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  • Research article
  • Open Access

Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition

BMC Bioinformatics201011:293

https://doi.org/10.1186/1471-2105-11-293

©  Chen et al; licensee BioMed Central Ltd. 2010

  • Received: 20 November 2009
  • Accepted: 31 May 2010
  • Published:

Abstract

Background

Metabolic pathway is a highly regulated network consisting of many metabolic reactions involving substrates, enzymes, and products, where substrates can be transformed into products with particular catalytic enzymes. Since experimental determination of the network of substrate-enzyme-product triad (whether the substrate can be transformed into the product with a given enzyme) is both time-consuming and expensive, it would be very useful to develop a computational approach for predicting the network of substrate-enzyme-product triads.

Results

A mathematical model for predicting the network of substrate-enzyme-product triads was developed. Meanwhile, a benchmark dataset was constructed that contains 744,192 substrate-enzyme-product triads, of which 14,592 are networking triads, and 729,600 are non-networking triads; i.e., the number of the negative triads was about 50 times the number of the positive triads. The molecular graph was introduced to calculate the similarity between the substrate compounds and between the product compounds, while the functional domain composition was introduced to calculate the similarity between enzyme molecules. The nearest neighbour algorithm was utilized as a prediction engine, in which a novel metric was introduced to measure the "nearness" between triads. To train and test the prediction engine, one tenth of the positive triads and one tenth of the negative triads were randomly picked from the benchmark dataset as the testing samples, while the remaining were used to train the prediction model. It was observed that the overall success rate in predicting the network for the testing samples was 98.71%, with 95.41% success rate for the 1,460 testing networking triads and 98.77% for the 72,960 testing non-networking triads.

Conclusions

It is quite promising and encouraged to use the molecular graph to calculate the similarity between compounds and use the functional domain composition to calculate the similarity between enzymes for studying the substrate-enzyme-product network system. The software is available upon request.

Keywords

  • Triad
  • Similarity Score
  • Benchmark Dataset
  • Enzyme Molecule
  • Product Compound

Background

Metabolism (the Greek word for "change" or "overthrow") is the biochemical modification of chemical compounds in living organisms and cells. It comprises a series of chemical reactions that occur in a cell and enable it to keep living, growing and dividing. Without metabolism we would not be able to survive. Metabolism comprises a series of chemical reactions that occur in a cell and enable it to keep living, growing and dividing. Metabolism usually consists of sequences of enzymatic steps, the so-called metabolic pathways. The number of metabolic pathways is very large, reflecting the fact that "life is extremely complicated". Metabolic pathways interact in a complex way in order to allow an adequate regulation. This interaction includes the enzymatic control and hormone control. In the current study, we are focused on the enzyme control category, where metabolic pathway is the network linking various chemical reactions of compounds (substrates or products) catalyzed by enzymes. As is known, many metabolic pathways are available in the pathway databases, such as KEGG PATHWAY [1], which enable us to analyze known metabolic pathways. However, since there are many compounds and enzymes whose biological functions are not discovered completely, many reactions cannot be determined. Thus, determination of the network of substrate-enzyme-product triads (whether the substrate can be transformed into the product with the catalyst enzyme) would be very helpful for expanding our knowledge about the metabolic pathways, and conducting in-depth studies in this regard. However, it is time-consuming and expensive to determine the network through biological experiments alone. Therefore, it is highly desired if an automated method can be developed to address this problem. Encouraged by the successes of using computational approaches to tackle various problems in different biological systems (see, e.g., [2–7]), here we are to develop a different computational approach for predicting the network of substrate-enzyme-product triads.

The benchmark dataset used in this study consists of positive triads and negative triads, where the number of negative triads was about 50 times as many as positive ones. To evaluate the prediction model, one-tenth triads were randomly selected as testing samples and the rest triads used to train the prediction engine. The Nearest Neighbour Algorithm [8, 9] was used to conduct prediction, where the metric to measure the nearness was formulated by combining the compound similarity and functional domain composition. The compound similarity was calculated based on the SMILES [10, 11] and graph representations [12]; while the functional domain composition representations [13, 14] were used to represent the enzyme samples and estimate their similarity. The highest accuracy thus obtained in predicting the positive triads was 95.41%. Interestingly, it was observed through this research that similar triads always tended to have the same network.

