Dereplication of microbial metabolites through database search of mass spectra

Abstract

Natural products have traditionally been rich sources for drug discovery. In order to clear the road toward the discovery of unknown natural products, biologists need dereplication strategies that identify known ones. Here we report DEREPLICATOR+, an algorithm that improves on the previous approaches for identifying peptidic natural products, and extends them for identification of polyketides, terpenes, benzenoids, alkaloids, flavonoids, and other classes of natural products. We show that DEREPLICATOR+ can search all spectra in the recently launched Global Natural Products Social molecular network and identify an order of magnitude more natural products than previous dereplication efforts. We further demonstrate that DEREPLICATOR+ enables cross-validation of genome-mining and peptidogenomics/glycogenomics results.

New natural products can be identified via mass spectrometry by excluding all known ones from the analysis, a process called dereplication. Here, the authors extend a previously published dereplication algorithm to different classes of secondary metabolites.

Introduction

Since 1990s, there has been a decline in the pace of antibiotics discovery from natural sources¹. However, natural product discovery has recently gained attraction due to multiple technological advances, exemplified by the discovery of teixobactin^2,3. Global Natural Products Social (GNPS) molecular networking project⁴ is a recent mass spectrometry data repository in the field of natural products. While thousands of laboratories have contributed billion mass spectra, identifying spectra of known natural products in this repository is a challenging problem. The first attempts to solve this problem date back to 1960s^5–8, two decades before mass spectrometry-based database search tools appeared in proteomics^9,10. However, to date, computational mass spectrometry of small molecules is regarded as a less mature field than the proteomic counterpart^11–18.

One of the main challenges in the field of natural product is the high rate of re-discovery of known natural products. The process of using the information about the chemical structure of a known natural product to identify this compound in an experimental sample (without having to repeat the entire isolation and structure-determination process) is called dereplication. Development of chemical structure databases such as PubChem (≈83 million compounds), ChemSpider (≈58 million compounds), ChEMBL (2.1 million compounds), ChemBank (1.2 million compounds), ChEBI (440,000 compounds), Dictionary of Natural Products (≈300 thousand compounds), AntiMarin (≈60 thousand compounds), KEGG (≈16 thousand compounds), MetaCy (≈10 thousand compounds), mzCloud (≈3 thousand compounds), NuBBEDB (2500 compounds), MIBiG (≈1600 compounds), DrugBank (1360 compounds with structural information), and Norine (≈1000 compounds) has paved the way for development of bioinformatics tools for natural product dereplication. Early dereplication approaches were based on deriving the exact chemical formula using high-resolution precursor mass and searching for compounds described by this formula in chemical structure databases^19–22. However, this approach often fails because the number of possible formulas rapidly increases with the molecular mass of metabolites and because existing chemical databases contain many compounds with identical formulas.

Several strategies have been proposed for dereplicating spectra of metabolites, including (i) combinatorial fragmentation strategies that use systematic bond disconnection approach to break bonds between heavy atoms^23–28, (ii) the HighChem Mass Frontier approach that predicts fragmentation based on standard reactions and a library of fragmentation rules; (iii) approaches based on learning the mapping between mass spectra and molecular formula of the candidate compounds from reference spectra to create predicted fragmentation trees and possible structures against which mass spectra can be searched^29,30; (iii) approaches that use stochastic Markov modeling for simulating mass spectra from molecular structures and matching them against experimental spectra³¹; (iv) approaches that construct in silico mass spectra by fragmenting peptides and lipids along specific bonds^32,33; (v) approaches that annotate the structural motifs rather than the entire structure³⁴; (vi) the ab initio approaches that predict the likely fragmentation of a molecule by computing its energetic landscape³⁵.

Currently, the fast spectral library search programs³⁶ search over 1000 spectra against the entire NIST library per seconds. However these approaches are unable to directly search chemical structure libraries. Despite recent progress (CSI:FingerID²⁹ increased metabolite identification rates fivefold as compared to previous approaches), the existing tools for metabolite identifications are either limited to specific classes of molecules such as peptides and lipids^32,33, work best for identification of small molecules (below 500 Da)²⁹, or become prohibitively time-consuming for searching large spectral datasets²⁹. Recently, we introduced DEREPLICATOR³² for searching spectral datasets against the database of peptidic natural products (PNPs) that include nonribosomal peptides (NRPs), and ribosomally synthesized and post-translationally modified peptides (RiPPs). DEREPLICATOR constructs theoretical spectra of peptide natural products by disconnecting all the bridges and 2-cuts representing amide bonds, and measuring the masses of the connected components. By applying spectral networks³⁷, DEREPLICATOR enabled identification of variants of known PNPs. While DEREPLICATOR search of GNPS identified hundreds of peptides and their variants, it is limited to dereplicating PNPs and cannot identify other classes of natural products such as polyketides and terpenes.

Here we describe DEREPLICATOR+ algorithm for dereplicating spectra against diverse metabolites. After searching nearly two hundred million tandem mass spectra in the GNPS molecular networking infrastructure, DEREPLICATOR+ identifies five times more molecules than the previous approaches. DEREPLICATOR+ enables high-throughput identification of variants of known natural products by spectral networks.

Results

Outline of the DEREPLICATOR+ algorithm

Figure 1 and Supplementary Figure 1 show the DEREPLICATOR+ pipeline that includes the following steps described in the Methods section: (i) constructing metabolite graphs from metabolite chemical structures, (ii) generating fragmentation graphs, (iii) constructing decoy fragmentation graphs, (iv) annotating target and decoy fragmentation graphs by spectra and scoring metabolite-spectrum matches (MSMs), (v) computing statistical significance of MSMs and evaluating the false discovery rate (FDR), and (vi) enlarging the set of MSMs by molecular networking (Fig. 1).

Fig. 1.

