or targeted proteomics for comprehensive protein characterization. Hybrid approaches leverage the strengths of each method to enhance accuracy and coverage in peptide identification.
3. How does de novo peptide sequencing compare to database searching?
Unlike database searching, which relies on pre-existing protein databases for peptide identification, de novo sequencing enables the discovery of novel peptides and variants without prior sequence information.
4. What are the emerging trends in de novo peptide sequencing?
Machine Learning: Advances in machine learning algorithms are enabling more accurate and efficient de novo sequencing by leveraging large-scale spectral datasets and training models to predict peptide sequences.
Single-cell Proteomics: De novo sequencing is gaining prominence in single-cell proteomics studies, where conventional database searching may be impractical due to limited sample availability and cellular heterogeneity.
