Figures
Abstract
The EC numbers represent enzymes and enzyme genes (genomic information), but they are also utilized as identifiers of enzymatic reactions (chemical information). In the present work (ECAssigner), our newly proposed reaction difference fingerprints (RDF) are applied to assign EC numbers to enzymatic reactions. The fingerprints of reactant molecules minus the fingerprints of product molecules will generate reaction difference fingerprints, which are then used to calculate reaction Euclidean distance, a reaction similarity measurement, of two reactions. The EC number of the most similar training reaction will be assigned to an input reaction. For 5120 balanced enzymatic reactions, the RDF with a fingerprint length at 3 obtained at the sub-subclass, subclass, and main class level with cross-validation accuracies of 83.1%, 86.7%, and 92.6% respectively. Compared with three published methods, ECAssigner is the first fully automatic server for EC number assignment. The EC assignment system (ECAssigner) is freely available via: http://cadd.whu.edu.cn/ecassigner/.
Citation: Hu Q-N, Zhu H, Li X, Zhang M, Deng Z, Yang X, et al. (2012) Assignment of EC Numbers to Enzymatic Reactions with Reaction Difference Fingerprints. PLoS ONE 7(12): e52901. https://doi.org/10.1371/journal.pone.0052901
Editor: Andrew R. Dalby, University of Westminster, United Kingdom
Received: September 21, 2012; Accepted: November 23, 2012; Published: December 28, 2012
Copyright: © 2012 Hu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by grants from the National Natural Science Foundation of China, the Ministry of Science and Technology of China (973 and 863 Programs) and the National Mega Project on Major Drug Development. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The Enzyme Commission number (EC number) is a numerical classification scheme for enzymes, based on the chemical reactions they catalyze [1]. Strictly speaking, EC numbers do not specify enzymes, but enzyme-catalyzed reactions. There are 6 main EC levels in the EC system: (1) EC 1 for Oxidoreductase reactions, (2) EC 2 for Transferase reactions, (3) EC 3 for Hydrolase reactions, (4) EC 4 for Lyase reactions, (5) EC 5 for Isomerase reactions, and (6) EC 6 for Ligase reactions. The basis of linking genomics and chemistry is the EC numbers (1).
A wide range of research areas in molecular biology and medical biochemistry require a reliable enzyme classification system, e.g., metabolic network reconstruction and systems biology. When research scientists in the above mentioned areas wish to unambiguously refer to an enzyme and its function, the EC number introduced by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (IUBMB) is used [2].
The EC numbers represent enzymatic reactions (chemical information), but they are also utilized as identifiers of enzymes and enzyme genes (genomic information) [3]. The EC number plays a key role in classifying enzymatic reactions and in linking the enzyme genes or proteins to reactions in metabolic pathways [4]. This duality of the EC numbers makes it possible to link the genomic repertoires of enzyme genes to the chemical repertoire of metabolic pathways [3].
The assignment of the EC numbers is performed manually, based on published experimental data on individual enzymes, by the Joint Commission on Biochemical Nomenclature of the International Union of Biochemistry and Molecular Biology and the International Union of Pure and Applied Chemistry. Unfortunately, there are numerous reactions known to be present in various pathways but without any official EC numbers, because the EC number assignment requires published articles on full characterization of enzymes [3], [4].
There are some computational EC assignment systems, which are purely based on chemical knowledge, without any use of protein sequence or other information on enzymes [3]–[6].
In E-zyme [4], the authors proposed a ‘RDM (reaction center (R), the difference region (D), and the matched region (M)) patterns’ method to predict the potential EC numbers to given reactant pairs (substrates and products) or uncharacterized reactions. The method consists of three steps: (i) graph alignment of a query reactant pair (substrates and products) for computing the query RDM pattern, (ii) multi-layered partial template matching by comparing the query RDM pattern with template patterns related with known EC numbers and (iii) weighted major voting scheme for selecting appropriate EC numbers. What should be noted here is that the input of E-zyme is a query reactant pair (substrates and products). For whole reactions, E-zyme is based on a manual RPAIR (reactant pairs) database [7], [8]. Therefore, in the E-zyme server, the method is currently only applicable to two molecules by using a graph comparison method [9].
Latino et al. [6] developed a method using MOLMAP (molecular mapping of atom-level properties) descriptors, which relies on a Kohonen SOM (self-organizing map) that defines types of covalent bonds on the basis of their physicochemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants and numerically encodes the pattern of changes in bonds during a chemical reaction. A genome-scale data set of enzymatic reactions available in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database was encoded by the MOLMAP descriptors and was explored for the assignment of the official EC number from the reaction equation with Random Forests as the machine learning algorithm. In the method (using MOLMAP), physicochemical descriptors [10] are used to describe reactions. The descriptors are very helpful to understand reaction mechanisms [11], [12], however limited by the commercial availability.