DEREPLICATOR+ pipeline. The method consists of (i) constructing metabolite graphs from metabolites chemical structures, (ii) generating fragmentation graphs, (iii) constructing decoy fragmentation graph for each metabolite (iv) annotating target and decoy fragmentation graphs by spectra and scoring metabolite-spectrum matches (MSMs), (v) computing statistical significance of MSMs and evaluating FDR, and (vi) enlarging the set of MSMs by molecular networking

Datasets

To benchmark DEREPLICATOR+, we used the AntiMarin database (60,908 compounds and 29,491 unique compounds) and the Dictionary of Natural Products (254,727 compounds and 83,889 unique compounds). Compounds are flagged as duplicates if they have identical chemical structures. We searched all spectra from the following reversed-phase liquid chromatography high-resolution mass spectrometry datasets specified in Supplementary Table 1 and Supplementary Note 1. Spectra_ActiSeq (178,635 spectra from MSV000078604 and 473,135 spectra from MSV000078839) contains spectra obtained from bacterial extracts of 36 strains of Actinomyces with published draft genomes. The spectral dataset is partitioned into 36 subsets corresponding to these strains^32,38. Spectra_Library (5473 annotated spectra) contains a combination of spectral libraries from the GNPS public library, the NIH natural products library, the Food and Drug Administration natural products library, MASSBANK, HMDB, and RESPECT, all available from GNPS. Spectra_Lichen (926,864 spectra from MSV000078584) contains spectra obtained from extracts of Peltigera sp³⁹. Illumina paired end reads (82,722,940) of length 250 bp obtained from the same sample were assembled by metaSPAdes⁴⁰. The total length of contigs longer than 1000 bp in the assembled metagenome is 136 Mb. Spectra_Cyan (11,921,457 spectra from MSV000078568) contains spectra from extracts of cyanobacterial strains, partitioned into 317 datasets corresponding to individual collections⁴¹. Four of these strains have their genomes available (Moorea bouillonii PNG19MAY05, Moorea producens JHB22AUG96, M. producens NAK12DEC93-3La, and M. producens PAL15AUG08). Spectra_GNPS (248.1 million spectra) contains spectra from 555 GNPS datasets with 77,045 samples (deposited by over 200 labs before August 2017). Spectra_Fungi, Spectra_Acti, and Spectra_Pseudo refer to subsets of Spectra_GNPS containing spectra from Fungi, Actinomyces, and Pseudomonas, respectively. To identify the spectra coming from media and contamination, several analyzed samples (called blank samples) consisted only of media with no bacteria cultured in them.

Discovering chalcomycin variants from Actinomyces spectra

At 1% FDR (corresponding to the p value threshold 10⁻⁷ computed by MS-DPR⁴²), DEREPLICATOR identified 73 unique compounds (166 MSMs) in Spectra_ActiSeq. At 0% FDR (corresponding to the p value threshold 10⁻⁸), DEREPLICATOR identified 66 unique compounds (148 MSMs). In contrast, at 1% FDR (score threshold of 6), DEREPLICATOR+ identified 488 compounds and 8194 MSMs (1.2% of all spectra in Spectra_ActiSeq dataset). At 0% FDR (score threshold of 9), it identified 154 compounds (2666 MSMs), a twofold increase as compared to DEREPLICATOR.

DEREPLICATOR+ not only identified more unique compounds at the same FDR, but also identified more spectra per compounds (average of 2.2 spectra per compound for DEREPLICATOR, versus 16.7 spectra per compound for DEREPLICATOR+). This is partially because spectra from the same compound often differ in the quality of fragmentation. DEREPLICATOR is mainly limited to identification of the highest quality spectra since it uses a rather restrictive fragmentation model. DEREPLICATOR+ identifies spectra of lower quality since it uses a more detailed fragmentation model.

Among 154 compounds identified by DEREPLICATOR+ at 0% FDR, ClassyFire⁴³ classified 92 of them as peptides and amino acids derivatives, 32 as lipids, 5 as benzenoids, and 6 as other classes. Out of 154, 72 of these compounds have the known Actinomyces origin according to AntiMarin (Supplementary Data 1).

To analyze some of DEREPLICATOR+ identifications in more detail, we selected a very stringent score threshold of 15 (0% FDR, 29 compounds) and removed four compounds that were present in blank samples from the background media. Table 1 describes 24 = 29−5 compounds (covering 19 PNPs, 2 polyketides (PKs), 2 terpenes, and 1 benzenoid) identified by DEREPLICATOR+. These 24 metabolites form 15 metabolite families and reveal additional 557 variants of these metabolites through molecular networks^37,44. DEREPLICATOR missed 10 out of these 24 metabolites at 3% FDR (2 PKs, 2 terpenes, 1 benzenoid, and 5 short PNPs with <8 amide bonds). Supplementary Data 1 describes all the 488 identifications of DEREPLICATOR+ in Actinomyces spectra at 1% FDR threshold.

Table 1.

The list of top scoring 25 metabolites identified by DEREPLICATOR+in the search of the Spectra_ActiSeq dataset against the AntiMarin database at the score threshold of 15

compound	class	CN	CO	DEREP+p-value	DEREP p-value	producer	ref	gene similarity	Cmp
stenothricin-III	peptide	21	4	9.10−22	9.10−14	S. roseosporous	63	BGC0000431(100%)	0
doricin	peptide	16	4	3.10−27	2.10−10	S. pristinaespiralis	64	BGC0000952(97%)	74
arylomycin-A2	peptide	14	5	5.10−15	4.10−7	S. roseosporous	65	BGC0000306(88%)	26
WS-9326-A	peptide	16	5	1.10−20	4.10−17	S. griesoflavus	66	BGC0001297(100%)	2
arylomycin-A4	peptide	14	5	3.10−15	2.10−9	S.roseosporous	65	BGC0000306(88%)	26
ostreogrycin-B	peptide	16	3	1.10−17	7.10−8	S. pristinaespiralis	64	BGC0000952(97%)	74
SP-Chymostatin-B	peptide	12	2	6.10−9	4.10−10	Streptomyces sp. E14	67	unknown	95
pristinamycin-IC	peptide	16	3	1.10−15	2.10−9	S. pristinaespiralis	64	BGC0000952(97%)	0
salinamide-E	peptide	13	7	9.10−13	0.002	Streptomyces CNH287	68	BGC0001230(100%)	0
antimycin-B1	benzenoid	5	8	3.10−7	5.10−6	Streptomyces albus	62	BGC0000958(86%)	15
virginiamycin-S1	peptide	13	3	2.10−23	9.10−15	S. pristinaespiralis	64	BGC0000952(97%)	74
ostreogrycin-A	peptide	4	4	2.10−17	0.002	S. pristinaespiralis	64	BGC0000952(97%)	6
actinomycin-X2	peptide	24	4	5.10−19	3.10−10	Streptomyces CNS654	69	BGC0000296(71%)	2
A-21978-C2	peptide	30	7	8.10−13	3.10−9	S. roseosporous	70	BGC0000952(59%)	0
soyasaponin-I	triterpene	1	21	1.10−12	1	S. hygroscopicus	71	unknown	53
C35H56O13	polyketide	2	18	3.10−10	1	S. Mg1	46	unknown	4
nocardamine	peptide	12	1	4.10−15	3.10−8	S. Mg1	46	unknown	2
ostreogrycin-G	peptide	4	4	3.10−22	0.001	S. pristinaespiralis	49	BGC0000952(97%)	111
virginiamycin-M1A	peptide	4	4	6.10−17	0.02	S. pristinaespiralis	49	unknown	0
virginiamycin-S2	peptide	13	4	6.10−21	4.10−10	S. pristinaespiralis	49	BGC0000952(97%)	7
salinamide-A	peptide	14	8	1.10−19	1.10−7	Streptomyces CNB091	68	BGC0001230(100%)	5
chalcomycin	polyketide	1	18	1.10−9	1	S. Mg1	46	BGC0000047(64%)	3
soyasaponin-II	triterpene	1	20	2.10−10	1	S. Tu6071	71	unknown	53
WA-3854-A2	peptide	4	5	1.10−8	6.10−7	S. ghanaensis	72	unknown	9