Methods

Materials

Molecular samples were downloaded from the public database KEGG [15, 16] at http://www.genome.jp/kegg/ (release 53.0 in 2010), from which 16,144 molecules were retrieved. Among these molecules, only 2123 compounds take part in the main reactant-pairs in each metabolic reaction of yeast. For these selected small molecules, after removing those that had no information to calculate their similarity with other small molecules, we had 1,326 small molecules left; for enzyme molecules, after removing those whose functional domain compositions were not available, 939 enzyme molecules of yeast genome were obtained.

Although a same substrate might be converted into many products with different catalyst enzymes, a triad and its network would be unique. Each of the triads in the positive dataset consists of two small molecules (one for the substrate and one for the product) and one enzyme molecule. All the triads in the positive dataset were determined by solid experiments, and they were extracted from two KEGG files "reaction" and "enzyme", downloaded from ftp://ftp.genome.jp/pub/kegg/pathway/map/ (8th January, 2010). Each of the samples in the negative dataset, the so-called "negative triad", was generated by randomly picking two small molecules (one for the substrate and one for the product) and one enzyme molecule. Since the possibility for such three molecules to be a positive triad was extremely low, the credibility of the negative dataset thus constructed would be also very high. Also, to reflect the real world that the number of positive triads is much less than that of the negative ones, the negative triads were generated 50 times as many as the positive ones. The final benchmark dataset thus constructed contains 14,592 positive triads and 729,600 negative triads. Positive triads are also termed as networking triads, and negative triads termed as non-networking triads.

In order to evaluate the prediction model, one-tenth positive triads and one-tenth negative triads were randomly selected as testing samples, while the rest triads in the benchmark dataset were used to train the prediction engine. The detail information for the (1,460+72,960) = 74,420 testing samples and (13,132+656,640) = 669,772 training samples can be found in Additional File 1.

Encoding Methods

A key step for conducting accurate prediction and analysis is to effectively encode and compare the three components: substrates, enzymes, and products. Since substrates and products are compounds, some established methods, such as SMILES [10, 11] and MACCS keys [17, 18] can be used to estimate the similarity of compounds. Recently, a method based on graph theory was proposed to measure the similarity of two compounds by means of the undirected graph [12]. Using graphic approaches to study biological systems can provide an intuitive vision and useful insights for helping analyze complicated relations therein, as indicated by many previous studies on a series of important biological topics, such as enzyme-catalyzed reactions [19–26], protein folding kinetics and folding rates [27–29], inhibition of HIV-1 reverse transcriptase [30–32], inhibition kinetics of processive nucleic acid polymerases and nucleases [33], and drug metabolism systems [34]. In this study, a different graph approach [12] will be utilized as described below.

Graph representation

Using graph representation to estimate the similarity of two compounds was proposed by Hattori et al. [12]. According to their method, each chemical structure can be represented by a two-dimensional (2D) graph where the vertices correspond to the atoms and the edges correspond to the bonds between them. The similarity of the two compounds is estimated by detecting their common subgraphs, followed by aligning them accordingly. The similarity score between two compounds by the graph representation can be calculated by the online web-server at http://www.genome.jp/ligand-bin/search_compound. However, the web-server only provides similarity scores that are greater than 0.4. Accordingly, in the current study, the similarity of two compounds is assigned to be zero if it is less than 0.4. The similarity score thus obtained between two compounds c1 and c2 is denoted by Sgraph(c1 c2).

Meanwhile, the following non-graphic SMILES [10, 11] approach will also be utilized to facilitate comparison.

SMILES

Abbreviated from the full name of "Simplified Molecular Input Line Entry System" [10, 11], SMILES is a line representation for compound, which consists of a series of characters without including spaces. The similarity score between two compounds with the SMILES representation can be obtained from a pre-computed database called STITCH [35] at http://stitch.embl.de/cgi/, where the similarity score between two compounds c1 and c2 is denoted by SSMILES(c1, c2)/1000. The developers of STITCH applied the open-source Chemistry Development Kit [36] to calculate the chemical fingerprints and used the Tanimoto 2 D chemical similarity scores [37, 38].

