Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm
© The Author(s) 2017
Received: 21 March 2017
Accepted: 8 August 2017
Published: 16 August 2017
Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications at multiple stages of the drug discovery pipeline. Quantifying the similarity of natural products is a particularly important problem, as the biological activities of these molecules have been extensively optimized by natural selection. The large and structurally complex scaffolds of natural products distinguish their physical and chemical properties from those of synthetic compounds. However, no analysis of the performance of existing methods for molecular similarity calculation specific to natural products has been reported to date. Here, we present LEMONS, an algorithm for the enumeration of hypothetical modular natural product structures. We leverage this algorithm to conduct a comparative analysis of molecular similarity methods within the unique chemical space occupied by modular natural products using controlled synthetic data, and comprehensively investigate the impact of diverse biosynthetic parameters on similarity search. We additionally investigate a recently described algorithm for natural product retrobiosynthesis and alignment, and find that when rule-based retrobiosynthesis can be applied, this approach outperforms conventional two-dimensional fingerprints, suggesting it may represent a valuable approach for the targeted exploration of natural product chemical space and microbial genome mining. Our open-source algorithm is an extensible method of enumerating hypothetical natural product structures with diverse potential applications in bioinformatics.
KeywordsChemical similarity Natural products Chemical fingerprints Chemical structure enumeration
Quantifying the molecular similarity of chemical structures is a central task in cheminformatics [1, 2]. The assumption that similar molecules are more likely to have similar biological or physicochemical properties than dissimilar ones  underlies the diverse applications of molecular similarity calculations in drug discovery, particularly in ligand-based virtual screening and medicinal chemistry, but also in toxicology, chemogenomics, and pharmacology. Consequently, a large and diverse set of methods for the efficient abstract representation of chemical information is available. Although both one-dimensional and three-dimensional descriptors have been developed, two-dimensional molecular fingerprint algorithms, which decompose a chemical graph into a sequence of bits, remain the most common method for representing structural information in the context of assessing molecular similarity . Once generated, chemical fingerprints can be rapidly compared to one another with the widely used Tanimoto coefficient, or one of several other distance metrics , in order to quantify the similarity of any two chemical structures.
A number of publicly available datasets [6–8] have been developed for use in benchmarking studies, and several groups have compared the performance of existing two-dimensional chemical fingerprint algorithms on these benchmark datasets [9–12]. A recent review concluded that, while commonly used fingerprinting algorithms have similar performances, circular fingerprints generally perform best . Several studies have also concluded that two-dimensional similarity search methods outperform three-dimensional methods . A number of groups have additionally investigated the performance of several distance (or similarity) metrics [5, 13–15], or combinations thereof [16–18], used to compare chemical fingerprints. These results generally validate the popularity of the Tanimoto coefficient.
Despite these extensive analyses of the performance of molecular descriptors and distance metrics, no comparative analysis of the performance of chemical fingerprinting algorithms on the unique and diverse scaffolds of natural products has to date been reported. Natural products and their derivatives represent a historically invaluable source of industrial and pharmaceutical agents, and the basis for the majority of approved small molecule clinical drugs . These complex small molecules are biosynthesized from simple metabolic building blocks by large, multi-domain enzymes or enzyme complexes in combinatorial strategies . Quantifying chemical similarity is therefore a particularly important task for natural products due to their potent biological activities. In particular, the ability to cheminformatically determine whether a molecule is a member of a known class of bioactive natural products—for instance, the glycopeptides or β-lactams—may facilitate the targeted exploration of chemical space. Moreover, the ability to reliably associate putative natural product structures generated by genomic structure prediction with known natural product classes could facilitate the targeted mining of microbial genomes.
The large and structurally complex scaffolds of natural products distinguish them from synthetic agents. In particular, cheminformatic studies have reported that natural products have greater chemical diversity, greater molecular weight, greater three-dimensional complexity (including more rotatable bonds, more stereocenters, and a higher fraction of sp3 carbons), lower hydrophobicity and greater polarity, fewer aromatic rings, more heteroatoms, and more hydrogen bond donors and acceptors relative to synthetic agents [21, 22], and contain unique pharmacophores and ring systems [23, 24]. However, existing collections are biased towards synthetically tractable scaffolds  and by Lipinski’s ‘rule of 5’ physicochemical parameters for orally bioavailable drugs , despite the fact that a significant proportion of approved natural product drugs violate these rules . A recent analysis reported that only 17% of natural product ring scaffolds are present in commercially available screening collections . Because of these disparities between naturally occurring and commercially available chemistries, several new methods to chart natural product chemical space have been described [28–32]. However, in the context of new algorithm development to explore natural product chemical space, a rigorous assessment of existing methods for quantifying the molecular similarity of natural products is essential.
