medical-7
de novo peptide sequencing
3260 papers • 126 benchmarks • 313 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.
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Most implemented papers
De novo peptide sequencing by deep learning
Ngoc Hieu Tran, L. Xin, Ming Li, B. Shan, Xianglilan Zhang•Mon Jul 17 2017
The proposed DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides to solve the complex optimization task of de novo sequencing.
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