Functional domain composition representation

Since enzyme belongs to protein, we can use various descriptors for proteins as summarized in a recent review [39] to represent enzymes. In this study, we adopted the functional domain composition to represent the enzyme samples because it has been successfully used for predicting various protein attributes [6, 13, 14, 40–46]. The concept of protein functional domain composition was first introduced by Chou and Cai for predicting protein subcellular localization [13], where the SBASE-A database [47] was used that contained 2,005 functional domains. In this research, we used a more complete database, the InterPro database (release 23.1, December 2009) [48] that contained 21,144 functional domain entries. Accordingly, by following the similar procedures as elaborated in [13], an enzyme molecule e can be formulated as the following 21144-D vector
(1)
where x i = 1 if there is a hit at the i-th functional domain entry by searching the InterPro database for the enzyme sample e; otherwise, x i = 0. Thus, the similarity between two enzyme molecules, e1 and e2 is given by [13]
(2)

where is the dot product of two vectors, and and are their modulus, respectively.

Thus, the similarities between any two substrate-enzyme-product triads can be calculated using the above equations, as will be further discussed below.

K-Nearest Neighbour Algorithm (KNN)

In this research, the K-Nearest Neighbour (KNN) algorithm [5, 8] was applied to predict a query triad belonging to networking or non-networking. To utilizing the KNN algorithm, we have to first define a metric to measure the nearness between two triads T1 = (s1, e1, p1) and T2 = (s2, e2, p2), where s1, e1, p1 represent the substrate, enzyme, product in the first triad T1, and s2. e2, p2 those in the second triad T2. Since there are three members in each triad, and we do not know which one of the three will play more important role in determining the network, let us first define the following metric with a weight parameter to measure the nearness between the two triads:
(3)

where the weight factor w can be obtained by optimizing the predicted result. According to the KNN rule [8, 49, 50], also named the "voting KNN rule", a query triad should be assigned to the class represented by a majority of its K nearest neighbours. If the majority of its K nearest neighbour triads belong to the triad networking, and so does the query triad; otherwise, it belongs to the non-networking triad.

Accuracy Measurement

The accuracy of prediction is defined by
(4)
where TP represents true positives, TN true negative, FP false positives, and FN false negative [51–54], with
(5)
for the sensitivity and
(6)

for the specificity.

In order to evaluate the performance of prediction models more accurate, Matthew's correlation coefficient (MCC) [55] was employed in this study, which is defined by
(7)

Results

The predicted accuracies with K = 1 and w = 1/4, 1/2, and 3/4 for the testing triads in which the substrate and product compounds were represented by SMILES are given in Table 1, while those with graph to represent the compounds are given in Table 2. The detailed predicted results are provided in Additional File 2.
Table 1

Prediction accuracies of testing samples using SMILES to represent substrate and product compounds.

w

Prediction accuracy for each class (%)

Overall prediction accuracy

(ACC) (%)

Matthew's correlation coefficient (MCC) (%)

 

Networking triads (SN)

Non-networking triads (SP)

  

1/4

94.25

94.95

94.94

49.14

1/2

83.01

87.77

87.68

28.62

3/4

79.11

83.74

83.65

22.94

Table 2

Prediction accuracies of testing samples using graph to represent substrate and product compounds.

w

Prediction accuracy for each class (%)

Overall prediction accuracy

(ACC) (%)

Matthew's correlation coefficient (MCC) (%)

 

Networking triads (SN)

Non-networking triads (SP)

  

1/4

95.41

98.77

98.71

75.67

1/2

85.68

97.56

97.32

58.39

3/4

82.19

97.47

97.17

55.77

It can be seen from Table 1 and 2 that, when w = 1/4 and using the graph representation for the substrate and product compounds, we obtained not only the highest overall prediction accuracy (ACC = 98.71%) but also the highest MCC value (MCC = 75.67%), indicating that the graph representation approach is really quite effective.

Shown in Table 3 are the prediction accuracies when K = 3, 5, and w = 1/4. Compared with the case of K = 1, although the rate for the non-networking triads was remarkably increased somewhat, the rate for the networking triads was decreased.
Table 3

Prediction accuracies of testing samples using different K.

Representation of compound

K

Prediction accuracy for each class (%)

  

Networking triads (SN)

Non-networking triads (SP)

SMILES

3

92.67

92.03

 

5

89.79

92.92

Graph

3

95.34

99.48

 

5

94.18

99.48

Discussion

Our results have shown that, in the study of the substrate-enzyme-product triad network, it is quite promising and encouraged to use the functional domain composition to represent enzyme and use the graph descriptor to represent substrate and product compounds, fully consistent with the advantage of using functional domain to represent enzyme samples for predicting enzyme family classification [56–58] and the advantage of using the graph descriptor to represent compounds as discussed in [12].

