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

PLoS Comput Biol. 2023 Jan 20;19(1):e1010457. doi: 10.1371/journal.pcbi.1010457. eCollection 2023 Jan.

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 samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antibodies, Monoclonal
  • Deep Learning*
  • Peptide Hydrolases
  • Peptides / chemistry
  • Proteomics* / methods
  • Sequence Analysis, Protein / methods
  • Tandem Mass Spectrometry / methods

Substances

  • Peptides
  • Peptide Hydrolases
  • Antibodies, Monoclonal

Grants and funding

This work was supported by Foundation of Knut and Alice Wallenberg (2016.0023) to LH, JM and LM, Vetenskapsrådet 2020-02419 to JM and LM, and Alfred Österlunds Stiftelse to JM and LM. The funders had no role in study design, data collection, analysis, publication decision, or manuscript preparation. None of the researchers received a salary from the funders.