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Article Cite This: ACS Omega 2019, 4, 6229−6237
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Chemical Diversity of Cyanobacterial Compounds: A Chemoinformatics Analysis Mariana Gonzaĺ ez-Medina and Jose ́ L. Medina-Franco*
ACS Omega 2019.4:6229-6237. Downloaded from pubs.acs.org by 5.101.217.120 on 04/03/19. For personal use only.
Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico ABSTRACT: Cyanobacterial metabolites are natural products with attractive biotechnological applications and distinctive chemical structures. In this work, we discuss the results of a chemoinformatic analysis of the diversity of the chemical structures of cyanobacterial metabolites from marine and freshwater environments. The diversity analysis was based on calculated molecular properties of pharmaceutical interest, chemical scaffolds using the Bemis−Murcko definition, and extended connectivity molecular fingerprints. To this end, we used the Platform for Unified Molecular Analysis, which is an open web-based chemoinformatic server, to quantify the structural diversity of compound data sets. It was found that most molecular properties of cyanobacterial compounds are significantly different depending on whether they come from a marine or freshwater environment. Marine and freshwater metabolites have, in general, different scaffolds with only about five percent of scaffold overlap. Comparative analysis of the scaffolds and structural fingerprints indicated that the chemical structures of cyanobacteria metabolites from the marine source are, overall, more diverse than the chemical structures of the cyanobacteria metabolites from freshwater.
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INTRODUCTION Cyanobacteria are microorganisms that have complex mechanisms to biosynthesize compounds.1 They are capable of performing oxygenic photosynthesis, a process that contributed to the increase of oxygen in the early atmosphere2 and allowed the development of higher forms of life.3 Nowadays, cyanobacteria are primary fixers of carbon and nitrogen and play an important role sustaining the biosphere homeostasis.4 During their evolution, cyanobacteria have developed a prolific secondary metabolism that is widely studied for their ecological implications and biotechnological applications.5 Cyanobacterial toxins are the focus of extensive ecotoxicological studies because of their association with toxic events and disease.6 Released in harmful algal blooms, cyanotoxins can induce hazardous effects to exposed individuals.7 From a biotechnological perspective, cyanobacteria are a promising source of biofuels8 (e.g., isobutyraldehyde and isobutanol),9 food supplements (e.g., Spirulina platensis10,11 and Nostoc commune),12 and bioplastics (e.g., polyhydroxyalkanoates). In addition to the applications described before, cyanobacteria are a rich source of bioactive compounds with pharmaceutical applications that range from cytotoxicity; antimicrobial, antiviral, protease inhibition; to anti-inflammation.13,14 Cyanobacteria can be found in a range of morphological types such as unicellular, surface-attached, filamentous colonyand mat-forming species15 and therefore thrive in diverse terrestrial, marine, and freshwater habitats.16 Given the wide range and applications of cyanobacteria, most research groups focus their efforts on the study of cyanobacteria from one environment type. This leads to scientific reports concerning © 2019 American Chemical Society
secondary metabolites, studied from a certain environment, to be strongly associated with biological activities related to the scope of some research groups. This problem can be observed in freshwater metabolites, which are mostly reported as protease inhibitors, while marine metabolites have been associated with a wider range of biological activities. Although it is well-known that cyanobacteria are capable of synthesizing an extensive variety of natural products with diverse and complex structures,17,18 the chemical diversity of cyanobacterial compounds has been quantified only on a limited basis. A recent study reported the comparison of the chemical structures of cyanobacterial natural products with natural products from other sources. This study demonstrated that cyanobacteria have different and interesting chemical structures.19 The goal of this manuscript is to discuss the results of a comprehensive quantitative analysis of the chemical diversity of cyanobacterial metabolites published from January 2009 to June 2017. The analysis was done using chemoinformatic methods well established to assess the chemical diversity of compound data sets.20
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MATERIALS AND METHODS Data Sets and the Overall Strategy. The data sets were obtained through a systematic search in ScienceDirect (www. Received: February 25, 2019 Accepted: March 25, 2019 Published: April 3, 2019 6229