As indicated in Additional File 1, there are 1,460 positive triads in testing samples. For each of these positive triads T i (i = 1,2,⋯,1460), we calculated the distance of Eq.3 (with w = 1/4 and using the graph descriptor for substrate and product compounds) from T i to its nearest positive triad and nearest negative triad in the training set, respectively. Denote the two distances thus obtained by P i and N i , respectively. Shown in Fig 1 are two curves generated from P i and N i , named as P-curve and N-curve, respectively. The P-curve is the one with the index i of T i as its X-axis and P i as its Y-axis. The N-curve is the one with the index i of T i as its X-axis and N i as its Y-axis. It can be seen from Fig 1 that the N-curve is almost always above the P-curve, meaning that the distances of the 1,460 testing triads to their nearest positive triads in the training set are almost always smaller than those to their nearest negative triads in the training set, fully consistent with the very high success rate of 95.41% for predicting the 1,460 networking triads, as shown in Table 2. Furthermore, for the distribution of these distance values, there are 1,104 (75.62%) T i with P i < 0.15, while there are only 174 (11.92%) T i with N i < 0.15. The most of N i (1268, 86.85%) were clustered in the interval from 0.15 to 0.4, indicating that the distance defined by Eq.3 for the KNN algorithm with w = 1/4 can separate the positive triads and negative triads very well. Also, since the distance of Eq.3 is defined based on the similarities of two substrates, two enzymes and two products, the smaller the distance between the two triads, the more similar the two triads are. It is interesting to see from the current study that the similar triads as defined by our formulation almost always exhibit the same network.
Figure 1
Figure 1

P-curve and N-curve.

As indicated by comparing the results in Table 1, Table 2 and Table 3, the best predicted rate for the 1,460 networking triads in the testing set was 95.41%, with w = 1/4 and K = 1. Of these triads, 67 were mispredicted. It is instructive to see the reason behind these by examining Table 4, where the difference between the distance to the nearest positive triad and the distance to the nearest negative triad for each of the 67 misclassified triad samples was given. As we can see from the table, the maximum difference was 0.285 and the minimum difference was 0.000256. Shown in Fig 2 is the distribution of the distance differences listed in Table 4. Of the 67 misclassified positive samples, 47 (70.15%) samples are with the distance differences less than 0.1, implying that the mispredicted triads are pretty close to the margin of correct prediction, and that the current metric as defined in Eq.3 for measuring the nearness for the KNN algorithm is quite effective.
Table 4

Distance to nearest positive triads and negative triads of misclassified positive triads.

Substrates

Enzymes

Products

Distance

Differences

   

Positive triads

Negative triads

 

