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. 2017 Jan;13(1):30-37.
doi: 10.1038/nchembio.2219. Epub 2016 Oct 31.

Dereplication of Peptidic Natural Products Through Database Search of Mass Spectra

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Free PMC article

Dereplication of Peptidic Natural Products Through Database Search of Mass Spectra

Hosein Mohimani et al. Nat Chem Biol. .
Free PMC article

Abstract

Peptidic natural products (PNPs) are widely used compounds that include many antibiotics and a variety of other bioactive peptides. Although recent breakthroughs in PNP discovery raised the challenge of developing new algorithms for their analysis, identification of PNPs via database search of tandem mass spectra remains an open problem. To address this problem, natural product researchers use dereplication strategies that identify known PNPs and lead to the discovery of new ones, even in cases when the reference spectra are not present in existing spectral libraries. DEREPLICATOR is a new dereplication algorithm that enables high-throughput PNP identification and that is compatible with large-scale mass-spectrometry-based screening platforms for natural product discovery. After searching nearly one hundred million tandem mass spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure, DEREPLICATOR identified an order of magnitude more PNPs (and their new variants) than any previous dereplication efforts.

Figures

Figure 1
Figure 1
DEREPLICATOR pipeline. DEREPLICATOR pipeline includes the following steps: (i) generating decoy database of PNPs (ii) constructing theoretical spectra for all PNPs in the database, (iii) generating and scoring PSMs, (vi) computing p-values of PSMs and generating the set of statistically significant PSMs, (v) computing false discovery rate, (vi) enlarging the set of found PSMs through variable dereplication via spectral networks. Various steps related to target and decoy databases are shown in green and red boxes, respectively. Six peptides identified in target database and two peptides identified in decoy database are shown in green and red, respectively.
Figure 2
Figure 2
Number of PSMs and peptides identified by DEREPLICATOR. For each x (shown as p-value along the x-axis), the plots show the number of identified PSMs or peptides with p-values below x. (Top) Number of PSMs (a) and peptides (b) for the target AntiMarin and decoy databases in the search of Spectra4. 1787 PSMs and 180 unique PNPs with p-value below 10−13 were dereplicated via spectral networks. (Bottom) Number of PSMs (c) and peptides (d) for the target AntiMarin and decoy databases in the search of SpectraGNPS. All searches were performed with the precursor mass tolerance 0.05 Da.
Figure 3
Figure 3
Number of peptides identified by DEREPLICATOR in SpectraHigh dataset. The number of unique peptides identified from Fungal/Actinomycetales/Pseudomonas/Cyanobacteria spectral datasets, coming from Fungal/Actinomycetales/Pseudomonas/Cyanobacteria sources. Since B. subtilis was added to the extracts from the samples SpectraActi and SpectraPseu, 42 and 22 peptides from Bacillus sources identified in SpectraActi and SpectraPseu represent contaminants. Since Bacillus growth media is similar to that of Actinomycetes and Pseudomonas, samples from Actinomycetes and Pseudomonas often have small Bacillus contaminations that originates from pre-autoclaving growth in the media.
Figure 4
Figure 4
Spectral networks illustrating the results of SILAC experiment. (a) Spectral network of surugamides from S. albus J1074 when the strain is labeled by 13C6 isoleucines. A path connecting five green nodes reveals surugamide A (911.621 Da, observed at m/z 912.610) and four SILAC incorporations into isoleucine with characteristic 6 Da mass shifts (surugamide A has four isoleucines which are observed as addition of 6 Da, 12 Da, 18 Da and 24 Da to the precursor ion). Blue nodes reveal incorporations in surugamide B with three isoleucines (897.605 Da, observed at m/z 898.611), and purple nodes reveal incorporations in a previously unknown surugamide variant with two isoleucines (m/z 884.589). (b) Spectral network of surugamides from S. albus J1074 when the strain is labeled by 13C6 lysine. Green and blue nodes reveal SILAC incorporations into a single lysine in surugamides A and B. Sizes of the nodes reflect relative abundance based on total intensity of the ion that was fragmented. Width of the edges connecting the nodes reflects the similarity (cosine score) between corresponding spectra. Since we used a stringent cosine threshold 0.7, some related spectra are not connected by edges. (c) structure of surugamide A.
Figure 5
Figure 5
Generating theoretical spectra and computing p-values of PSMs formed by PNPs with various architectures. (a) Generating the theoretical spectrum of a branch-cyclic peptide (only 12 out of 90 peaks in the theoretical spectrum are shown). Nodes and edges in the PNP graph are shown as circles and lines. Bridges are shown as red edges. The intensities of all peaks in the theoretical spectrum are the same since prediction of intensities remains an open problem. (b) MS-DPR explores a large set of peptides (enriched for high-scoring peptides) to accurately estimate p-values. Each such set is illustrated as a collection of seven peptides, each with a different shuffled sequence of amino acids. (c) Constructing decoy database of PNPs by randomly rearranging amino acids while preserving the architecture of a PNP.

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