Spectrum graph-based de-novo sequencing algorithm MaxNovo achieves high peptide identification rates in collisional dissociation MS/MS spectra

Abstract

We describe MaxNovo, a novel spectrum graph-based peptide de-novo sequencing algorithm integrated into the MaxQuant software. It identifies complete sequences of peptides as well as sequence tags that are incomplete at one or both of the peptide termini. MaxNovo searches for the highest-scoring path in a directed acyclic graph representing the MS/MS spectrum with peaks as nodes and edges as potential sequence constituents consisting of single amino acids or pairs. The raw score is a sum of node and edge weights, plus several reward scores, for instance, for complementary ions or protease compatibility. For search-engine identified peptides, it correlates well with the Andromeda search engine score. We use a particular score normalization and the score difference between the first and second-best solution to define a combined score that integrates all available information. To evaluate its performance, we use a human cell line dataset and take as ground truth all Andromeda-identified MS/MS spectra with an Andromeda score of at least 100. MaxNovo outperforms other software in particular in the high-sensitivity range of precision-coverage plots. We also identify incomplete sequence tags and study their statistical properties. Next, we apply MaxNovo to ion mobility-coupled time of flight data. Here we achieve excellent performance as well, except for potential swaps of the two amino acids closest to the C-terminus, which are not well resolved due to the low end of the mass range in MS/MS spectra in this dataset. We demonstrate the applicability of MaxNovo to palaeoproteomics samples with a Late Pleistocene hominin proteome dataset that was generated using three proteases. Interestingly, we did not use any machine learning in the construction of MaxNovo, but implemented expert domain knowledge directly in the definition of the score. Yet, it performs as good as or better than the leading deep learning-based algorithm.

Paleoproteomics

“…In phylogenetic studies of taxa for which the exact protein sequence is unknown, software is available for de novo sequencing . This is especially valuable for determining sequences in extinct species for which genomic data cannot be obtained ,,, or authenticating PMF markers. ,, A variety of software has been used to perform de novo sequencing on ancient proteins, including PEAKS, Byonic, and MaxNovo. Algorithmic differences between the different software influence the accuracy of identification.…”

A handle on mass coincidence errors inde novosequencing of antibodies by bottom-up proteomics

“…The previously published version of Stitch was already able to handle PEAKS, FASTA, and plain text data, which is now extended to include the output from Casanovo, MaxNovo, Novor.Cloud, and pNovo. [34][35][36][37][38][39][40][41] These peptides are aligned to the germline sequences of the corresponding gene-segments (V/J/C) for the appropriate species and placed when the alignment score exceeds a user-defined cutoff.…”

“…These updates include a massbased alignment algorithm (akin to Meta-SPS-contig 33 ), a post processing procedure to assign I/L residues based on secondary fragments, a built-in spectrum viewer, a graph based analysis which tracks the connectivity between identified sequence variants, and the ability to use a wider selection of de novo peptide sequencing programs as input, including Casanovo, MaxNovo, Novor.Cloud, PEAKS, and pNovo. [34][35][36][37][38][39][40][41][42] Combined, these changes make Stitch compatible with a larger range of data processing pipelines and improve its tolerance to peptide-level sequencing errors.…”

Spectra without stories: reporting 94% dark and unidentified ancient proteomes

“…Machine learning and de novo sequencing without databases are other alternatives. Since novel and extinct sequences may be present in ancient samples, a de novo -centric approach may discover amino acid substitutions relevant to evolutionary studies and improve overall proteome composition 45 , 46 . Validation and optimisation may be required for these alternative approaches, and a substantial collection of quality ancient spectra is needed to train relevant machine learning models.…”