Name
Title | ||
|---|---|---|
Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition. |
Abstract | ||
|---|---|---|
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.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.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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1186/1471-2105-11-293 | BMC Bioinformatics |
Keywords | Field | DocType |
mathematical model,enzyme,microarrays,prediction model,metabolic pathway,bioinformatics,kinetics,enzymes,algorithms,structure activity relationship,binding sites | Molecular graph,Substrate (chemistry),Nearest neighbour algorithm,Biology,Triad (anatomy),Bioinformatics | Journal |
Volume | Issue | ISSN |
11 | 1 | 1471-2105 |
Citations | PageRank | References |
12 | 2.31 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Chen | 1 | 12 | 2.65 |
| Kaiyan Feng | 2 | 34 | 4.60 |
| Yu-Dong Cai | 3 | 340 | 34.45 |
| Kuo-Chen Chou | 4 | 946 | 64.26 |
| Hai-Peng Li | 5 | 12 | 2.99 |