For each compound we show its classification by ClassyFire (class), a software tool for metabolite classification⁴³, as well as the DEREPLICATOR+ p-value (DEREP+ p-value), and the DEREPLICATOR p-value (DEREP p-value). DEREPLICATOR p-values are computed using MS-DPR method⁴². For DEREPLICATOR+, p-value computation is described in the METHOD section. In all cases, the compounds have been reported in another Actinomyces species, and the corresponding references are shown. In 17 out of 24 cases, the compounds have known BGC, and in all these cases DEREPLICATOR+ identifications were validated by the BLAST search of the BGC. In each case, the number of compounds in the connected components of the molecular network (Cmp) for each identified metabolite is also shown. While soyasaponin was first discovered in plants⁷³, it was shown later that it is also produced by Streptomyces⁷¹. Number of nitrogen to carbon bonds (CN), and oxygen to carbon bonds (CO) in the molecular structures are also shown. Supplementary Data 1 is an extended version of this table, including a comprehensive list of all the 488 identifications of DEREPLICATOR+ in Spectra_Acti at 1% FDR

In 17 out of 24 cases, the compounds had a known biosynthetic gene cluster (BGC) reported in the MIBiG database, and in all these cases we cross-validated DEREPLICATOR+ identifications by searching their BGCs against the known genome of their producers (BLAST search found very similar homologs). Although the BGC is currently unknown for antibiotic C₃₅H₅₆O₁₃ (originally isolated from Streptomyces hirsutus)⁴⁵, we used C₃₅H₅₆O₁₃ identification in Streptomyces Mg1 to derive its BGC. Analysis of the PK gene clusters reported by antiSMASH in the Streptomyces Mg1 genome led to assignment of antibiotic C₃₅H₅₆O₁₃ to a PK BGC in Streptomyces Mg1. Kersten et al.⁴⁶ showed that this BGC is responsible for production of PK chalcomycin. The antibiotic C₃₅H₅₆O₁₃ is very similar to chalcomycin (differing only in a single monomer methylation), implying that this BGC is likely responsible for the production of both chalcomycin and antibiotic C₃₅H₅₆O₁₃. Analysis of the number of peptide bonds in the compounds identified by DEREPLICATOR and DEREPLICATOR+ revealed that DEREPLICATOR+ outperforms DEREPLICATOR in the identification of compounds with a small number of peptide bonds (Fig. 2).

Fig. 2.

The distribution of the number chemical bonds for identifications. The distribution of the number of a amide bonds and b C–O bonds in the compounds identified at 1% FDR by DEREPLICATOR+ only (shown in red), DEREPLICATOR only (shown in blue), and both (shown in green) in Spectra_ActiSeq. Over 33% (155 out of 464) of DEREPLICATOR+ identifications have a single or no amide bonds

DEREPLICATOR+ analysis of the Spectra_ActiSeq dataset identified 56 out of 207 compounds in the DNP annotated as Actionmyces/Streptomyces (Supplementary Data 2). Supplementary Figure 2 shows a comparison between DEREPLICATOR p values and DEREPLICATOR+ p values for 24 top identifications in Table 1.

After running DEREPLICATOR, DEREPLICATOR-CN, DEREPLICATOR-CN-CO, and DEREPLICATOR-G (defined in Methods sections) on Spectra_ActiSeq with FDR threshold 3%, they identified 75, 91, 404, and 496 compounds, respectively. In contrast, DEREPLICATOR+ identifies 1024 compounds at this FDR. DEREPLICATOR+ significantly improves on DEREPLICATOR in the case of short peptides with small number of amide bonds. As an example, DEREPLICATOR fails to identify the antibiotic arylomycin A4, a branch-cyclic peptide with five amino acids, at 3% FDR, while DEREPLICATOR+ discovered it at 0% FDR with p value 3 × 10⁻¹⁵. Supplementary Figure 3 compares arylomycin A4 annotation of DEREPLICATOR and DEREPLICATOR+.

Supplementary Note 2, Supplementary Data 1, 3, and 4, and Supplementary Figure 4 describe the performance of DEREPLICATOR+ on different fragmentation methods (collision-induced dissociation (CID) and higher-energy C-trap dissociation (HCD)), identification of frequent mass spectrometry contaminants⁴⁷, and results of searches of the larger databases.

The NIST spectral library search tool MSPepSearch identified 34, 12, and 12 compounds in searching Spectra_ActiSeq against NIST17, LipidBlast and MoNA. At 1% FDR DEREPLICATOR+ recovered 27/34 identifications from NIST, 8/12 identifications from LipidBlast, and 9/12 identifications from MoNA (Supplementary Data 5). At this FDR threshold DEREPLICATOR+ identified 315 compounds, 272 of them absent from LipidBlast, MoNA, and NIST search results.