Egelhofer et al. [2] have developed tools for the validation of the EC number classification scheme, in which there are two major steps: (1) the chemical similarity calculation using atom and bond types, and (2) the characterization of the individual reaction by considering reaction pairs. The authors defined at least one and if necessary more than one unique difference keys for each sub-subclass of the EC number classification system. In the method, the authors only considered atom or bond types.
Similarity concept is widely used in biological and chemical studies. Automatic perception of similarities between metabolic reactions, i.e. their classification, is a Chemoinformatics issue with an impact in bioinformatics, biotechnology, or medicinal chemistry [6], [12]. There are some researchers developing different methods to measure reaction similarity between individual steps of enzymatic reaction mechanisms and to quantitatively measure the similarity of enzymatic reactions based upon their explicit mechanisms [13], [14]. Ridder and Wagener [15] clustered a data set of metabolic reactions using a difference fingerprint defined by the differences in occurrences of each SYBYL atom type in the reactant and product fingerprints. Faulon et al [16] employed molecular signatures of topological atom neighborhoods to derive reaction signatures and used such descriptors with SVM (support vector machine) to classify metabolic reactions in terms of EC numbers. In RxnFinder [17], our group proposed reaction difference fingerprints and reaction similarity to search biochemical reactions. In this work, reaction difference fingerprints, which not only consider atom and bond types, but also bond connections (variable path lengths), are applied to assign EC numbers to enzymatic reactions.
Methods
Reaction Selection
The 5120 training reactions used in ECAssigner are selected from 8458 KEGG [18] reactions after excluding the following six cases (some cases are overlapped):
- 965 reactions with missing molecules (unbalanced reactions) (for example, NH3+ CO2< = > Cyanide (R00152)),
- 395 reactions with Glycans (such as Glycan+H2O< = >D-Mannose+Glycan (R01331)),
- 1280 reactions without EC assignments, for instance,
- (3a) a non-enzymatic reaction, 2 D-Urobilinogen+Oxygen < = >2 D-Urobilin +2 H2O (R00070);
- (3b) an incomplete reaction, Protein< = >L-Alanyl-tRNA (R00165);
- 932 reactions with molecules having not structure information, for example, 16 ATP +16 H2O +8 Reduced ferredoxin < = >8 e- +16 Orthophosphate+16 ADP +8 Oxidized ferredoxin (R00002),
- 1174 reactions with R-atom types or polymer-like molecules,
- (5a) a reaction example for R-atoms, 2 Long-chain alcohol+Oxygen < = >2 Long-chain aldehyde +2 H2O (R00041);
- (5b) a reaction example for polymer-like molecules, Polyphosphate+n H2O< = >(n+1) Oligophosphate (R00001),
- 99 reactions (61 reactions are racemase or epimerase reactions and 38 reactions having zero fingerprints when fingerprint length is selected at 3) with zero reaction difference fingerprints, for instance, L-Glutamate< = >D-Glutamate (R00260) glutamate racemase.
Molecular Fingerprints
The molecular fingerprint (MFP) of a molecule is defined as MFP = (Fi), in which Fi refers to a molecular fragment with real occurrences of a molecule. Fi is obtained by molecular fragmentation method. Here, we use the linear fragments up to 7 atoms (6 bonds) that are computed by OpenBabel package [19].
Reaction Difference Fingerprints
The fingerprints of reactant molecules minus the fingerprints of product molecules will generate reaction difference fingerprints (RFP) defined as: RFP = MFP reactants- MFP products = (RFi), where RFi refers to a difference fragment with difference occurrences of reactants and products. For KEGG reaction Urea-1-carboxylate+H2O < = >2 CO2+2 NH3 (R00005: C01010+ C00001< = >2 C00011+2 C00014), the reaction difference fingerprints are calculated using formula RFP R00005 = MFP C01010+C00001− MFP 2C00011+2C00014.
Euclidean Distance
After calculating reaction difference fingerprints of two reactions, the similarity of two reactions can be computed using a Euclidean distance measurement as defined as Di,j = ED(RFP i, RFP j). The significance of the distance values will be discussed in the RESULTS AND DISCUSSIONS section.
