de novo peptide sequencing
17 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
These leaderboards are used to track progress in de novo peptide sequencing
Libraries
Use these libraries to find de novo peptide sequencing models and implementationsMost implemented papers
De novo peptide sequencing by deep learning
nh2tran/DeepNovo
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PNAS 2017
In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing.
Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework
cmb-chula/SMSNet
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Molecular & Cellular Proteomics 2019
Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases.
Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices
denisbeslic/denovopipeline
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Nature Machine Intelligence 2021
De novo peptide sequencing is the key technology for finding novel peptides from mass spectra.
Sequence-to-sequence translation from mass spectra to peptides with a transformer model
Noble-Lab/casanovo
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bioRxiv 2023
A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum.
Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry
biocomputing-research-group/casanovo-dia
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Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples.
Protein identification with deep learning: from abc to xyz
nh2tran/DeepNovo
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We combine two modules de novo sequencing and database search into a single deep learning framework for peptide identification, and integrate de Bruijn graph assembly technique to offer a complete solution to reconstruct protein sequences from tandem mass spectrometry data.
Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry
nh2tran/DeepNovo-DIA
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Nature Methods 2018
We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data.
DeepNovoV2: Better de novo peptide sequencing with deep learning
Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy.
pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework
denisbeslic/denovopipeline
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Bioinformatics 2019
In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum.
PepNet: A Fully Convolutional Neural Network for De novo Peptide Sequencing
lkytal/PepNet
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ResearchSquare 2022
The de novo peptide sequencing, which does not rely on a comprehensive target sequence database, provided us a way to identify novel peptides from tandem mass (MS/MS) spectra.
