Assignment of EC Numbers to Enzymatic Reactions with Reaction Difference Fingerprints

Hu, Qian; Zhu, Hui; Li, Xiaobing; Zhang, Manman; Deng, Zhe; Yang, Xiaoyan; Deng, Zixin

doi:10.1371/journal.pone.0052901

Cited by 26 publications

(28 citation statements)

References 17 publications

(32 reference statements)

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“…This model was developed using a 3-layer predictor using the ENZYME [23] dataset (approximately 9800 enzymes when the model was developed), which was able to achieve an overall accuracy above 86%. Other relevant methods with similar classification scores have also been reported [10,15,20,24,25]. All these methods have proved to be robust; however, they are all outdated since they cannot predict the EC 7 classification, and should therefore be updated in accordance with the new EC class.…”

Section: Introductionmentioning

confidence: 94%

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Alignment-Free Method to Predict Enzyme Classes and Subclasses

Concu

Cordeiro

2019

IJMS

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The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure–activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.

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Section: Introductionmentioning

confidence: 94%

“…Several methodological strategies and tools have been proposed to classify enzymes based on different approaches [3,4,5,6,7,8,9,10]. The Basic Local Alignment Search Tool (BLAST) [11] is likely to be one of the most powerful and used tools which finds regions of similarity between biological sequences.…”

Section: Introductionmentioning

confidence: 99%

Alignment-Free Method to Predict Enzyme Classes and Subclasses

Concu

Cordeiro

2019

IJMS

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“…Functional annotation of those enzymes has been followed by direct or indirect approaches, such as computational sequence/structure analysis and comparison with characterized enzymes [217]. 3D-Fun [218], MOLMAP descriptors [219], ECAssigner [220], EC-blast [221] and the recently released DeepEC [222] have been developed to find links between the EC number and enzyme structures.…”

Section: Substrate-structure Connection Of Cehsmentioning

confidence: 99%

Carboxylic Ester Hydrolases in Bacteria: Active Site, Structure, Function and Application

Kim

2019

Crystals

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Carboxylic ester hydrolases (CEHs), which catalyze the hydrolysis of carboxylic esters to produce alcohol and acid, are identified in three domains of life. In the Protein Data Bank (PDB), 136 crystal structures of bacterial CEHs (424 PDB codes) from 52 genera and metagenome have been reported. In this review, we categorize these structures based on catalytic machinery, structure and substrate specificity to provide a comprehensive understanding of the bacterial CEHs. CEHs use Ser, Asp or water as a nucleophile to drive diverse catalytic machinery. The α/β/α sandwich architecture is most frequently found in CEHs, but 3-solenoid, β-barrel, up-down bundle, α/β/β/α 4-layer sandwich, 6 or 7 propeller and α/β barrel architectures are also found in these CEHs. Most are substrate-specific to various esters with types of head group and lengths of the acyl chain, but some CEHs exhibit peptidase or lactamase activities. CEHs are widely used in industrial applications, and are the objects of research in structure- or mutation-based protein engineering. Structural studies of CEHs are still necessary for understanding their biological roles, identifying their structure-based functions and structure-based engineering and their potential industrial applications.

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“…In addition, these methods are not suitable for the annotation of de novo reactions since current pathway prediction tools only provide information about enzyme catalytic biotransformations and not about their sequences. These shortcomings motivated the development of alternative computational methods based on the structural similarity of reactants and products for identifying candidate protein sequences for orphan enzymatic reactions (30,33,(36)(37)(38)(39)(40). The idea behind these approaches was to assess the similarity of two enzymatic reactions via the similarity of their reaction fingerprints, i.e., the mathematical descriptors of the structural and topological properties of the participating metabolites (41), which could eliminate the problems associated with non-matching or unassigned protein sequences.…”

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confidence: 99%

“…One class of reaction-fingerprint computational methods compares all of the compounds participating in reactions (40), which includes both reactants and cofactors. The application of this group of methods is restricted to specific enzymatic reactions that do not involve large cofactors (30,33,(36)(37)(38)(39)(40). This is because the structural information of the large cofactors overwhelmingly contributes to the corresponding reconstructed reactionfingerprint, and consequently, reactions with similar cofactors will inaccurately be classified as similar (35)(36)(37)(38).…”

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confidence: 99%

Assigning enzyme sequences to orphan and novel reactions using knowledge of substrate reactive sites

Hadadi

MohamadiPeyhani

Mišković

et al. 2017

Preprint

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All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/210039 doi: bioRxiv preprint first posted online Oct. 27, 2017; 2 AUTHORS SUMMARYThe recent advances in pathway generation tools have resulted in a wealth of de novo hypothetical enzymatic reactions, which lack knowledge of the protein-encoding genes associated with their functionality. Moreover, nearly half of known metabolic enzymes are orphan, i.e., they also lack an associated gene or protein sequence. Proposing genes for catalytic functions of de novo and orphan reactions is critical for their utility in various applications ranging from biotechnology to medicine. In this work, we propose a novel computational method that will bridge the knowledge gap and provide candidate genes for both de novo and orphan reactions. We demonstrate that information about a small chemical structure around the reactive sites of substrates is sufficient to correctly assign genes to the functionality of enzymatic reactions. ABSTRACTThousands of biochemical reactions with characterized biochemical activities are still orphan. Novel reactions predicted by pathway generation tools also lack associated protein sequences and genes.Mapping orphan and novel reactions back to the known biochemistry and proposing genes for their catalytic functions is a daunting problem. We propose a new method, BridgIT, to identify candidate genes and protein sequences for orphan and novel enzymatic reactions. BridgIT introduces, for the first time, the information of the enzyme binding pocket into reaction similarity comparisons. It ascertains the similarity of two reactions by comparing the reactive sites of their substrates and their surrounding structures, along with the structures of the generated products. BridgIT compares orphan and novel reactions to enzymatic reactions with known protein sequences, and then, it proposes protein sequences and genes of the most similar non-orphan reactions as candidates for catalyzing the novel or orphan reactions. We performed BridgIT analysis of orphan reactions from KEGG 2011 (Kyoto Encyclopedia of Genes and Genomes, published in 2011) that became non-orphan in KEGG 2016, and BridgIT correctly predicted enzymes with identical third-and fourth-level EC numbers for 91% and 56% of these reactions, respectively. BridgIT results revealed that it is sufficient to know information about six atoms together with their connecting bonds around the reactive sites of the substrates to match a protein sequence to the catalytic activity of enzymatic reactions with maximal accuracy. Moreover, the same information about only three atoms around the reactive site allowed us to correctly match 87% of the analyzed enzymatic reactions. Finally, we used BridgIT to provide candidate protein sequences for 137000 novel enzymatic reactions from the recently introduced ATLAS of Biochemistry...

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Assignment of EC Numbers to Enzymatic Reactions with Reaction Difference Fingerprints

Cited by 26 publications

References 17 publications

Alignment-Free Method to Predict Enzyme Classes and Subclasses

Alignment-Free Method to Predict Enzyme Classes and Subclasses

Carboxylic Ester Hydrolases in Bacteria: Active Site, Structure, Function and Application

Assigning enzyme sequences to orphan and novel reactions using knowledge of substrate reactive sites

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