Assignment Strategy
The smaller the distance between two reactions, the more similar they are. The EC number of the closest (the most similar), with minimum distance, training reaction will be assigned to an input reaction. If there are several reactions having the same minimum distance, the EC number occurring most often will be selected.
Results and Discussion
Length Selection of Reaction Difference Fingerprints
In the work, the first step is to select the fingerprint length. In the model development, different fragment lengths are used to get leave-one-out cross-validation (as applied by Yamanisi et al. [4]) accuracies as shown in Table 1, which are plotted in Figure 1. From the path length-accuracy plot, the model using fragments with path length equal to 3 is the best. In ECAssigner, path length is selected as 3.
Performance of the Selected Reaction Difference Fingerprints
The validation accuracy is 0.831; 0.867, 0.926 for sub-subclass, subclass, main class respectively. Then, the validation accuracy is investigated on the individual EC levels as shown in Table 2, from which the rank order of accuracy is: EC2>EC3>EC1>EC6>EC4>EC5. More than 82% (2112+1398+735)/5120 reactions are with EC 1, 2, or 3. The results on main classes EC1, 2, and 3 are better than those on EC4, 5, and 6.
Results Over Reaction Distances
EC number is assigned according to the Euclidean distance of the input reaction to the training reactions. The validated results are counted with the distance to the closest training reaction as shown in Figure 2. From Figure 2, 85% reactions are predicted with distance smaller than 20, in which more than 90% predictions are correct. From the cross-validation results, the accuracy will become worse with the increase of Euclidean distance. The predictions made with distances smaller than 50 will lead to higher accuracy.
Incorrect Assignments
Two main reasons for the incorrect assignments are analyzed: (1) the same chemical transformation pattern with different participating molecules; (2) the same chemical transformation pattern with intra-molecular and inter-molecular difference.
For the first case, a reaction Diphosphate+H2O < = > 2 Orthophosphate (R00004) by another reaction Diphosphate+D-Fructose 6-phosphate < = > Orthophosphate+D-Fructose 1,6-bisphosphate (R00764) (as shown in Fig. 3), R00004 is a diphosphate phosphohydrolase or pyrophosphate phosphohydrolase reaction. The EC number of R00004 is 3.6.1. The closest reaction to R00004 is R00764, which is a phosphotransferase reaction. The EC number of R00764 is 2.7.1. Both reactions cleave an O-P bond and add an oxygen atom to a Phosphate atom. The difference is that R00004 will use water (3.6.1) and R00764 will use a molecule other than water (2.7.1).
For the second reason, a reaction D-glucose-1-phosphate 6-phosphotransferase (R00016) by another reaction D-Ribose 1,5-phosphomutase (R01057) (as shown in Fig. 4), R00016 belongs to 2.7.1 and R01057 is classified as 5.4.2. Both reactions transfer phosphate group however in different ways. R00016 is an inter-molecular transformation; however R01057 is an intra-molecular reaction.
EC Number Assignments of those Reactions without Experimental Evidence
An important application is to assign EC numbers for those reactions without enzyme validations. We applied our method to the first reaction example commented as ‘enzyme not yet characterized’, that is, S-Adenosyl-L-methionine+L-Tryptophan < = > S-Adenosyl-L-homocysteine+Abrine (R00683) (as shown in Fig. 5). There are three steps in the EC number assignments:
- calculate the RDF of the input reaction, for instance, RFP R00683 for R00683,
- compute distances to the training reactions and obtain the minimum distance. For R00683, the minimum is 2, which relates with S-Adenosyl-L-methionine+Serotonin< = >S-Adenosyl-L-homocysteine+N-Methylserotonin (R02910) (as shown in Fig. 5),
- assign the EC number of the training reaction corresponding with minimum distance to the input reaction. The EC number of R02910 is 2.1.1. The EC number of R00683 is thus assigned as 2.1.1, which is commented as ‘Transferases. Transferring one-carbon groups. Methyltransferases’.
From the comparisons of R00683 and R02910, the EC number assignment is reasonable, which indicates that ECAssigner could be potentially applied to predict a reference EC number for a query reaction.
Comparisons with Other Methods
Comparisons with EC number assignment methods are listed in Table 3. E-zyme [4] applied an error-prone manual method (2) to assign EC numbers for whole reactions. Based on manually curated RPAIR, E-zyme is not an automatic server for a whole reaction. However, E-zyme applied automatic method for two molecules (one reactant and one product). The method explored by Latino et al. [6], used physicochemical properties calculated by commercial software. Egelhofer et al. [2] computes difference keys (atom or bond types) in their strategy, however they only considered the reaction difference of atom and bond types. Both Latino et al. [6] and Egelhofer et al. [2] didn’t provide web server for their methods. From the comparisons, ECAssigner is the first automatic server for EC number assignment.
