Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics.

ABSTRACT: Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samp

SUBMITTER: Gueto-Tettay C 

PROVIDER: S-EPMC9891523 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature