Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification
| Autor: | Sarah C. Jenson, Isabelle O'Bryon, Eric D. Merkley |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Whole genome sequencing
0303 health sciences Sequence database Computer science 030302 biochemistry & molecular biology Review Computational biology Proteomics Biochemistry DNA sequencing 03 medical and health sciences Tandem Mass Spectrometry Metaproteomics Graph (abstract data type) Identification (biology) Databases Protein Peptides Molecular Biology Peptide sequence 030304 developmental biology |
| Zdroj: | Protein Sci |
| ISSN: | 1469-896X 0961-8368 |
| DOI: | 10.1002/pro.3919 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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