Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification

Abstract

Mass spectrometry‐based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph‐based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

Affinity Selection from Synthetic Peptide Libraries Enabled by De Novo MS/MS Sequencing

“…De novo sequencing (Taylor and Johnson 2001;Seidler et al 2010;Hunt et al 1992) is an alternative approach to interpreting MS/MS spectra that is better-suited to peptide libraries, since it is applicable in principle to any peptide sequence, provided complete series of fragment ions can be assigned (with the exception that Ile and Leu are not routinely distinguished). But compared to database searching, the reliability of automated de novo sequencing continues to be viewed with skepticism O'Bryon et al 2020).…”

Degradation of Diatom Protein in Seawater: A Peptide-Level View

“…Peptides were identified from the raw mass spectra using a combination database-driven and database-independent de novo sequencing approach. De novo peptide sequencing, where the amino acid sequence of peptides is determined solely from the mass spectra without comparison to a reference database (Allmer, 2011), was advantageous in this study because we lacked a paired metagenome to thoroughly characterize the microbial community from the seawater inoculum (Muth et al, 2015(Muth et al, , 2018O'Bryon et al, 2020). The combining of database and de novo is termed de novo-directed proteomics, and was performed using PeaksDB within Peaks Studio (v8.5;Bioinformatics Solutions, Waterloo, Canada;Zhang et al, 2012).…”

In Silico Study of Physicochemical Characteristics and Anticancer Potential of Peptides Derived from Salvia Hispanica

“…The other remaining 1067 peptides are considered as de novo sequences since they haven't been identi ed in other organisms; these peptides are presented in Supplementary Information Table 2, along with their physicochemical characteristics. Additionally, the average local con dence (ALC) of each de novo sequence presented is a percentage that ranges from 0 to 100% and it indicates how likely the complete sequence of the peptide is correct; thus, it represents an average of the con dence of the assignment of each individual amino acid residue on each peptide (Ma et al, 2003;O'Bryon et al, 2020). In accordance with (Tran et al, 2016), peptides with a low ALC (< 50%) indicate a large number of incorrect amino acid residues in each sequence.…”