C00002

YIL139C

C06397

0.24

0.19125

0.04875

C00002

YPL271W

C00008

0.25

0.22125

0.02875

C00002

YPR033C

C00020

0.1

0.03375

0.06625

C00003

YKR066C

C00004

0.25

0.225

0.025

C00003

YPR167C

C00004

0.25

0.177831

0.072169

C00010

YER090W

C00024

0.25

0.1125

0.1375

C00010

YER178W

C00024

0.189188

0.1425

0.046688

C00024

YAL054C

C00033

0.21

0.199626

0.010374

C00024

YCL030C

C06548

0.25

0.0975

0.1525

C00024

YLR153C

C00033

0.21

0.199626

0.010374

C00025

YHR037W

C03912

0.375

0.202643

0.172357

C00026

YIR034C

C00449

0.271688

0.25

0.021688

C00035

YGL047W

C00096

0.1875

0.165

0.0225

C00037

YOL049W

C00051

0.48375

0.25

0.23375

C00047

YPL096W

C12989

0.25

0.225

0.025

C00055

YBL013W

C04121

0.177831

0.12375

0.054081

C00055

YDR410C

C04121

0.25

0.22125

0.02875

C00055

YKR069W

C04121

0.25

0.19125

0.05875

C00065

YBR263W

C00143

0.375

0.25

0.125

C00065

YLR058C

C00143

0.375

0.25

0.125

C00083

YPL231W

C12647

0.073223

0.02625

0.046973

C00085

YKL104C

C00352

0.25

0.2325

0.0175

C00086

YIR029W

C00499

0.4525

0.375

0.0775

C00096

YBR252W

C00144

0.25

0.12

0.13

C00096

YGR036C

C00636

0.25

0.24375

0.00625

C00108

YDR354W

C04302

0.375

0.2325

0.1425

C00109

YCL018W

C06032

0.383376

0.36625

0.017126

C00118

YGL026C

C03506

0.375

0.37375

0.00125

C00143

YGL125W

C00440

0.3025

0.28375

0.01875

C00155

YNL256W

C01118

0.25

0.22125

0.02875

C00167

YJR131W

C00191

0.25

0.21

0.04

C00191

YOR065W

C05787

0.25

0.19875

0.05125

C00223

YDR062W

C12096

0.0825

0.082244

0.000256

C00223

YMR296C

C12096

0.0825

0.04875

0.03375

C00234

YDR408C

C04376

0.36625

0.32125

0.045

C00333

YJR153W

C00470

0.375

0.12375

0.25125

C00448

YDL205C

C16144

0.225

0.19125

0.03375

C00582

YHL003C

C05598

0.25

0.1875

0.0625

C00582

YKL008C

C05598

0.25

0.1875

0.0625

C00632

YDR120C

C05831

0.25

0.15

0.1

C00652

YML086C

C06316

0.565

0.36625

0.19875

C00842

YDR127W

C06017

0.1125

0.09

0.0225

C00864

YDR531W

C03492

0.25125

0.25

0.00125

C00931

YDL205C

C01024

0.59625

0.375

0.22125

C01063

YBL015W

C09813

0.1275

0.1125

0.015

C01079

YDR044W

C03263

0.41875

0.25

0.16875

C01096

YCL030C

C02888

0.25

0.22875

0.02125

C01100

YIL116W

C01267

0.375

0.25

0.125

C01902

YML008C

C08830

0.375

0.25

0.125

C02411

YGR155W

C03058

0.09

0.075

0.015

C02909

YHR007C

C14098

0.25

0.195

0.055

C03012

YDR402C

C11713

0.36375

0.2575

0.10625

C03598

YPR167C

C04297

0.25

0.1875

0.0625

C04751

YAR015W

C04823

0.34

0.32875

0.01125

C04874

YDR452W

C05925

0.16875

0.125

0.04375

C06102

YLR231C

C06105

0.535

0.25

0.285

C06397

YBR029C

C07838

0.18375

0.17625

0.0075

C06599

YNL202W

C06600

0.147938

0.113376

0.034562

C06714

YDR127W

C06723

0.0975

0.08625

0.01125

C07649

YDR402C

C12673

0.55

0.3625

0.1875

C07732

YGR234W

C07733

0.3075

0.25

0.0575

C09811

YGL063W

C09812

0.1125

0.10125

0.01125

C11907

YPR118W

C11908

0.25

0.22875

0.02125

C11923

YFR015C

C12384

0.25

0.03

0.22

C11923

YLR258W

C12384

0.25

0.03

0.22

C14082

YHR007C

C14089

0.25

0.195

0.055

C15786

YGR060W

C15797

0.09375

0.08625

0.0075

Figure 2
Figure 2

Distribution of differences in Table 4.

Like most of the other prediction methods, the current prediction method also has its own limitation. For example, for those query triads without any similarity at all to any of the triads in the training datasets, the performance of the current prediction method might be poor. This is because the current prediction method was established on the basis of the "triad similarity", i.e., the similarity between substrates, between enzymes, and between products.

As pointed out by one of the anonymous reviewers, it would be interesting to further discuss the current algorithm from the viewpoint of divergent and convergent evolution [59]. We shall work on such an interesting topic in our future work.

Conclusions

Metabolic pathway is one of the key biological networks, consisting of many metabolic reactions involving substrates, enzymes, and products, where substrates can be transformed into products with some particular catalytic enzymes. Knowledge about the network of substrate-enzyme-product triads is very useful for in-depth studies of the metabolic pathways. It is both time-consuming and costly to determine the network through biological experiments alone, and hence it is highly desired to develop computational methods in this regard. The computational method reported in this paper can be used to identify the network of substrate-enzyme-product triads with quite high success rate. It is anticipated that the method may become a very useful tool for studying drug metabolism systems. Meanwhile, as shown through this study, it is quite promising to introduce the molecular graph and functional domain composition into this area. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [60], we shall design a user-friendly web-server for the prediction method so that many experimental bench scientists can easily use it to get the desired results without the need to go through all the mathematical details.

Declarations

Acknowledgements

We would like to take this opportunity to thank the four anonymous Reviewers for their constructive comments, which are very helpful for strengthening the presentation of this paper. This study was supported by the Grant from Shanghai Commission for Science and Technology (KSCX2-YW-R-112).