Benchmarking DEREPLICATOR+ on spectral library

To benchmark the accuracy of DEREPLICATOR+ in identification of spectra from known compounds, we searched 5473 annotated spectra from Spectra_Library against a database of their 5473 chemical structures, plus 83,889 distinct chemical structures from the DNP database. We removed 2697 duplicate compounds that were shared between the spectral library and the DNP database, resulting in 86,665 distinct compounds. By allowing a large precursor mass tolerance of 0.5 Da, each spectrum is searched against 1235 compounds on average.

DEREPLICATOR+ correctly identified 34% (1878) compounds as the top rank prediction, while DEREPLICATOR-G and DEREPLICATOR identified 30% (1647) and 17% (964) compounds as top rank prediction. To break ties for matches with the same score, we took a pessimistic approach, assigning the lowest rank to the correct prediction. Since each spectrum was compared with 1235 structures on average, a random predictor would correctly identify only 5473/1235 = 4.4 spectra on average.

We reanalyzed DEREPLICATOR+, DEREPLICATOR-G, and DEREPLICATOR on Spectra_Library, reducing the precursor mass threshold from 0.5 Da to 0.02 Da. This reduced the search space from 1235 candidate structures to only 24 candidate structures per spectrum, and increased the number of correct identifications to 44% (2414), 40% (2216), and 25% (1362) for DEREPLICATOR+, DEREPLICATOR-G, and DEREPLICATOR, respectively (Fig. 3 and Supplementary Data 6). DEREPLICATOR+ identified 32% more PNPs, and overall 77% more compounds than DEREPLICATOR at 0.02 Da tolerance. MS-FINDER²⁸ identified 20% of compounds from Spectra_Library as top compounds, 27% as top three, and 38% as top 10 compounds using a precursor and product ion tolerance of 0.02 Da similar to what we used for DEREPLICATOR+ (Supplementary Data 7).

Fig. 3.

Searching 5473 MS/MS from Spectra_Library against a library composed of DNP and GNPS spectral library (86,665 compounds). Allowing for a precursor mass tolerance of 5 Da, DEREPLICATOR+ correctly identified 19% (1048) compounds as top rank identification, while DEREPLICATOR-G and DEREPLICATOR identified 12% (685) and 7% (384) compounds as top rank, respectively

We further observed a positive correlation between the success rate of DEREPLICATOR+ in identifying the correct compound as the top-scoring MSM, and the total number of oxygen–carbon (O–C) and nitrogen–carbon (N–C) bonds in the structure. For example, among compounds with 1–10 O–C/N–C bonds, 21% were correctly identified as the top-scoring MSMs, while the success rate increased to 38% for compounds with 11–20 O–C/N–C bonds, and 55% for compounds with 21–30 O–C/N–C bonds.

Among DEREPLICATOR+ identifications of spectra from Spectra_Library with DNP classification available (224 compounds), 28% (63) are classified as terpenes, 14% (32) are alkaloids, 13% (29) as flavonoids, 13% (28) as peptides, 8% (18) as aliphatic natural products, 8% (18) as simple aromatic natural products, 5% (11) as benzopyranoids, 4% (9) as steroids, 2% (5) as carbohydrates, 2% (5) as oxygen heterocycles, 1% (2) as lignans, 1% (2) as polycyclic aromatic natural products, 0.5% (1) as PKs, and 0.5% (1) as benzofuranoids (Supplementary Data 6).

At 3% FDR threshold, DEREPLICATOR+ identifies 3087 spectra in Spectra_Library, where for 1802 spectra the correct compound is ranked one, for 812 spectra the rank is two or three, and for 473 spectra the rank is four or above. If we define success as the prediction of the correct compound with rank 1, DEREPLICATOR+ has a recall rate of 33% (1802 out of 5473) and precision rate of 58% (1802 out of 3087). If we define success as the prediction of the correct compound among top 3 identifications, DEREPLICATOR+ has a recall rate of 48% (2614 out of 5473) and precision rate of 85% (2614 out of 3087). DEPLICATOR+ failed to correctly identify 67% (3671 out of 5473) spectra in the Spectral_Library dataset even though true-positive structures were present in the database.

Supplementary Note 3 and Supplementary Data 6, 8, and 9 describe the performance of DEREPLICATOR+ on isomer compounds, adducts, and negatively charges spectra.

Identifying heterocyst glycolipid from Lichen spectra

DEREPLICATOR+ identified 21 metabolites in the Spectra_Lichen dataset at 1% FDR (Supplementary Data 10). One of these metabolites, heterocyst glycolipid, was shown to be consistent with the genetic capacity detected in the lichen³⁹.

Identifying almiramides from Cyanobacteria spectra

DEREPLICATOR+ identified 791 distinct compounds (10,375 MSMs) at 1% FDR corresponding to the score threshold of 6 (Supplementary Data 11). DEREPLICATOR identified 64 PNPs at the p value threshold of 10⁻⁷ and 1% FDR³².

Almiramide is a linear hybrid NRP-PK with a 2-methyloct-7-enoic PK tail and amino acid sequence of Methyl-Val, Methyl-Val, Val, Ala, and Methyl-Phe isolated from Lyngbya majuscula PAB04NOV05-7⁴⁸. DEREPLICATOR+ run on Spectra_Cyan identified almiramide with 0% FDR not only in PAB04NOV05-7, but also in PAB4NOV05-1, PAP27OCT08-2, PAB9APR05-6, and PAP29JUN07-2. DEREPLICATOR failed to identify this NRP-PK at 1% FDR.

Among these datasets, metagenomics data for PAP27OCT08-2 is available. After assembling the short reads using metaSPAdes⁴⁰ and genome mining using antiSMASH⁴⁹, one of the NRPS-PKS gene clusters is assigned as the putative almiramide biosynthetic gene cluster based on the amino acid sequence and the PK genes (Supplementary Figure 5 and Supplementary Table 2). Almiramides PK tail, 2-methyloct-7-enoic, is identical to the PK tail of jamaicamide, which was shown to be synthesized by genes JamF–JamL in the jamaicamide gene cluster⁵⁰, and these genes are very similar to genes in the almiramide gene cluster. Moreover adenylation domains in this putative gene clusters encode for two methylated phenylalaline, an alanine and a methylated alanine. Although this computational analysis points to a putative BGC for almiramide, it would benefit from a follow-up experimental validation.