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relevant to describe the polarity (nHBDon, nHBAcc, and ALogP), flexibility (TopoPSA and nRotB), and size (MW) of the compounds. To determine whether there is a significant difference between the calculated properties between cyanobacteria metabolites from marine and freshwater sources, we used the Wilcoxon rank-sum test. Chemical Scaffolds. The term scaffold is used to describe the core structure of a compound to which functional groups are attached.25 Scaffold diversity is one of the many criteria used to characterize data set diversity26 and screening libraries. Given their well-documented chemical and structural diversity,27 natural products have the potential to offer novel scaffolds to create diverse screening libraries, which lead to new structures that become drugs or compounds in clinical development.28,29 To analyze the scaffold diversity of the cyanobacterial metabolites, the Bemis and Murcko30 scaffold representation was computed. The Murcko framework method systematically removes side chains to convert molecules into ring systems, joined by linkers, and creates a framework that retains information on atom type. All acyclic compounds were assigned the same chemotype identifier (ID) and also included in this study. In this work, we refer to acyclic compounds and scaffolds as chemotypes. The scaffold diversity was quantified using the standard method cyclic system recovery (CSR) curves and Shannon entropy.31 Briefly, a CSR curve plots the fraction of chemotypes (x-axis) versus the fraction of compounds that contain those chemotypes (y-axis). The curves can be further characterized by calculating the area under the curve (AUC), and the fraction of chemotypes required to retrieve 50% of the molecules (F50). Structural Fingerprints. To compare the structural diversity of the entire molecules (i.e., scaffolds and side chains) between cyanobacterial compounds from freshwater and marine sources, we used extended connectivity molecular fingerprints32 with a diameter of 4 (ECFP_4). This is an iterative, topological molecular fingerprint that compares fragments of molecules starting from a diameter of 0 up to the desired diameter. This process is repeated to describe the entire molecule. The fragments are then assigned to a position in the fingerprint table using a hashing function, where positions with a fragment assigned will get a value of 1 and position without an assigned fragment to get a 0. The Tanimoto coefficient was used to measure the ratio of the set
scie ncedirect.com ) and Web of Science (w ww. webofknowledge.com) in the period of January 2009 to June 2017. The search terms were “cyanobacterium” OR “cyanobacteria” AND “natural products”. The search also covered bibliographic references of the included studies. Inclusion criteria were cyanobacterial origin, the first report of the compound or analogue, and unambiguous structure elucidation. The search yielded 578 cyanobacterial compounds collected in different environments. We analyzed the chemical diversity of 560 cyanobacterial metabolites from three major environmental sources: marine, terrestrial, and freshwater. Those compounds that were not part of any of these groups were excluded. For this analysis, compounds isolated from terrestrial and freshwater cyanobacteria were grouped together and herein are referred to as “freshwater” (Table 1). To assess the chemical diversity of these molecules, three major criteria were considered: molecular properties of pharmaceutical interest, molecular scaffolds, and structural fingerprints. These criteria have been used to analyze natural compounds from different sources and origin.21 The results and figures were obtained using the Platform for Unified Molecular Analysis,22 a webbased application to calculate the molecular properties, visualize the chemical space, and compute the structural and chemical diversity of compound databases. Table 1. Sources of Cyanobacterial Metabolites and Number of Compounds Analyzed in This Work data set
number of compounds analyzed
freshwater marine
279 281
Properties of Pharmaceutical Interest. The first molecular representations employed to assess the molecular similarity between freshwater and marine compounds were six molecular descriptors commonly used to compare new compounds with approved drugs23 and suggest whether these compounds could be orally active: number of hydrogen bond donors (nHBDon), number of hydrogen bond acceptors (nHBAcc), octanol−water partition coefficient (ALogP), topological polar surface area (TopoPSA), number of rotatable bonds (nRotB), and molecular weight (MW). These properties were computed with the R package rcdk,24 which are
Table 2. Statistical Distribution of MW, Topological Polar Surface Area (TopoPSA), Hydrogen Bond Donors (nHBDon), Hydrogen Bond Acceptors (nHBAcc), Number of Rotatable Bonds (nRotB), and Octanol−Water Partition Coefficient (ALogP) property MW nHBAcc nRotB ALogP nHBDon TopoPSA
source
min
1st Qu
median
mean
3rd Qu
max
std. dev
freshwater marine freshwater marine freshwater marine freshwater marine freshwater marine freshwater marine
298 174 0 0 0 1 −18 −14 0 0 20 20
632 522 10 6 10 8 −7 −3 6 1 188 99
900 693 18 11 15 10 −4 −2 8 3 295 163
861 686 16 11 14 14 −5 −2 8 3 277 166
1050 825 23 14 18 19 −2 0 10 4 353 208
1780 1459 44 35 40 56 4 5 24 19 713 573
299 248 9 6 7 9 4 3 4 3 131 91
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Figure 1. Distribution of six molecular properties of pharmaceutical relevance.