Authors’ Affiliations

(1)
Institute of Systems Biology, Shanghai University, Shanghai, 200444, PR China
(2)
College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, PR China
(3)
Centre for Computational Systems Biology, Fudan University, Shanghai, 200433, PR China
(4)
Division of Imaging Science, Medical School, Stopford Building, The University of Manchester, M13 9PT, UK
(5)
Gordon Life Science Institute, San Diego, California 92130, USA
(6)
CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, PR China

References

  1. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, et al.: KEGG for linking genomes to life and the environment. Nucleic Acids Res 2008, (36 Database):D480–484.Google Scholar
  2. Chou KC: Review: Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry 2004, 11: 2105–2134.View ArticlePubMedGoogle Scholar
  3. Chou KC, Cai YD, Zhong WZ: Predicting networking couples for metabolic pathways of Arabidopsis. EXCLI Journal (Experimental and Clinical Sciences International Online Journal for Advances in Science) 2006, 5: 55–65.Google Scholar
  4. Wang JF, Yan JY, Wei DQ, Chou KC: Binding of CYP2C9 with diverse drugs and its implications for metabolic mechanism. Medicinal Chemistry 2009, 5: 263–270. 10.2174/157340609788185954View ArticlePubMedGoogle Scholar
  5. Chou KC, Shen HB: Recent progress in protein subcellular location prediction. Anal Biochem 2007, 370(1):1–16. 10.1016/j.ab.2007.07.006View ArticlePubMedGoogle Scholar
  6. Chou KC, Shen HB: A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS ONE 2010, 5(4):e9931. 10.1371/journal.pone.0009931View ArticlePubMedPubMed CentralGoogle Scholar
  7. Du QS, Huang RB, Wang SQ, Chou KC: Designing inhibitors of M2 proton channel against H1N1 swine influenza virus. PLoS ONE 2010, 5(2):e9388. 10.1371/journal.pone.0009388View ArticlePubMedPubMed CentralGoogle Scholar
  8. Cover TM, Hart PE: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 1967, 13: 21–27. 10.1109/TIT.1967.1053964View ArticleGoogle Scholar
  9. Chou KC, Cai YD: Prediction of protein subcellular locations by GO-FunD-PseAA predicor. Biochemical and Biophysical Research Communications 2004, 320: 1236–1239. 10.1016/j.bbrc.2004.06.073View ArticlePubMedGoogle Scholar
  10. Weininger D: SMILES 1. Introduction and Encoding Rules. J Chem Inf Comput Sci 1988, 28: 31–36.View ArticleGoogle Scholar
  11. Qu DL, Fu B, Muraki M, Hayakawa T: An encoding system for a group contribution method. J Chem Inf Comput Sci 1992, 32: 443–447.View ArticleGoogle Scholar
  12. Hattori M, Okuno Y, Goto S, Kanehisa M: Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J Am Chem Soc 2003, 125(39):11853–11865. 10.1021/ja036030uView ArticlePubMedGoogle Scholar
  13. Chou KC, Cai YD: Using functional domain composition and support vector machines for prediction of protein subcellular location. Journal of Biological Chemistry 2002, 277(48):45765–45769. 10.1074/jbc.M204161200View ArticlePubMedGoogle Scholar
  14. Cai YD, Zhou GP, Chou KC: Support vector machines for predicting membrane protein types by using functional domain composition. Biophysical Journal 2003, 84: 3257–3263. 10.1016/S0006-3495(03)70050-2View ArticlePubMedPubMed CentralGoogle Scholar
  15. Goto S, Nishioka T, Kanehisa M: LIGAND: chemical database for enzyme reactions. Bioinformatics 1998, 14(7):591–599. 10.1093/bioinformatics/14.7.591View ArticlePubMedGoogle Scholar
  16. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 2006, (34 Database):D354–357. 10.1093/nar/gkj102Google Scholar
  17. Fukunaga K: Introduction to Statistical Pattern Recognition. 2nd edition. New York: Academic; 1990.Google Scholar
  18. McGregor MJ, Pallai PV: Clustering of large databases of compounds: Using MDL "Keys" as structural descriptors. J Chem Inf Comput Sci 1997, 37: 443–448.View ArticleGoogle Scholar
  19. Chou KC: A new schematic method in enzyme kinetics. European Journal of Biochemistry 1980, 113: 195–198.View ArticlePubMedGoogle Scholar