Identifying spectra from GNPS dataset

DEREPLICATOR+ identified 410,539 spectra (0.2% of all spectra in Spectra_GNPS) representing 5336 compounds at 1% FDR and the score threshold of 12 in this dataset (Supplementary Data 12). Sixty-seven compounds (3106 spectra) were found in the decoy database at the same FDR. DEREPLICATOR identified 41,403 spectra representing 974 compounds, while DEREPLICATOR-G identified 82,260 spectra representing 1417 compounds at 1% FDR. Figure 4 shows the number of compounds identified by DEREPLICATOR+ in Spectra_GNPS at different score thresholds. Among these 5336 identifications, 643 (12%) have mass below 400, 2421 (45%) have masses between 400 to 800, 1425 (26%) have masses between 800 to 1200 Da, and 847 (15%) have masses above 1200 Da (Supplementary Figure 6).

Fig. 4.

The number of matches to the target and decoy databases for the Spectra_GNPS dataset. At 1% FDR (score threshold of 12), DEREPLICATOR+ identified 5336 unique compounds (410,539 MSMs) in target and 67 unique compounds (3106 MSMs) in the decoy database

After removing spectra with less than five peaks from Spectra_GNPS, MS-Cluster⁵¹ partitioned it into 1,578,409 clusters. For 37,436 (2.3%) of those clusters, DEREPLICATOR+ identified at least one of the spectra in the cluster at 1% FDR (111 clusters have decoy identifications). Spectra_GNPS forms 37,750 spectral families in the spectral network³⁷, and DEREPLICATOR+ identified at least one member among 18,561 (49%) of these spectral families at 1% FDR (110 families have decoy identifications).

ClassyFire classified all compounds found by DEREPLICATOR as peptides. For DEREPLICATOR+ identifications, 2913 (55%) were classified as peptides, 1595 (30%) as lipids, 272 (5%) as organic oxygen compounds, 160 (3%) as organoheterocyclic compounds, 155 (3%) as PKs, and 133 (2.5%) as benzenoids. Figure 5 shows the distribution of compounds from different classes in the DEREPLICATOR identifications, DEREPLICATOR+ identifications, and the entire AntiMarin.

Fig. 5.

The distribution of compounds from different classes in GNPS. a The distribution of compounds from different classes among 739 identifications from DEREPLICATOR. b The distribution of compounds from different classes among 5336 identifications from DEREPLICATOR+. c The distribution of compounds from different classes among all 60,908 AntiMarin compounds. All compounds were classified with ClassyFire⁴³

Among 5336 DEREPLICATOR+ identifications in Spectra_GNPS, 692 have class annotations in the DNP database. Among them, 227 (32.8%) are terpenoids, 150 (21.6%) are aliphatic natural products, 101 (14.5%) are alkaloids, 69 (9.9%) are peptides, 39 (5.6%) are simple aromatic natural products, 31 (4.4%) are steroids, 21 (3.0%) are flavonoids, 14 (2.0%) are benzopyranoids, 12 (1.7%) are lignans, 7 (1.0%) are oxygen heterocycles, 7 (1.0%) are oxygen heterocycles, 5 (0.7%) are carbohydrates, 5 (0.7%) are polypyrroles, and 3 (0.5%) are PKs.

To compare performance of DEREPLICATOR+, DEREPLICATOR-G, and DEREPLICATOR on peptides, we classified all antimarin compounds into five categories, (i) non-peptides with three or less amino acids (79%), (ii) linear peptides (7%), (iii) cyclic peptides (3%), (iv) branch-cyclic peptides (2%), and (v) more complicated peptides (7%). Among 5336 DEREPLICATOR+ identifications, 1981 are non-peptides, 1331 are linear, 807 are cyclic, 706 are branch-cyclic, and 525 are peptides with more complicated structures. Among 1417 identifications by DEREPLICATOR-G, 175 are non-peptide, 143 are linear peptides, 607 are cyclic peptides, 369 are branch-cyclic peptides, and 123 are peptides with more complicate structures. Among 974 identifications by DEREPLICATOR, 133 are linear peptides, 487 are cyclic peptides, 298 are branch-cyclic peptides, and 56 are peptides with more complicate structures.

As an example of how DEREPLICATOR+ improves on DEREPLICATOR on peptides, we analyzed antrimycin A⁵², a peptide identified by DEREPLICATOR+ at 0% FDR in actinobacteria Kitasatospora cystargynea RLe10 (MSV000080284), but missed by DEREPLICATOR at 3% FDR. To distinguish between the effect of addition of O–C and carbon–carbon (C–C) bonds to our model, and fragmentation graph, we introduced a scoring DEREPLICATOR-PEP-FG, where only N–C bonds up to depth three are fragmented. DEREPLICATOR+, DEREPLICATOR-G, DEREPLICATOR-PEP-FG, and DEREPLICATOR assigned scores 27, 8, 8, and 5 to the PSM formed by antrimycin A. DEREPLICATOR-PEP-FG annotated three additional internal ions as compared to DEREPLICATOR, consistent with the previous annotations of the antrimycin A spectra (Supplementary Figure 3)⁵³ . MS-DPR⁴² assigned a p value of 1 × 10⁻⁶ and 2 × 10⁻¹⁰ to the PSMs formed by antrimycin using DEREPLICATOR and DEREPLICATOR-PEP-FG scoring, respectively.

Figure 6 shows the fraction of DEREPLICATOR+ identifications at 1% FDR in Spectra_Cyan, Spectra_Acti, Spectra_Fungi, Spectra_Pseudo, and Spectra_GNPS produced by various microbial sources. Majority of compounds identified in Spectra_Cyan, Spectra_Acti, and Spectra_Fungi come from Cyanobacteria, Actinomyces, and Fungi sources, respectively. In the case of Spectra_Pseudo, Pseudomonas is the second major source after Bacillus, due to the presence of bacillus molecules in the growth media used for this dataset.

Fig. 6.