drugs.21,33 The distinctive molecular complexity and structures of natural products as compared to other compounds attract the attention of researchers to further analyze the chemical diversity of natural products from different sources.19 Properties of Pharmaceutical Interest. Table 2 summarizes the statistical distribution of the six calculated molecular properties. As none of the molecular properties follows a normal distribution, we used Wilcoxon rank-sum test to determine if there is a significant difference between the two groups. Overall, there is a significant difference between freshwater and marine metabolites for most of the molecular properties. The only molecular property for which there is no statistical difference is nRotB. Although there are some hydrophilic outliers in the marine data set, on average, freshwater metabolites are significantly more hydrophilic than marine compounds, with higher MW and TopoPSA and more nHBDon and nHBAcc. The histograms in Figure 1 show how the frequency bars of the freshwater compounds are shifted toward higher values for all the properties except ALogP, for which the compounds have lower and negative values. Overall, the profile of the six physicochemical properties of cyanobacteria metabolites with the same set of properties reported for natural products from different sources (e.g., fungi metabolites, marine natural products, or plants) reveals that cyanobacterial metabolites are different with a notable increased MW.19,21 The chemical space spanned by the cyanobacterial metabolites was defined by the six molecular properties describing each compound. To be able to visualize these properties in two dimensions and facilitate the analysis and exploration of chemical diversity between groups, principal component analysis (PCA)34 was used for data dimensionality reduction. Figure 2 depicts the first two principal components
of structural features that two compounds have in common with respect to the total structural features of both compounds.
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RESULTS AND DISCUSSION
Overall, natural products are structurally different and more complex than molecules from combinatorial synthesis and
Figure 2. Visual representation of the chemical space of cyanobacterial compounds. PCA of six molecular properties of pharmaceutical relevance is listed in Table 2. The first two PCs recovered 89% of the variance. Selected compounds are labeled and their chemical structures are presented in Figures 3 and 4. 6231
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Figure 3. Chemical structures of cyanobacterial compounds from freshwater that are outliers in the property-based chemical space. The values of each of the six properties are indicated. The relative position of each compound in the chemical space is shown in Figure 2.
cathepsin inhibitors isolated from samples of Lyngbya cf. confervoides collected near Grassy Key and Key Largo, USA.35 Aeruginazole DA1497 (Figure 3) is a cyclic peptide isolated from Microcystis aeruginosa, a freshwater cyanobacteria, that has moderate antimicrobial activity against Staphylococcus aureus.36 Anacyclamide A15, a molecule also with high MW (Figure 3), is an example of a cyanobactin which is a ribosomal cyclic peptide produced by cyanobacteria.37 Acyclolaxaphycin B and acyclolaxaphycin B3 (Figure 4) are acyclic dodecapeptides found in the extract of Anabaena torulosa.38 Mooreaside A is a cerebroside isolated from the marine cyanobacterium Moorea producens.39 Table 3 indicates that MW, TopoPSA, nHBDon, nHBAcc, and ALogP have higher weight contributing to variability in
(PC1 and PC2) used to visualize the chemical space of marine and freshwater compounds. These two PCs are newly formed vectors obtained by linear combinations of the previous six vectors with each molecular property assigning a higher weight to those molecular properties that are more important for the variability within the compound data set under investigation. The first two PCs recovered 89% of the covariance. Selected outliers are identified in Figure 2, and their chemical structures, along with the molecular properties, are shown in Figures 3 and 4. Hassallidins C and D, indicated in Figure 2 as outliers in the cyanobacterial chemical space, are large glycosylated lipopeptides containing 16-carbon β-hydroxy fatty acids. They have large MW, TopoPSA, nHBDon, and nHBAcc, and low ALogP. Grassystatins A−C are potent and selective 6232
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Figure 4. Chemical structures of cyanobacterial compounds from a marine source that are outliers in the property-based chemical space. The values of each of the six properties are indicated. The relative position of each compound in the chemical space is shown in Figure 2.