Modular natural products can be characterized by a number of structural or biosynthetic features, including the nature of the enzymatic assembly line responsible for their biosynthesis (nonribosomal peptide, polyketide, or hybrid), their size, the presence or absence of starter units, their pattern of macrocyclization, and the action of tailoring reactions such as glycosylation, thiazole/oxazole formation, chlorination, or N-methylation. In order to evaluate the impact of each of these features in turn on chemical similarity search, we developed LEMONS (Library for the Enumeration of MOdular Natural Structures). LEMONS is a Java software package designed to enumerate hypothetical natural product structures given a user-determined set of biosynthetic parameters. Each hypothetical structure is subsequently modified by substituting one or more monomers, or by adding, removing, or changing the site of one or more tailoring reactions. The modified structure can then be compared to the entire library of original structures, using a two-dimensional fingerprint or another chemical similarity method. A correct match is scored if the modified structure displays greater chemical similarity to the original structure than to any of the other structures within the library. This process is repeated for each modified structure in turn, and the proportion of correct matches is determined for each chemical similarity method. LEMONS can thus be used to calculate the percentage of correct matches between original and modified structures for any chemical similarity method, using a user-input list of possible monomers and tailoring reactions.
Chemical similarity methods evaluated in this study
Substructure keys-based fingerprint
Substructure keys-based fingerprint
Substructure keys-based fingerprint
Substructure keys-based fingerprint
Retrobiosynthesis and alignment
We repeated this proof-of-concept experiment four times, substituting two, three, four, and five proteinogenic amino acids in turn (Fig. 3b). Plotting the trend of correct matches against number of substituted monomers revealed that the performance of all methods decreased when a greater number of monomers were substituted. However, with a greater number of monomer substitutions, a more clear separation in accuracy between methods was observed. No change in the ranking of chemical similarity methods was observed as the number of monomer substitutions was increased.
In addition to nonproteinogenic amino acids and polyketide monomers, modular natural products commonly contain starter units that mediate biological activity, including short- and long-chain straight and branched fatty acids, aromatic and alicyclic acids, and amino acid derivatives . These starter units might be expected to make a significant contribution to chemical similarity search due to their unique substructures or topologies. We investigated the impact of common starter units on similarity search in natural product-like chemical space by diversifying hypothetical hybrid natural products with 23 common starter units, including 4 fatty acids, 13 aromatic acids, 3 alicyclic acids, and 3 small starter units (Additional file 1: Table S1). In general, the performance of each similarity search method was similar for linear hybrid natural products with and without starter units (Additional file 3: Fig. S2). However, significant differences were observed in performance when comparing starter unit and non-starter unit substitution: for instance, the percentage of natural products correctly matched by the graph-only CDK fingerprint was 16.22% higher for starter unit substitutions, but 30.47% lower for the Pubchem fingerprint. Averaged across all fingerprints, a starter unit substitution was equivalent to 1.70 non-starter-unit substitutions (Additional file 3: Fig. S2). These results suggest that, in general, similarity search is strongly influenced by the unique structural properties of modular natural product starter units. While this phenomenon may facilitate the clustering of natural products of the same family that share a common starter unit, it may represent an obstacle to the cheminformatic identification of natural product families with variable starter units.
