Spectral Library Searching To Identify Cross-Linked Peptides

Methods harnessing protein cross-linking and mass spectrometry (XL-MS) offer high-throughput means to identify protein–protein interactions (PPIs) and structural interfaces of protein complexes. Yet, specialized data dependent methods and search algorithms are often required to confidently assign peptide identifications to spectra. To improve the efficiency of matching high confidence spectra, we developed a spectral library based approach to search cross-linked peptide data derived from Protein Interaction Reporter (PIR) methods using the spectral library search algorithm, SpectraST. Spectral library matching of cross-linked peptide data from query spectra increased the absolute number of confident peptide relationships matched to spectra and thereby the number of PPIs identified. By matching library spectra from bona fide, previously established PIR-cross-linked peptide relationships, spectral library searching reduces the need for continued, complex mass spectrometric methods to identify peptide relationships, increases coverage of relationship identifications, and improves the accessibility of XL-MS technologies

Cumulative number of distinct peptides as a function of the addition of more good spectra (identified with ≥ 0

<p><b>Copyright information:</b></p><p>Taken from "Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry"</p><p>Genome Biology 2004;6(1):R9-R9.</p><p>Published online 10 Dec 2004</p><p>PMCID:PMC549070.</p><p>Copyright © 2004 Desiere et al.; licensee BioMed Central Ltd.</p>9). Eventually the pattern is expected to show saturation, as most observable peptides will have been cataloged. However, at present there is no evidence of saturation and around 100 new peptides are still cataloged per 1,000 identified spectra added