the outliers in Figures 2−4. Those that are shifted to the right on the PC1 axis are those with higher MW, TopoPSA, nHBDon, nHBAcc, and lower ALogP, suggesting that these metabolites are more hydrophilic. nRotB has a higher contribution to the second principal component (PC2) and is not strongly correlated with the other five molecular properties. Compounds shifted toward negative values on PC2 have more rotatable bonds and compounds with more positive PC2 values have less rotatable bonds. Molecular diversity techniques are based on systematic pairwise comparisons. Herein, the pair-wise comparison of compound distances in terms of molecular properties was calculated using Euclidean distance to compare the inter- and intra-set molecular property dissimilarity. First, all the pair-wise Euclidean distances were computed and then the mean interand intra-set Euclidean distances were used to generate the
Table 3. Contribution of the Six Molecular Properties to Each PC property
PC1
PC2
MW TopoPSA nRotB nHBDon nHBAcc ALogP
0.420 0.450 0.282 0.424 0.440 −0.411
0.118 0.206 −0.949 0.127 0.164 0.003
PC1. For this data set of cyanobacterial compounds, there is an inverse correlation between MW, TopoPSA, nHBDon, nHBAcc, and ALogP. This means that if there is an increase in the first four molecular descriptors, the ALogP of the cyanobacterial metabolite decreases. This can be observed on 6233
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Figure 6. CSR curves. Table 4 summarizes the statistics. Figure 5. Euclidean distance of marine and freshwater cyanobacterial compounds based on six molecular properties of pharmaceutical relevance (Table 2). Largest inter- and intra-data set distances are marked in dark red and the shortest Euclidean distances are marked in white.
Table 5. SSE of the 10−60 Most Populated Scaffolds
distance matrix shown in Figure 5. According to these values, freshwater compounds have the largest intra-data set distance, suggesting that this group has a larger chemical diversity. To determine whether there is a significant difference between the properties of marine and freshwater compounds, we used the Wilcoxon rank-sum test that is suited for nonpaired data that does not follow a normal distribution. According to Wilcoxon’s test result, diversity is not significantly different between freshwater and marine secondary metabolites. Chemical Scaffolds. Table 4 summarizes the number of unique chemotypes (N) and the number of chemotypes containing only one compound (NSING). The fraction of chemotypes and singletons relative to the number of molecules in the data set was analyzed (FNM and FNSING.M, respectively). CSR curves were computed for each data set to analyze the distribution of chemotypes (Figure 6). To generate the CSR curves, the fraction of chemotypes was plotted on the x-axis and the fraction of compounds that contain those chemotypes was plotted on the y-axis. CSR curves were characterized by calculating the AUC and the fraction of chemotypes required to retrieve 50% of the molecules (F50). A data set with maximum diversity would contain a different chemotype for each molecule in the library. In this case, the CSR curve would be represented with a diagonal with an AUC of 0.5. The CSR curve for the marine data set (Figure 6) indicates that this data set is more diverse by scaffolds than in freshwater with an AUC and F50 of 0.76 and 0.10, respectively. Table 4
data set
SSE10
SSE20
SSE30
SSE40
SSE50
SSE60
freshwater marine
0.917 0.937
0.882 0.929
0.869 0.919
0.87 0.914
0.863 0.916
0.856 0.908
indicates that there are more scaffolds in the marine data set with an FNM of 0.40. To determine if there is a significant difference in the proportion of scaffolds and singletons, we used z score, which indicates that the difference was not significant for FNM, FNSIN.N, or F50. Scaled Shannon entropy (SSE)31 is a metric used to quantify the relationship between the entropy and information content. If an entropy close to 0 is obtained using the number of compounds distributed in a number of the most frequent chemotypes (e.g., the top 60 most populated chemotypes), this indicates that all the compounds share the same chemotype and it would make sense to say that this data set will give more information about which chemotype we can observe for the compounds on our data set. SSE will be equal to 1 (its maximum value) only when all chemotypes contain the same number of compounds or when each chemotype contains only one compound. Table 5 summarizes the SSE for the first 60 most frequent chemotypes in each library. Figure 7 shows the distribution and SSE values of compounds in the top 30 most frequent chemotypes. Table 5 indicates that marine compounds are more diverse, with SSE values ranging from 0.937 to 0.908, compared to freshwater compounds. The main difference can be observed in Figure 8. Freshwater compounds have three scaffolds with IDs 8, 43, and 35 containing 29, 26, and 24 compounds each, respectively. For the marine compounds, the first and most frequent scaffold (Figure 7) corresponds to acyclic systems (ID