The fractions of DEREPLICATOR+ identifications from various sources in Spectra_Cyan (112 compounds), Spectra_Acti (149 compounds), Spectra_Fungi (138 compounds), and Spectra_Pseudo (40 compounds). Majority of compounds identified in Spectra_Cyan, Spectra_Acti, and Spectra_Fungi come from Cyanobacteria, Actinomyces, and Fungi sources, respectively. In the case of Spectra_Pseudo, Pseudomonas is the second major source after Bacillus, due to contamination. The fractions of compounds from different sources in Spectra_GNPS (5336 compounds) and AntiMarin (60,908 compounds) are also shown

Among all spectra in Spectra_GNPS, 72.7% (230.1 million) are from the time-of-flight (TOF) instruments, while 18.1% (57.5 million) are from the Orbitrap instruments. Among identifications in Spectra_GNPS, 4.4 million (62.0%) are from the TOF instruments and 2.3 million (32.8%) are from the Orbitrap instruments (Supplementary Data 13).

Discussion

The GNPS molecular networking project has enabled searches of mass spectra against spectral libraries to identify known natural products and discover their variants. However, while spectra from GNPS represent a gold mine for future natural products discovery, their interpretation remains challenging. Currently, the GNPS spectral library, the most comprehensive spectral library of natural products, has <10,000 compounds. The vast majority of GNPS spectra have evaded all attempts to interpret them, indicating that there exists a large dark matter of metabolomics⁴.

Currently, only 0.2% of spectra from GNPS are identified by spectral library search. Many of the still unidentified spectra are likely formed by known molecules present in chemical structure databases such as PubChem. Identifying the spectra of the compounds which are present in chemical structure libraries but absent in spectral libraries requires the development of algorithms for matching spectra of natural products against chemical databases.

We recently developed DEREPLICATOR and VarQuest^32,54 tools for identifying PNPs and their variants through database search of mass spectra. However, these tools are not designed for identification of other types of natural products such as PKs and lipids. Generalizing them to other types of natural products requires both generalizing from amide bonds to other types of bonds that usually break during tandem mass spectrometry (e.g., C–O bonds and C–C bonds) and also handling the sequential process of a compound fragmentation as a fragmentation graph. Our results show that generalized bond breakage and the sequential fragmentation are both crucial in enabling DEREPLICATOR+ to identify 77% more compounds (consisting of non-peptide metabolites and mixed peptide-PKs) that were missed by DEREPLICATOR. Moreover, ≈33% of compounds identified by DEREPLICATOR+ have either a single or no amide bond.

In contrast to existing database search tools in metabolomics^23–36, DEREPLICATOR+ is the first database search tool for natural products that can (i) search the entire GNPS molecular networking infrastructure against large databases of chemical structures, and (ii) identify variants of known metabolites using molecular networking^37,44. Similar to all the existing metabolomics tools, DEREPLICATOR+ is not applicable to all the classes of compounds. DEREPLICATOR+ greatly improves on the existing tools for PNPs and enables identifying the important class of peptides-PKs and PKs.

Currently DEREPLICATOR+ uses a simple shared peaks scoring to match spectra against fragmentation graphs, and computing FDR for the found identifications. This simple scoring scheme results in a high false-negative rate. Meanwhile, DEREPLICATOR+ enables automatic collection of large metabolite spectra libraries by searching billions of spectra, similar to those in proteomics. This pave the path for machine learning methods to improve scoring scheme and enhance the false-negative rates.

Methods

Overview of the DEREPLICATOR algorithm

DEREPLICATOR³² generates the theoretical spectrum of a PNP by first constructing a peptide graph, where each node is an amino acid and each edge is a peptide bond. Afterwards, it considers various fragmentations of the peptide graph by removing each possible bridge or 2-cut (each such removal breaks the peptide graph into two connected components). DEREPLICATOR generates the theoretical spectrum of a peptide as the set of masses of all resulting connected components, where the mass of a component is defined as the total mass of its constituent amino acids. Afterwards, it compares each experimental spectrum against the theoretical spectra of all PNPs in a database and finds the best scoring PSM. The statistically significant PSMs (as defined by their p values) reveal identified PNPs.

Outline of DEREPLICATOR+ algorithm

While DEREPLICATOR was shown to accurately identify spectra of PNPs, this approach is not applicable to other types of metabolites that go through multiple fragmentations at different bonds in a mass spectrometer. To address this complication, DEREPLICATOR+ introduces the concept of the fragmentation graph. Below, we describe various steps of the DEREPLICATOR+ algorithm for identifying spectra of all metabolites.

Constructing metabolite graphs

Supplementary Tables 3 and 4 show prevalence of different atoms and bonds in natural products from the AntiMarin database. In addition to N–C bonds in peptides, metabolites often break at O–C and C–C bonds. We thus refer to N–C, O–C, and C–C bonds as metabolite bonds. Disconnecting all metabolite bonds in a metabolite breaks it into connected component that form the nodes of the metabolite graph labeled by their elemental composition (the mass of the node is defined as the total mass of elements in the connected component). Edges in the metabolite graph correspond to metabolite bonds (Fig. 7). The theoretical spectrum of a metabolite graph is defined by the masses of all connected subgraphs of the metabolite graph formed by disconnecting bridges and 2-cuts, and the DEREPLICATOR-G score refers to the count of shared peaks between the experimental spectrum and the theoretical spectrum of the metabolite graph within a tolerance threshold. We further classify all 2-cuts into feasible (formed by either C–N or C–O bonds) and infeasible (containing at least one C–C bond).

Fig. 7.

From metabolite structure to metabolite graph. a Structure of a polyketide chalcomycin, b the metabolite graph of chalcomycin with 43 nodes and 46 edges, c structure of a terpene heterocyst glycolipid, and d metabolite graph of heterocyst glycolipid with 40 nodes and 40 edges. Each node in the metabolite graph is a connected component of the structure after dissociating all metabolite bonds. C–C bonds are shown in blue and C–O bonds are shown in red. The metabolite graph of chalcomycin has 43 nodes and 46 edges, including 18 bridges, 8 feasible 2-cuts, and 20 infeasible 2-cuts. The metabolite graph of heterocyst has 40 nodes and 40 edges including 34 bridges, 2 feasible 2-cuts, and 4 infeasible 2-cuts

Generating fragmentation graphs

Since metabolites often go through multiple fragmentations at different bonds during tandem mass spectrometry, theoretical spectra formed by all bridges and 2-cuts of the metabolite graphs poorly model real spectra. We thus model a theoretical spectrum of a metabolite as a fragmentation graph originally introduced for de novo sequencing of PNPs by multistage mass spectrometry⁵⁵. Note that our concept of the fragmentation graph (constructed based on the chemical structure of a known metabolite) differs from the fragmentation trees introduced in Rasche et al.⁵⁶, which are constructed based solely on mass spectra to characterize chemical formula and potential substructure motifs of the metabolites²⁹. Constructing fragmentation graphs solely from structures has computational advantages over the relatively slow fragmentation tree approach since it allows one to precompute fragmentation graphs for all structures in a single scan of a chemical database, thus enabling fast analysis of large spectral datasets. The additional advantage of constructing fragmentation graphs from chemical structures is that, in contrast to fragmentation trees⁵⁶, this method can capture substructures absent from the public substructure databases such as PubChem CACTVS and Klekota–Roth⁵⁷.