  20. Chou KC, Forsen S: Graphical rules for enzyme-catalyzed rate laws. Biochemical Journal 1980, 187: 829–835.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Chou KC, Forsen S: Graphical rules of steady-state reaction systems. Canadian Journal of Chemistry 1981, 59: 737–755. 10.1139/v81-107View ArticleGoogle Scholar
  22. Chou KC, Liu WM: Graphical rules for non-steady state enzyme kinetics. Journal of Theoretical Biology 1981, 91: 637–654. 10.1016/0022-5193(81)90215-0View ArticleGoogle Scholar
  23. Zhou GP, Deng MH: An extension of Chou's graphical rules for deriving enzyme kinetic equations to system involving parallel reaction pathways. Biochemical Journal 1984, 222: 169–176.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Myers D, Palmer G: Microcomputer tools for steady-state enzyme kinetics. Bioinformatics (original: Computer Applied Bioscience) 1985, 1(2):105–110.View ArticleGoogle Scholar
  25. Chou KC: Graphical rules in steady and non-steady enzyme kinetics. Journal of Biological Chemistry 1989, 264: 12074–12079.PubMedGoogle Scholar
  26. Andraos J: Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws: new methods based on directed graphs. Canadian Journal of Chemistry 2008, 86: 342–357. 10.1139/V08-020View ArticleGoogle Scholar
  27. Chou KC: Review: Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophysical Chemistry 1990, 35: 1–24. 10.1016/0301-4622(90)80056-DView ArticlePubMedGoogle Scholar
  28. Chou KC, Shen HB: FoldRate: A web-server for predicting protein folding rates from primary sequence. The Open Bioinformatics Journal 2009, 3: 31–50. [http://www.bentham.org/open/tobioij/] 10.2174/1875036200903010031View ArticleGoogle Scholar
  29. Chou KC, Shen HB: Review: recent advances in developing web-servers for predicting protein attributes. Natural Science 2009, 2: 63–92. [http://www.scirp.org/journal/NS/] 10.4236/ns.2009.12011View ArticleGoogle Scholar
  30. Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Chou KC, Kezdy FJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F: Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry 1993, 32: 6548–6554. 10.1021/bi00077a008View ArticlePubMedGoogle Scholar
  31. Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Chou KC, Kezdy FJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F: Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. Journal of Biological Chemistry 1993, 268: 6119–6124.PubMedGoogle Scholar
  32. Althaus IW, Gonzales AJ, Chou JJ, Diebel MR, Chou KC, Kezdy FJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F: The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. Journal of Biological Chemistry 1993, 268: 14875–14880.PubMedGoogle Scholar
  33. Chou KC, Kezdy FJ, Reusser F: Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Analytical Biochemistry 1994, 221: 217–230. 10.1006/abio.1994.1405View ArticlePubMedGoogle Scholar
  34. Chou KC: Graphic rule for drug metabolism systems. Current Drug Metabolism 2010, 11: 369–78. 10.2174/138920010791514261View ArticlePubMedGoogle Scholar
  35. Kuhn M, Mering C, Campillos M, Jensen LJ, Bork P: STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res 2008, (36 Database):D684–688.Google Scholar
  36. Steinbeck C, Hoppe C, Kuhn S, Floris M, Guha R, Willighagen EL: Recent developments of the chemistry development kit (CDK) - an open-source java library for chemoand bioinformatics. Curr Pharm Des 2006, 12: 2111–2120. 10.2174/138161206777585274View ArticlePubMedGoogle Scholar
  37. Martin YC, Kofron JL, Traphagen LM: Do structurally similar molecules have similar biological activity? J Med Chem 2002, 45(19):4350–4358. 10.1021/jm020155cView ArticlePubMedGoogle Scholar
  38. Willett P, Barnard JM, Downs GM: Chemical similarity searching. Journal of Chemical Information and Computer Sciences 1998, 38(6):983–996.Google Scholar
  39. Chou KC: Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Current Proteomics 2009, 6(4):262–274. 10.2174/157016409789973707View ArticleGoogle Scholar
  40. Chou KC, Cai YD: Predicting protein structural class by functional domain composition. Biochem Biophys Res Commun 2004, 321(4):1007–1009. 10.1016/j.bbrc.2004.07.059View ArticlePubMedGoogle Scholar
