PRiSM: A prototype for exhaustive, restriction-free database searching for mass spectrometry-based identification

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Current peptide identification methods in mass spectrometry are limited, failing to identify the majority of spectra due to restricted search spaces.
• These restrictions, while speeding up computation, create blind spots and prevent comprehensive analysis.
• A method covering the entire search space is needed to establish a reliable baseline for peptide identification.

Purpose of the Study:

• To introduce the mass pattern paradigm (MPP) as a novel approach for peptide identification.
• To implement MPP into a prototype database search engine, PRiSM (PRotein-Spectrum Matching).
• To assess PRiSM's performance and gain insights into sensitivity-speed trade-offs for establishing an identification baseline.

Main Methods:

• Developed the mass pattern paradigm (MPP) for unrestricted peptide identification.
• Implemented MPP into the PRiSM database search engine.
• Evaluated PRiSM using high-precision mass spectra of varying identification difficulty.

Main Results:

• PRiSM achieved 84 agreements with SEQUEST on 100 low-difficulty spectra, with 75 being statistically significant.
• PRiSM identified 13 previously unidentifiable spectra from a challenging dataset, revealing 3 novel proteins.
• The prototype demonstrated statistically reliable identifications and controlled false discovery rates.

Conclusions:

• The PRiSM prototype, using the mass pattern paradigm, can reliably identify peptides even with noise present.
• It successfully identifies spectra missed by previous "extremely open" searches, offering a path towards a proteomics identification baseline.
• This method enhances peptide identification capabilities in mass spectrometry data analysis.