Modular natural products are attractive leads in drug discovery due to their evolved biological activities, but it is unclear which chemical similarity methods perform best in the chemical space occupied by their unique and complex scaffolds. Here, we present LEMONS, an algorithm for the enumeration of hypothetical natural product structures, and use it both to conduct a systematic comparison of chemical similarity methods for natural products, and to quantify of the impact of various structural parameters on similarity search. We find that, in general, size and macrocyclization have relatively little impact on similarity search, whereas the inclusion of nonproteinogenic amino acids and starter units more strongly affect similarity search. Circular fingerprints and a recently described retrobiosynthesis and alignment method consistently performed best across experimental conditions. There was a strong correlation between circular fingerprint radius and accuracy. Hashed topological fingerprints, which were outperformed by at least one fingerprint of every other type, appear to be an inappropriate strategy for natural product similarity search. Among substructure keys-based fingerprints, the Klekota–Roth fingerprint was most accurate. This fingerprint considers substructures which were determined to be privileged with respect to biological activity; it is conceivable that these substructures are overrepresented among natural products due to their evolved bioactivities. In general, our results support the use of circular fingerprints and retrobiosynthetic approaches for chemical similarity search in modular natural product chemical space.
As an extensible, open-source method to enumerate hypothetical natural product structures, LEMONS has diverse potential applications in bioinformatics beyond analysis of molecular similarity algorithms. Several reports, for instance, have highlighted the applicability of targeted hypothetical structure enumeration to the discovery of natural products from tandem mass spectrometry data of complex bacterial extracts [43, 44]. However, these methods require varying degrees of manual intervention to produce libraries of hypothetical structures. In contrast, LEMONS can be leveraged as a platform for rapid, untargeted exploration of desired regions of chemical space or natural product families. For instance, ‘macrolide-like’ chemical space could be explored by generating cyclic polyketides containing seven or eight monomers, with zero, one, or two glycosylations. The integration of untargeted hypothetical structure enumeration via LEMONS with existing methods for locating hypothetical chemical structures within mass spectral data may facilitate genome mining for desired secondary metabolites. Alternatively, the algorithm can be expanded to profile chemical space more broadly, particularly as its extensibility facilitates the addition of any number of new monomers or tailoring reactions. The ability to randomly profile a particular region of natural product chemical space may provide insights into the chemical evolution of natural product families.
A limitation of LEMONS with respect to the broader applications of hypothetical natural product structure enumeration is that its design considers only linear or cyclic permutations of monomers. Consequently, in its present implementation, the algorithm is only extensible to biosynthetic classes that can be modelled as post-translationally modified linear or cyclic polymers. Thus, while LEMONS could be extended to profile natural product classes such as ribosomally synthesized and posttranslationally modified peptides (RiPPs), a graph-based method of structure enumeration would be required to model classes such as terpenes or aminoglycosides, in which monomers (isoprene units or sugars, respectively) may more appropriately be considered subgraphs which can potentially connect to other subgraphs at multiple sites.
We describe LEMONS, an open-source and easily extensible algorithm for untargeted enumeration of modular natural product chemical structures. We use this algorithm to benchmark chemical similarity methods for modular natural products, finding that circular fingerprints and a newly described retrobiosynthetic approach (GRAPE/GARLIC) perform best, whereas topological fingerprints and most substructure-based fingerprints perform less well (with the notable exception of the Klekota–Roth fingerprint). Additionally, we investigate the impact of biosynthetic parameters on similarity search, finding that size and macrocyclization have relatively little impact on similarity search, whereas inclusion of nonproteinogenic amino acids and starter units have a stronger effect. Our results lead us to recommend the use of circular fingerprints and retrobiosynthetic approaches for modular natural product similarity search, and our method has diverse potential applications in chemical space exploration and microbial genome mining.
Development of an algorithm to enumerate hypothetical natural product structures
We developed LEMONS (Library for the Enumeration of MOdular Natural Structures), a Java software package designed to investigate the performance of chemical similarity metrics by enumerating hypothetical natural product structures given a user-determined set of biosynthetic parameters. LEMONS first enumerates a library of hypothetical natural product structures given a list of possible monomers and tailoring reactions. Each hypothetical natural product structure may subsequently be modified by substituting one or more monomers, or by adding, removing, or changing the site of one or more tailoring reactions. The modified structure is compared to the entire library of original structures using a two-dimensional molecular fingerprint, and the rank of the correct match (i.e., of the modified structure to the original structure from which it was derived) is determined. This process is repeated for each hypothetical structure within the library, and the rank distribution of each molecular fingerprint is written to a file. LEMONS uses the Chemistry Development Kit (version 1.5.9) for chemical structure generation and molecular fingerprint calculation . LEMONS source code is available at http://github.com/magarveylab/lemons.