Conclusions
The results demonstrate that the combination of reaction difference fingerprints (RDF) with Euclidean distance obtained satisfactory models to assign EC numbers for enzymatic reactions. The RDF descriptor of a reaction is defined as the difference between the molecular fingerprints of the products and the reactants and numerically encodes the transformation pattern of changes during a chemical reaction. A fingerprint length at 3 is validated. From the comparisons, ECAssigner calculates reaction difference fingerprints with variable lengths directly from chemical structures of a reaction, which does not need manual steps or commercial packages.
Author Contributions
Conceived and designed the experiments: Zixin Deng. Performed the experiments: HZ XBL MMZ QNH. Analyzed the data: HZ XBL MMZ Zhe Deng XYY QNH. Wrote the paper: QNH Zixin Deng.
References
- 1.
Barrett AJ (1992) Enzyme Nomenclature. Academic Press, San Diego, California.
- 2. Egelhofer V, Schomburg I, Schomburg D (2010) Automatic Assignment of EC Numbers. PLoS Comput Biol 6(1): e1000661 .
- 3. Kotera M, Okuno Y, Hattori M, Goto S, Kanehisa M (2004) Computational Assignment of the EC Numbers for Genomic-Scale Analysis of Enzymatic Reactions. J Am Chem Soc 126: 16487–16498.
- 4. Yamanishi Y, Hattori M, Kotera M, Goto S, Kanehisa M (2009) E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs. Bioinformatics 25: i179–i186.
- 5. Latino DA, Zhang QY, Aires-de-Sousa J (2008) Genome-scale classification of metabolic reactions and assignment of EC numbers with self-organizing maps. Bioinformatics 24: 2236–44.
- 6. Latino DA, Aires-de-Sousa J (2009) Assignment of EC numbers to enzymatic reactions with MOLMAP reaction descriptors and random forests. J Chem Inf Model 49: 1839–46.
- 7. Oh M, Yamada T, Hattori M, Goto S, Kanehisa M (2007) Systematic analysis of enzyme-catalyzed reaction patterns and prediction of microbial biodegradation pathways. J Chem Inf Model 47: 1702–1712.
- 8. Leber M, Egelhofer V, Schomburg I, Schomburg D (2009) Automatic assignment of reaction operators to enzymatic reactions. Bioinformatics 25: 3135–3142.
- 9. Hattori M, Okuno Y, Goto S, Kanehisa M (2003) Development of a Chemical Structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J Am Chem Soc 125: 11853–11865.
- 10. Zhang QY, Aires-de-Sousa J (2005) Structure-based classification of chemical reactions without assignment of reaction centers. J Chem Inf Model 45: 1775–1783.
- 11. Zitha-Bovens E, Maas P, Wife D, Tijhuis J, Hu QN, et al. (2009) COMDECOM: Predicting the Lifetime of Screening Compounds in DMSO Solution. Journal of Biomolecular Screening 14: 557–565.
- 12. Sacher O, Reitz M, Gasteiger J (2009) Investigations of enzyme-catalyzed reactions based on physicochemical descriptors applied to hydrolases. J Chem Inf Model 49: 1525–34.
- 13. O’Boyle NM, Holliday GL, Almonacid DE, Mitchell JB (2007) Using reaction mechanism to measure enzyme similarity. J Mol Biol 368: 1484–1499.
- 14. Holliday GL, Almonacid DE, Bartlett GJ, O’Boyle NM, Torrance JW, et al. (2007) MACiE (Mechanism, Annotation and Classification in Enzymes): novel tools for searching catalytic mechanisms. Nucleic Acids Res 35: D515–D520.
- 15. Ridder L, Wagener M (2008) SyGMa: Combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 3: 821–832.
- 16. Faulon JL, Misra M, Martin S, Sale K, Sapra R (2008) Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptors. Bioinformatics 24: 2335–2349.
- 17. Hu QN, Deng Z, Hu H, Cao DS, Liang YZ (2011) RxnFinder: Biochemical Reaction Search Engines Using Molecular Structures, Molecular Fragments, and Reaction Similarity. Bioinformatics 27: 2465–2467.
- 18. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, et al. (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36: D480–D484.
- 19. O’Boyle NM, Morley C, Hutchison GR (2007) Using reaction mechanism to measure enzyme similarity. J Mol Biol 368: 1484–1499.