peptididentifiering

Peptididentifiering is the process of determining the sequence or identity of peptides from experimental data, typically in the context of proteomics. It involves translating mass spectrometry measurements into peptide sequences that can be mapped to proteins or proteomes. The core goal is to establish which peptides are present in a sample and, by extension, which proteins are expressed.

The typical workflow starts with sample preparation and proteolytic digestion, often using trypsin, to produce peptides. The mixture is separated by liquid chromatography and analyzed by tandem mass spectrometry (MS/MS). Fragmentation spectra are matched to candidate peptide sequences using computational methods. Identified peptide-spectrum matches are then assembled into a picture of the proteome, with protein inference addressing how observed peptides support protein presence.

Common approaches for peptididentifiering include database searching, de novo sequencing, and spectral library matching. Database search tools (for example Mascot, SEQUEST, X!Tandem) compare experimental spectra against theoretical spectra generated from protein sequence databases. De novo sequencing directly infers a peptide sequence from a spectrum without relying on a database. Open modification searches and spectral libraries expand capabilities to detect unexpected modifications and improve confidence.

Validation and reporting are essential. False discovery rate (FDR) control, often via target-decoy strategies, helps assess accuracy. Data standards such as mzIdentML and mzML support sharing and reproducibility, while deposition in repositories like ProteomeXchange facilitates access to raw data and identifications.

Applications span large-scale proteomics, biomarker discovery, and post-translational modification profiling. Challenges include complex spectra, database size, homologous peptides, and contamination. Ongoing developments focus on improved scoring, PTM localization, and integration with protein inference to provide robust, quantitative peptide identifications.