The Generating Function Approach for Peptide Identification in Spectral Networks

Abstract

Tandem mass (MS/MS) spectrometry has become the method of choice for protein identification and has launched a quest for the identification of every translated protein and peptide. However, computational developments have lagged behind the pace of modern data acquisition protocols and have become a major bottleneck in proteomics analysis of complex samples. As it stands today, attempts to identify MS/MS spectra against large databases (e.g., the human microbiome or 6-frame translation of the human genome) face a search space that is 10-100 times larger than the human proteome, where it becomes increasingly challenging to separate between true and false peptide matches. As a result, the sensitivity of current state-of-the-art database search methods drops by nearly 38% to such low identification rates that almost 90% of all MS/MS spectra are left as unidentified. We address this problem by extending the generating function approach to rigorously compute the joint spectral probability of multiple spectra being matched to peptides with overlapping sequences, thus enabling the confident assignment of higher significance to overlapping peptide-spectrum matches (PSMs). We find that these joint spectral probabilities can be several orders of magnitude more significant than individual PSMs, even in the ideal case when perfect separation between signal and noise peaks could be achieved per individual MS/MS spectrum. After benchmarking this approach on a typical lysate MS/MS dataset, we show that the proposed intersecting spectral probabilities for spectra from overlapping peptides improve peptide identification by 30-62%.

Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison

“…Although spectral similarity measures such as PCC and SA specifically take fragment intensity into account, the definition of a threshold indicative of spectral identify requires the estimation of a similarity distribution 38 or scoring of all possible spectral combinations. 39 Both methods are, compared to the classical target-decoy estimation, computationally expensive, and many alternative amino acid combinations can be found whose spectra only vary to a very minor degree. The USE and particularly the implemented interfaces to public repositories and prediction servers might play an important role in the development of robust methods estimating such thresholds.…”

Dereplication, sequencing and identification of peptidic natural products: from genome mining to peptidogenomics to spectral networks

“…Moreover, even if these tools were able to efficiently search for peptides with more than 2 modifications, the resulting PSMs often would not be reported as statistically significant since many RiPPs are poorly fragmented (due to presence of complex modifications). Search for multiple variable modifications often results in a high false discovery rate (FDR) even for microbial organisms with small proteomes [67]. …”

Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison

“…or query) spectrum to a reference spectrum still remains difficult. Although spectral similarity measures such as PCC and SA have a clearly defined value range, the definition of a threshold indicative of spectral identify requires the estimation of a similarity distribution(27) or scoring of all possible spectral combinations(28). We implemented and tested an approach to estimate E-values(29) based on scoring a query spectrum to multiple (predicted) spectra with essentially equal precursor mass and precursor charge but differing amino acid composition to estimate the similarity distribution.…”