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de novo peptide sequencing

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.

Papers

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Showing 1–24 of 24 papers

TitleStatusHype
Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide SequencingCode1
Universal Biological Sequence Reranking for Improved De Novo Peptide SequencingCode1
Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide SequencingCode0
NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics—0
AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information—0
Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometryCode0
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide SequencingCode1
Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning modelCode1
De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experimentsCode1
Introducing π-HelixNovo for practical large-scale de novo peptide sequencingCode1
PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencingCode0
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencingCode1
Sequence-to-sequence translation from mass spectra to peptides with a transformer modelCode1
DPST: De Novo Peptide Sequencing with Amino-Acid-Aware TransformersCode0
DePS: An improved deep learning model for de novo peptide sequencing—0
PepNet: A Fully Convolutional Neural Network for De novo Peptide SequencingCode1
Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devicesCode1
Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing FrameworkCode1
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—0
pNovo 3: precise de novo peptide sequencing using a learning-to-rank frameworkCode0
DeepNovoV2: Better de novo peptide sequencing with deep learningCode0
Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometryCode0
Protein identification with deep learning: from abc to xyzCode0
De novo peptide sequencing by deep learningCode1
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