Table 4. Scaffold Counts, AUC, and F50 Obtained for Both Data Setsa data set
M
N
FNM
NSING
FNSING.M
FNSING.N
AUC
F50
freshwater marine
279 281
105 113
0.38 0.40
61 62
0.22 0.22
0.58 0.55
0.76 0.73
0.10 0.15
a
M: number of molecules; N: number of chemotypes; F: fraction; NSING: number of chemotypes with only one compound; AUC: area under the curve; F50: fraction of chemotypes required to retrieve 50% of the molecules. 6234
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Figure 7. 30 Most frequent scaffolds of (A) marine and (B) freshwater cyanobacterial natural products. It indicates the value of SSE for the 30 most frequent scaffolds (SSE30). The number underneath each bar is an ID assigned to each scaffold.
Figure 8. Most frequent scaffolds found in freshwater and marine cyanobacterial compounds. The number under each structure indicates the scaffold ID/frequency of the corresponding scaffold in each data set.
frequent scaffolds, only one is present in both freshwater and marine compounds, namely the scaffold with ID 15 (Figure 8). Structural Fingerprints. The distribution of the pairwise ECFP_4/Tanimoto similarities of the compounds within each group (i.e. freshwater and marine) was analyzed by means of cumulative distribution function (CDF) curves.40 The curve for freshwater compounds is shifted to the right in Figure 9, indicating that these metabolites have higher pair-wise similarity and freshwater compounds are less diverse with a similarity median of 0.155 (Table 6).
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CONCLUSIONS Cyanobacteria continue to yield chemically diverse metabolites with biological activities. The chemoinformatic-based analysis of the chemical diversity reported in this work revealed that there is a statistically significant difference between marine and freshwater metabolites for all the molecular properties of pharmaceutical relevance except the number of rotatable bonds. On average, freshwater metabolites are of bigger size, more polar, and more hydrophilic than marine metabolites. The flexibility of cyanobacterial metabolites, explained by the number of rotatable bonds, is similar regardless of the source of the compound. The scaffold analysis revealed that marine and freshwater metabolites have, in general, different scaffolds with only ∼5% of scaffold overlap. Comparison of whole
Figure 9. CDFs of the pairwise similarity values calculated with the Tanimoto coefficient and extended connectivity fingerprints, diameter 4 (ECFP_4).
27 with 30 compounds, Figure 8) and the other most frequent scaffolds are more evenly distributed. Among the most 6235
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Table 6. Statistical Distribution of the Pairwise Similarity Data Calculated with Tanimoto Coefficient and Extended Connectivity Fingerprints, Diameter 4 (ECFP_4) data set
min
1st Qu
median
mean
3rd Qu
max
std. dev
freshwater marine
0.027 0.011
0.110 0.091
0.155 0.115
0.196 0.138
0.231 0.151
1.000 1.000
0.136 0.093
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structures and scaffolds of cyanobacterial metabolites suggests that marine compounds tend to be more structurally diverse than freshwater molecules, yet differences in diversity are not statistically significant. Regardless of their source, secondary metabolites obtained from cyanobacteria can be a rich source of structurally unique and bioactive molecules.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected],
[email protected]. ORCID
Mariana González-Medina: 0000-0001-7365-939X José L. Medina-Franco: 0000-0003-4940-1107 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank Camila M. Crnkovic for providing the list of chemical structures analyzed in this work and her insights in this manuscript. J.L.M.-F. acknowledges the funding from Consejo Nacional de Ciencia y Tecnologiá (CONACyT, Mexico) grant number 282785 to support the computational analysis.
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