Linear structure generation
The first step in the LEMONS algorithm is the enumeration of a library of linear hypothetical natural product structures. A permutation of monomers is selected at random from one or more monomer sets in order to generate a polymer of a given size range. By default, four possible sets of monomers are included within LEMONS, including proteinogenic and nonproteinogenic amino acids, polyketide monomers, and starter units. LEMONS includes 45 nonproteinogenic amino acids derived from fungal and bacterial nonribosomal peptides, including β-hydroxylated and β-methylated amino acids, α-keto acids, β-amino acids, and other commonly occurring modified amino acids (Additional file 1: Table S1). LEMONS also includes 26 polyketide monomers, corresponding to 7 common polyketide extender units (malonate, methylmalonate, ethylmalonate, methoxymalonate, propionate, isobutyrate, and 2-methylbutyrate) at all possible oxidation states (Additional file 1: Table S1). Finally, LEMONS includes 23 common starter units, including four fatty acids, thirteen aromatic starter units, three alicyclic starter units, and three small starter units (Additional file 1: Table S1). A template is provided within the LEMONS source code to facilitate the addition of new monomer sets to the software package.
Tailoring reaction detection and execution
The second step in the LEMONS algorithm is the detection of all possible tailoring reactions. Polymers are converted to chemical structures, and potential sites of each reaction on the resulting chemical structure are identified. Tailoring reactions to be executed on the original library of hypothetical natural product structures are set via the ‘–initial_reactions’ option of the command line interface in a probabilistic manner, such that the argument ‘halogenation 0.5’ will cause LEMONS to attempt to execute a halogenation reaction on 50% of the scaffolds, while the arguments ‘halogenation 1’ and ‘halogenation 2’ will cause LEMONS to attempt to execute one and two halogenation reactions, respectively, on each scaffold. Five possible tailoring reactions are included in LEMONS by default (cyclization, halogenation, glycosylation, thiazole/oxazole formation, and amino acid N-methylation). A template is provided within the LEMONS source code to facilitate the addition of new reactions to the software package.
The cyclization reaction within LEMONS creates cyclic or branched hypothetical natural product structures by cyclizing either an N-terminal amine or a free hydroxyl at the C-terminal carboxylic acid. Only sp3 carbon-bound hydroxyls are considered potential sites of cyclization, permitting cyclization on serine and threonine residues or β-hydroxylated amino acids. The halogen reaction executes chlorination or bromination with equal probability. In order to capture the diverse patterns of natural product halogenation , any non-backbone carbon is considered a possible site of halogenation. The glycosylation reaction leverages a library of 69 deoxy and hexose sugars  to generate O-glycosylated hypothetical natural product structures (Additional file 4: Table S2). Both sp2 and sp3 carbon-bound hydroxyls are considered potential sites of O-glycosylation with a randomly selected sugar moiety. The thiazole and oxazole-forming reaction considers all cysteine and serine residues, respectively, as potential sites of thiazole or oxazole formation, and allows for bis-thiazole or oxazole formation. The N-methylation reaction considers all backbone amide nitrogens derived from proteinogenic and nonproteinogenic amino acids as potential sites of N-methylation.
Generation of modified structures
Once all potential tailoring reactions have been detected and a subset selected for each structure in the library of hypothetical natural product structures, the original structure is modified by substituting one or more monomers, or by adding, removing, or changing the site of one or more tailoring reactions, in order to produce a derivative of the same in silico assembly line. The set of monomers substituted into the original polymer can be specified independently from the set of monomers used to construct the original polymer. If the substitution set includes starter units, these monomers will only be substituted at the first residue. In order to maximize chemical diversity, each monomer in the original scaffold will be substituted before an individual monomer is swapped twice. Monomers which are the sites of chemical tailoring reactions are not substituted. The command line interface additionally allows the independent specification of new tailoring reactions to add to the original scaffold, tailoring reactions to remove from the original scaffold, and tailoring reactions whose sites may be changed. Thus, for instance, an unmethylated peptide can be methylated, a methylated peptide can have its N-methyl group removed, and the site of N-methylation can be varied. The execution of the modified reaction set allows for the conversion of the resulting modified structure to a chemical scaffold.