  41. Jia P, Qian Z, Zeng Z, Cai Y, Li Y: Prediction of subcellular protein localization based on functional domain composition. Biochem Biophys Res Commun 2007, 357(2):366–370. 10.1016/j.bbrc.2007.03.139View ArticlePubMedGoogle Scholar
  42. Yu X, Wang C, Li Y: Classification of protein quaternary structure by functional domain composition. BMC Bioinformatics 2006, 7: 187. 10.1186/1471-2105-7-187View ArticlePubMedPubMed CentralGoogle Scholar
  43. Xu X, Yu D, Fang W, Cheng Y, Qian Z, Lu W, Cai Y, Feng K: Prediction of peptidase category based on functional domain composition. J Proteome Res 2008, 7(10):4521–4524. 10.1021/pr800292wView ArticlePubMedGoogle Scholar
  44. Chou KC, Shen HB: ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem Biophys Res Comm 2008, 376: 321–325. 10.1016/j.bbrc.2008.08.125View ArticlePubMedGoogle Scholar
  45. Shen HB, Chou KC: QuatIdent: A web server for identifying protein quaternary structural attribute by fusing functional domain and sequential evolution information. Journal of Proteome Research 2009, 8: 1577–1584. 10.1021/pr800957qView ArticlePubMedGoogle Scholar
  46. Xiao X, Wang P, Chou KC: Predicting protein quaternary structural attribute by hybridizing functional domain composition and pseudo amino acid composition. Journal of Applied Crystallography 2009, 42: 169–173. 10.1107/S0021889809002751View ArticleGoogle Scholar
  47. Murvai J, Vlahovicek K, Barta E, Pongor S: The SBASE protein domain library, release 8.0: a collection of annotated protein sequence segments. Nucleic Acids Res 2001, 29(1):58–60. 10.1093/nar/29.1.58View ArticlePubMedPubMed CentralGoogle Scholar
  48. Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L, Duquenne L, et al.: InterPro: the integrative protein signature database. Nucleic Acids Res 2009, (37 Database):D211–215. 10.1093/nar/gkn785Google Scholar
  49. Denoeux T: A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics 1995, 25: 804–813. 10.1109/21.376493View ArticleGoogle Scholar
  50. Keller JM, Gray MR, Givens JA: A fuzzy k-nearest neighbours algorithm. IEEE Trans Syst Man Cybern 1985, 15: 580–585.View ArticleGoogle Scholar
  51. Cai YD, Chou KC: Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition. Biochem Biophys Res Comm 2003, 305: 407–411. 10.1016/S0006-291X(03)00775-7View ArticlePubMedGoogle Scholar
  52. Ding CH, Dubchak I: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 2001, 17: 349–358. 10.1093/bioinformatics/17.4.349View ArticlePubMedGoogle Scholar
  53. Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16(5):412–424. 10.1093/bioinformatics/16.5.412View ArticlePubMedGoogle Scholar
  54. Shen HB, Chou KC: Predicting protein fold pattern with functional domain and sequential evolution information. Journal of Theoretical Biology 2009, 256: 441–446. 10.1016/j.jtbi.2008.10.007View ArticlePubMedGoogle Scholar
  55. Matthews B: Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 1975, 405: 442–451.View ArticlePubMedGoogle Scholar
  56. Cai YD, Chou KC: Using functional domain composition to predict enzyme family classes. Journal of Proteome Research 2005, 4: 109–111. 10.1021/pr049835pView ArticlePubMedGoogle Scholar
  57. Cai YD, Chou KC: Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. Journal of Proteome Research 2005, 4: 967–971. 10.1021/pr0500399View ArticlePubMedGoogle Scholar
  58. Shen HB, Chou KC: EzyPred: A top-down approach for predicting enzyme functional classes and subclasses. Biochem Biophys Res Comm 2007, 364: 53–59. 10.1016/j.bbrc.2007.09.098View ArticlePubMedGoogle Scholar
  59. Almonacid DE, Yera ER, Mitchell JB, Babbitt PC: Quantitative comparison of catalytic mechanisms and overall reactions in convergently evolved enzymes: implications for classification of enzyme function. PLoS Comput Biol 2010, 6(3):e1000700. 10.1371/journal.pcbi.1000700View ArticlePubMedPubMed CentralGoogle Scholar
  60. Chen C, Chen L, Zou X, Cai P: Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. Protein & Peptide Letters 2009, 16(1):27–31. 10.2174/092986609787049420View ArticleGoogle Scholar

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