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Software tools and approaches for compound identification of LC–MS/MS data in metabolomics. The ABC’s (and XYZ’s) of peptide sequencing. Proposal for a common nomenclature for sequence ions in mass spectra of peptides. Computational methods for understanding mass spectrometry–based shotgun proteomics data. Mass-spectrometric exploration of proteome structure and function. Protein analysis by shotgun/bottom-up proteomics. An automated multidimensional protein identification technology for shotgun proteomics. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.
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Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics.
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