Disconnecting a bridge or a 2-cut in a graph G breaks it into connected components that we call descendants of G. Given a metabolite graph G, its fragmentation graph is defined as follows. The nodes of the graph are defined as all possible connected subgraphs of G and the edges of the graph are defined as directed edges connecting subgraphs to their descendants. We define the complete metabolite as the root of the graph, and define the depth of each node in the graph as the length of the shortest path from the root to this node (Fig. 8). Since mass spectrometry experiments with conventional collision energies usually do not produce many fragment ions corresponding to 3-cuts, we do not analyze 3-connected metabolite graphs. Only 70 metabolite graphs arising from compounds in AntiMarin (0.1%) are 3-connected. We further trim the fragmentation graph to eliminate nodes that correspond to unrealistic fragmentation rules.

Fig. 8.

Generating the fragmentation graph. a DEREPLICATOR[32] generates the theoretical spectrum of a PNP by first constructing a peptide graph, where each node is an amino acid and each edge is a peptide bond. Afterwards, it considers various fragmentations of the peptide graph by removing each possible bridge and 2-cut (each such removal breaks the peptide graph into two connected components). DEREPLICATOR generates the theoretical spectrum of a peptide as the set of masses of all resulting connected components, where the mass of a component is defined as the total mass of its constituent amino acids. b DEREPLICATOR+ sequentially breaks the metabolite graph at multiple bridges and 2-cuts, and models a theoretical spectrum of a metabolite as a fragmentation graph. Only a small subgraph of the fragmentation graph is shown

Given a parameter k, we use the following constraints to filter down the nodes in the fragmentation graphs: (i) all nodes with depths exceeding k are removed from the graph, (ii) only nodes with a path from the root consisting of maximum one 2-cut fragmentation are retained in the graph, (iii) 2-cut fragmentations are limited to feasible 2-cuts, and (iv) only nodes with a path from the root consisting of maximum one C–C bond fragmentation are retained in the graph. We use constraints (iii) and (iv) because, while the C–C bonds are common in natural products, they are less likely to break as compared to the C–N and C–O bonds. We evaluated the rationality of these rules by comparing 14 different fragmentation models on the Spectra_library dataset based on the total log likelihood of the known MSMs in comparison to the null fragmentation model that assumes that all the MSMs are random (Supplementary Note 4 and Supplementary Data 14). Our results show that, the optimal value of k is 6 in condition (i), and that conditions (ii)–(iv) represent rational rules to prune the fragmentation graph.

We constructed the fragmentation graph of heterocyst glycolipid⁵⁸ with chemical formula C₃₂O₈H₆₄. When considering only constraint (i), this fragmentation graph has 99 nodes at depth 1, 2721 nodes at depth 2, and 7216 nodes at depth 3. After trimming the graph with constraints (ii), (iii), and (iv), it has 69 nodes at depth 1, 632 nodes at depth 2, and 1182 nodes at depth 3. Note that there might be multiple nodes with identical masses and chemical formulas that are produced by different bridges and 2-cuts in the fragmentation graph.

Generating decoy fragmentation graphs

Supplementary Figure 7 shows the histogram of masses of all experimental peaks in the Spectra_Lichen dataset³⁹, and histogram of masses of all peaks from the theoretical spectra of metabolites in the AntiMarin database. We normalize the histograms of masses of experimental peaks to turn it into probability distributions and refer to the probability of an experimental peak being in the interval [x, y] as F(x, y). Similarly to generating decoy spectra for spectral library searches⁵⁹, DEREPLICATOR+ generates a decoy fragmentation graph for each target fragmentation graph. This is achieved by fixing the decoy structure identical to the target, and proceeding in a breadth-first manner by assigning a mass to each node in the decoy fragmentation graph as follows. The mass of the root is equal to the total mass of the metabolite, and for each node v, DEREPLICATOR+ samples mass(v) from the range [0,Mass(Parent(v))] of the distribution of all theoretical peaks, where Parent(v) is parent of the node v. In cases where a node has multiple parents in a graph, we consider the parent with minimum Mass (Supplementary Figure 8). Because we use fragmentation graphs from all AntiMarin compounds to learn the distribution from which we sample decoy fragmentation graphs, it captures the masses common in natural products.

Annotating fragmentation graphs by spectra and scoring MSMs

Similar to MS-Cluster and Molecular Networking approaches^37,44, DEREPLICATOR+ processes all spectra by retaining only six top intensity peaks in each window of size 50 Da. Given a spectrum, we annotate the nodes in a fragmentation graph in the breadth-first manner, starting from nodes at depth 1 (the root is assumed to be annotated). We say that a node is annotated by a spectrum, if (i) at least one of its direct ancestors is annotated, and (ii) its mass is explained by a peak in the spectrum. Given a metabolite M and spectrum S, we define score(M,S) as the number of unique masses in S that annotate a node in the fragmentation graph of M. We also define score_i(M,S) as the number of unique masses in S that annotate a node at the depth i and lower in the fragmentation graph of M. Using a threshold of 0.02 Da on ion detections for the spectrum of heterocyst glycolipid with 44 peaks, 2 peaks are annotated at depth 1, 3 peaks at depth 2, and 9 peaks at depth 3 (Fig. 9).

Fig. 9.

