Identification of Multi-Functional Enzyme with Multi-Label Classifier

Che, Yuxin; Ju, Ying; Xuan, Ping; Ren, Lan; Xing, Fei

doi:10.1371/journal.pone.0153503

Cited by 24 publications

(34 citation statements)

References 76 publications

(56 reference statements)

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“…Currently, the models of human metabolic networks are still under construction. According to the advances in computational biology, some algorithms, such as the machine learning methods for identification of multi-functional enzymes [ 49 , 50 ], can be valuable for the construction of metabolic network. The results of our simulations may become more precise to experimental results when the models become more complete.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Decision Making Approach to Identify Optimum Enzyme Targets and Drug Dosage for Remedying Presynaptic Dopamine Deficiency

Hsu

Wang

2016

PLoS ONE

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Model-based optimization approaches are valuable in developing new drugs for human metabolic disorders. The core objective in most optimal drug designs is positive therapeutic effects. In this study, we considered the effects of therapeutic, adverse, and target variation simultaneously. A fuzzy optimization method was applied to formulate a multiobjective drug design problem for detecting enzyme targets in the presynaptic dopamine metabolic network to remedy two types of enzymopathies caused by deficiencies of vesicular monoamine transporter 2 (VMAT2) and tyrosine hydroxylase (TH). The fuzzy membership approach transforms a two-stage drug discovery problem into a unified decision-making problem. We developed a nested hybrid differential evolution algorithm to efficiently identify a set of potential drug targets. Furthermore, we also simulated the effects of current clinical drugs for Parkinson’s disease (PD) in this model and tried to clarify the possible causes of neurotoxic and neuroprotective effects. The optimal drug design could yield 100% satisfaction grade when both therapeutic effect and the number of targets were considered in the objective. This scenario required regulating one to three and one or two enzyme targets for 50%–95% and 50%–100% VMAT2 and TH deficiencies, respectively. However, their corresponding adverse and target variation effect grades were less satisfactory. For the most severe deficiencies of VMAT2 and TH, a compromise design could be obtained when the effects of therapeutic, adverse, and target variation were simultaneously applied to the optimal drug discovery problem. Such a trade-off design followed the no free lunch theorem for optimization; that is, a more serious dopamine deficiency required more enzyme targets and lower satisfaction grade. In addition, the therapeutic effects of current clinical medications for PD could be enhanced in combination with new enzyme targets. The increase of toxic metabolites after treatment might be the cause of neurotoxic effects of some current PD medications.

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Section: Discussionmentioning

confidence: 99%

Fuzzy Decision Making Approach to Identify Optimum Enzyme Targets and Drug Dosage for Remedying Presynaptic Dopamine Deficiency

Hsu

Wang

2016

PLoS ONE

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“…The selection of the type of representation is an important factor, which directly affects the predictive performance. Various types of protein feature representations have been proposed in the literature, and the major ones employed for the prediction of enzymatic functions can be categorized as homology [ 2 , 10 , 17 ], physicochemical properties [ 9 ], amino acid sequence-based properties [ 2 , 3 , 5 , 7 , 12 , 14 , 15 , 21 – 25 ], and structural properties [ 6 , 10 , 11 , 13 , 18 – 20 ]. There are also a few EC number prediction methods, which utilize the chemical structural properties of compounds that interact with the enzymes [ 4 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature

et al. 2018

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BackgroundThe automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers.ResultsIn ECPred, each EC number constituted an individual class and therefore, had an independent learning model. Enzyme vs. non-enzyme classification is incorporated into ECPred along with a hierarchical prediction approach exploiting the tree structure of the EC nomenclature. ECPred provides predictions for 858 EC numbers in total including 6 main classes, 55 subclass classes, 163 sub-subclass classes and 634 substrate classes. The proposed method is tested and compared with the state-of-the-art enzyme function prediction tools by using independent temporal hold-out and no-Pfam datasets constructed during this study.ConclusionsECPred is presented both as a stand-alone and a web based tool to provide probabilistic enzymatic function predictions (at all five levels of EC) for uncharacterized protein sequences. Also, the datasets of this study will be a valuable resource for future benchmarking studies. ECPred is available for download, together with all of the datasets used in this study, at: https://github.com/cansyl/ECPred. ECPred webserver can be accessed through http://cansyl.metu.edu.tr/ECPred.html.

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“…Thirdly, extracting more useful information from enzyme sequences via different models and predicting the enzyme function using machine learning algorithms is the universal studied direction. Various discrete models to represent enzyme sequences were proposed in hopes to establish some sort of correlation through which the prediction could be more effectively carried out, such as from amino acid composition, to amino acid physico-chemical properties [7], to the various modes of pseudo amino acid composition [8], and to the higher-level forms of pseudo amino acid composition by conjoint triad feature and hierarchical context [9], sequential evolution information [10], InterPro signatures [11], and functional domain information. K-nearest neighbor method, Adaptive fuzzy K-nearest neighbor method [12], support vector machine [13], Neural network system [14], deep learning [15] have been proposed for enzyme classification.…”

Section: Introductionmentioning

confidence: 99%

“…K-nearest neighbor method, Adaptive fuzzy K-nearest neighbor method [12], support vector machine [13], Neural network system [14], deep learning [15] have been proposed for enzyme classification. Fourthly, Enzyme Commission (EC) system specifies the function of an enzyme by four digits and has a tree structure, many researchers predict enzyme functional classes and subclasses from top to bottom method, Che et al [10] predicted enzyme main six classes, corresponding to (1) Oxidoreductase, (2) Transferase, (3) Hydrolase, (4) Lyase, (5) Isomerase, (6) Ligase. EzyPrd is a three-layer predictor that can predict the enzyme main classes and subclasses [16].…”

Section: Introductionmentioning

confidence: 99%

MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier

et al. 2020

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Predicting enzymes function is an important and difficult problem, particularly when enzymes may have the multiplex character, i.e., some enzymes simultaneously have two or three function classes. Most of the existing enzyme function predictor can only be used to deal with the mono-functional enzymes. Actually, multi-functional enzymes should not be ignored because they usually possess diverse biological functions worthy of our special notice. By introducing the ''improved Hybrid Multi-label Classifier'' and ''neighbor score'', a new predictor, called MF-EFP, has been developed that can be used to deal with the systems containing both mono-functional and multi-functional enzymes. As demonstration, the jackknife cross-validation was performed with MF-EFP on a benchmark dataset of enzymes classified into the following 7 functional classes: (1) EC 1 Oxidoreductase, (2) EC 2 Transferase, (3) EC 3 Hydrolase, (4) EC 4 Lyase, (5) EC 5 Isomerase, (6) EC 6 Ligase, (7) EC7 Translocases, where none of enzymes included has ≥90% pairwise sequence identity to any other in a same subset. The subset accuracy and average precision thus obtained by MF-EFP was 85.62% and 94.16% respectively. Extensive experiments also show that MF-EFP can outperform the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, MF-EFP is freely accessible to the public at the web-site http:// www.jci-bioinfo.cn/MF-EFP.

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Identification of Multi-Functional Enzyme with Multi-Label Classifier

Cited by 24 publications

References 76 publications

Fuzzy Decision Making Approach to Identify Optimum Enzyme Targets and Drug Dosage for Remedying Presynaptic Dopamine Deficiency

Fuzzy Decision Making Approach to Identify Optimum Enzyme Targets and Drug Dosage for Remedying Presynaptic Dopamine Deficiency

ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature

MF-EFP: Predicting Multi-Functional Enzymes Function Using Improved Hybrid Multi-Label Classifier

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