2027· Methods in molecular biology· Vol 3071, pp.
337-351
· 0 citations
Medicine
TL;DR
This chapter presents a machine-learning model, called ProteoRift, capable of predicting multiple properties of the peptide directly from the spectra, which helps to apply more targeted filters in the database search and provides up to 12× speedup as compared to mass-only filtering in peptide identification.
One critical step in any targeted mass spectrometry experiment is selecting, from each protein of interest, a small number of peptides that respond well in the mass spectrometer and can serve as reliable proxies for protein quantification. Existing methods select target peptides either by relying on prior empirical mea...
Shreyash Sonthalia, Bo Wen, Priank Dasgupta et al.· Journal of Proteome Research· 0 citations
This work demonstrates that a pretrained spectrum encoder for tandem mass spectrometry proteomics trained on de novo sequencing learns generalizable representations of spectra, improves performance on downstream tasks where training data is limited, and can ultimately enhance data acquisition and analysis in proteomics...
Justin A. Sanders, Melih Yilmaz, Jacob H. Russell et al.· Molecular & Cellular Proteom...· 0 citations
InstaNovo-FM is presented, a self-supervised foundation model for bottom-up proteomics trained to reconstruct masked regions of tandem mass spectra and establishes a unified representation space for peptide fragmentation spectra, enabling robust transferability across the proteomics ecosystem.
M. Nieuwoudt, Marco Reverenna, Divanisha Patel et al.· bioRxiv· 1 citation· ⚡1
Structural elucidation of unknown compounds using tandem mass spectrometry (MS/MS) is an ongoing challenge. Expert chemists will often use common product ion peaks and neutral losses or peak matching software to predict structural aspects from MS/MS spectra, a process which is time consuming and limited by the molecu...
Connor H. Beveridge, Prageeth R. Wijewardhane, Matthew Muhoberac et al.· Analytical Chemistry· 0 citations
Overall, this chapter aims to empower biologists and chemists to harness machine learning for metabolomics, transforming unannotated spectra into interpretable chemical knowledge.
Filip Jozefov, Roman Bushuiev, Anton Bushuiev et al.· Methods in molecular biology· 0 citations
Modern biological research has become highly interdisciplinary, requiring mass spectrometry data generation to be combined with bioinformatics and data analysis skills to obtain meaningful results. However, many researchers who regularly conduct mass spectrometry experiments often lack practical experience in interpret...
S. Makhsatova, A. Kurmanbay, A. Kulyyassov· Eurasian journal of applied...· 0 citations
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