Comparison of modified and original structures
The final step in the LEMONS algorithm compares the ability of seventeen molecular fingerprints (Table 1) to correctly match the original and modified hypothetical natural product structures. The Tanimoto coefficient of a modified structure to each structure in the original library is calculated, and the rank of the Tanimoto coefficient to the original structure is calculated. This process is repeated for every structure within the original library, and the rank distribution of the correct match is written to a file.
Generation of chemical fingerprints
Three predominant approaches exist for computing two-dimensional molecular fingerprints . The first, substructure keys-based fingerprints, set the bits within a molecular fingerprint bit sequence based on the presence or absence of predefined structural features. The MACCS (Molecular ACCess System) fingerprint is among the best-known substructure keys-based fingerprints . The PubChem database implements another substructure keys-based fingerprint for similarity search, based on 881 structural keys . Both the 79 electrotopological state (E-state) fragments defined by Hall and Kier  and the 4860 unique chemical substructures which enrich for biological activity defined by Klekota and Roth  have also been used within substructure keys-based fingerprints implemented in the Chemistry Development Kit .
Topological or path-based fingerprints describe fragments of a molecule, typically by enumerating all paths through a molecule up to a certain number of bonds, then hashing each path to create a fingerprint. Topological fingerprints typically encode information about the atom types and number and type of bonds within each path. While individual bits correspond to unique structural features in substructure-based fingerprints, a single bit may correspond to several features in topological fingerprints, a phenomenon known as bit collision. The Daylight fingerprint  is probably the best-known topological fingerprint. Variants of the Daylight fingerprint are implemented in several open-source cheminformatic libraries, including RDKit  and the Chemistry Development Kit (CDK) .
Circular fingerprints are also hashed fingerprints which encode circular atom environments up to a maximum bond radius from the central atom rather than looking for paths through a chemical graph. Extended-connectivity fingerprints (ECFPs)  encode properties including chemical element, number of heavy-atom neighbours, number of hydrogens, and isotope and ring information, whereas functional connectivity fingerprints (FCFPs)  encode pharmacophoric features related to ligand-binding. The maximum bond length radius is appended to the end of the name: thus the ECFP6 fingerprint is an extended-connectivity fingerprint with a diameter of 6 bonds.
Other fingerprints adopt lexicographic approaches to the generation of molecular fingerprints. LINGO , for instance, is a text-based fingerprint calculated based on canonical SMILES. Seventeen molecular fingerprints were evaluated in this study, including ECFP and FCFP circular fingerprints with radii of 0, 2, 4, and 6; four substructure keys-based fingerprints (MACCS, PubChem, E-state, Klekota-Roth); one lexicographic fingerprint (LINGO); and four Daylight-like topological fingerprints (Table 1). All molecular fingerprints were generated with the Chemistry Development Kit (CDK) , version 1.5.9.
For each comparison, we reported the P value for either the Brunner–Munzel paired rank test or the Brunner–Munzel independence test. When utilizing a statistical hypothesis test, it is common practice to compare a P value to a pre-established significance level, e.g., α = 0.05. In some instances, we are performing 17 comparisons, and so would need to adjust the significance level for each individual comparison to account for the multiple comparisons. This could be done, for example, using a Bonferroni correction so that if we desired that the family wise error rate should not exceed 5%, then the significance level for each individual comparison would be 0.05/17 = 0.0029. We mention this only for completeness because, in the multiple testing scenarios we consider herein, the P values for the individual comparisons are so small that adjusting for multiple comparisons will not affect the conclusions presented.
MAS conceived of the project, wrote LEMONS, performed data analysis, and wrote the manuscript. CAD wrote GRAPE/GARLIC and contributed to data analysis. BCF contributed to data analysis. PDM provided advice on the data analysis. NAM supervised the project. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Availability of data and materials
LEMONS source code is available at https://github.com/magarveylab/lemons.
Consent for publication
Ethics approval and consent to participate
This work was supported by the National Science and Engineering Research Council (RGPIN 371576-2009, 101997-2006, and 388681-2011). Support for N.A.M. was obtained through a Canadian Institute for Health Research (CIHR) New Investigator Award, Ontario Early Investigator Award, the Canada Research Chairs Program, and a CIHR Operating Grant.
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