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de novo peptide sequencing
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De novo peptide sequencing refers to the process of determining the amino acid sequence of a peptide without prior knowledge of the DNA or protein it comes from. This technique is used in proteomics to analyze proteins and peptides, especially when the genomic sequence of the organism is unknown or the protein sequence is not available in databases.
The process typically involves mass spectrometry (MS), where peptides are ionized and fragmented. The mass spectrometer measures the masses of these peptide fragments. By analyzing the mass differences between the fragments, the machine learning model can infer the sequence of amino acids in the peptide.
This method is particularly useful for studying proteins from organisms with unsequenced genomes, post-translational modifications, and for discovering new proteins or variants.
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Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing
Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics
AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information
Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers
DePS: An improved deep learning model for de novo peptide sequencing
Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches
DeepNovoV2: Better de novo peptide sequencing with deep learning
Protein identification with deep learning: from abc to xyz
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