Annotating the fragmentation graph. The MSM formed by the fragmentation graph of a heterocyst glycolipid, and b its spectrum. While only 2 and 3 peaks out of 44 peaks in heterocyst glycolipid mass spectra get annotated at depths 1 and 2, respectively (shown in red and green), 9 peaks get annotated at depth 3 (shown in blue), resulting in a score of 2 + 3 + 9 = 14. c The MSM formed by the fragmentation graph of c chalcomycin, and d its mass spectrum. 5, 9, and 3 out of 42 peaks in chalcomycin spectrum are annotated at depth 1, 2, and 3, respectively, resulting in a score of 5 + 9 + 3 = 17

Computing the statistical significance of MSMs and evaluating FDR

We refer to the set of all depth 1 nodes of the fragmentation graph of a metabolite M as Children(root) and the number of peaks in S as |S|. Given an MSM formed by a metabolite M and a spectrum S, we estimate its statistical significance with respect to score₁(M,S) based on the following probabilistic model for a random MSM.

All the nodes from Children(root) are annotated independently with the probabilities as defined below. The score is calculated as the number of the annotated peaks and the probability of the random event of peak annotation is computed from the empirical distribution F of all experimental peaks shown in Supplementary Figure 7 in the range from mass 0 to the precursor mass of the spectrum S. The statistical significance of a match between metabolite M and spectrum S is defined as the ratio of random MSM scores exceeding or equal to score₁(M,S).

The probability p that a random experimental peak sampled from range [0, Mass(M)] of the experimental distribution annotates a theoretical peak m within tolerance δ is computed as (Supplementary Figure 9):

$$p\left(m\right)=F\left(m-\delta ,m+\delta \right).$$ 1

Given |S| experimental peaks, the probability that at least one of them annotates a theoretical peak m can be computed as:

$$q\left(m\right)=1-{\left(1-p\left(m\right)\right)}^{\mid S\mid }$$ 2

and the probability of having exactly s matches between metabolite graph M and spectrum S has a Poisson binomial distribution (note that due to our probability model definition the annotation events are independent):

$$P\left(\mathrm{number}\mathrm{of}\mathrm{match}=s\right)={\sum }_{\mathrm{all}\mathrm{subsets}I\mathrm{of}\mathrm{peaks}\mathrm{in}\mathrm{Children}\left(\mathrm{root}\right)\mathrm{of}\mathrm{size}s}{\prod }_{mϵI}q\left(m\right){\prod }_{m\notin I}\left(1-q\left(m\right)\right).$$ 3

Since there are |Children(root)| terms in the Poisson binomial distribution, it is not feasible to compute it in a brute force manner for large theoretical spectra and scores. However, the Poisson binomial distribution at value s is equal to the coefficient of Z^s in the following generating polynomial⁶⁰:

$${\prod }_{m\in \mathrm{Children}\left(\mathrm{root}\right)}\left(1-q\left(m\right)+q\left(m\right)Z\right)$$ 4

and this formula leads to an efficient approach for computing the p value of score₁.

This procedure can be generalized to scores at higher depths (score_i for i > 1) in the case of fragmentation graphs with tree structures. For leaf node u with mass m_u, we define the generating polynomial H_u as

$$H_u\left(Z\right)=1-q\left(m_u\right)+q\left(m_u\right)\cdot Z$$ 5

and for non-leaf nodes, we define:

$$H_u\left(Z\right)=1-q\left(m_u\right)+q\left(m_u\right)\cdot Z\cdot {\prod }_{v\in \mathrm{Children}\left(u\right)}{H}_v\left(Z\right).$$ 6

Following this recursion, H_root contains probability distribution of the score. In general case of non-tree fragmentation graphs, the proposed recursion provides a rough approximation in the considered probabilistic model due to statistical dependence of annotation events in vertices sharing descenders. For unbiased probability estimation Markov Chain Monte Carlo methods can be utilized.

In the case of heterocyst glycolipid discovered in Spectra_Lichen, the probability that 2 or more out of 44 peaks in its spectrum are explained by children of the root with a tolerance of 0.02 Da is 0.026. For a child of the root with mass 396.39 Da, the probability that 3 or more of its children are annotated is 0.0003 Da. For a child of this node with mass 378.38, the probability that 9 or more of its children are annotated is 3 × 10⁻¹⁵.

Enlarging the set of MSMs using molecular networking

The concept of spectral networks³⁷ (also known as molecular networks⁴⁴ in the field of natural products) was introduced to reveal spectra of related peptides within a proteomic dataset without knowing what these peptides are. While these networks were first introduced for linear peptides, they were later generalized to cyclic peptides and metabolites^44,61. Nodes in a molecular network correspond to spectra, while edges connect spectra that are generated from related metabolites (e.g., metabolites differing by a single variation). The variations that are captured by molecular networks help to infer mutations, modifications (such as oxidation and acetylation), or adducts (such as sodium and potassium).

DEREPLICATOR+ constructs the molecular network of all spectra and selects connected components with at least one identified spectrum. Using the MSM corresponding to this spectrum, it annotates variants of identified metabolites. Supplementary Figure 10 shows the molecular network of the chalcolmycin family identified by DEREPLICATOR+.

Versions of DEREPLICATOR+

To analyze the differences between DEREPLICATOR and DEREPLICATOR+, we analyzed the Spectra_ActiSeq dataset using three versions of DEREPLICATOR+ described below. DEREPLICATOR-CN is a version of DEREPLICATOR, where all C–N bonds are cut rather than amide bonds only. DEREPLICATOR-CN-CO is a version of DEREPLICATOR, where all C–N and all C–O bonds are cut. In DEREPLICATOR-G all C–N, C–O, and C–C bonds are cut. DEREPLICATOR+ differs with DEREPLICATOR-G in consideration of depth 2 and depth 3 fragmentations in addition to depth 1.

Code availability

DEREPLICATOR+ is available as both a stand-alone tool (http://mohimanilab.cbd.cmu.edu/software/) and a web application (http://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp).

Author contributions

H.M., A.G., A.S., A.M., and A.K. implemented DEREPLICATOR algorithm. H.M., E.S. and L.C. performed the analysis. L.-F.N. analyzed the results of the analysis. H.M., P.C.D. and P.A.P. designed and directed the work. H.M. and P.A.P. wrote the manuscript.

Data availability

All datasets analyzed in this study are available through GNPS infrastructures with access codes available in the Supplementary Table 1. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Competing interests

P.A.P. has an equity interest in Digital Proteomics, LLC, a company that may potentially benefit from the research results. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. The remaining authors declare no competing interests.

Electronic supplementary material

Supplementary Information accompanies this paper at 10.1038/s41467-018-06082-8.