Publication: Network-based function prediction and interactomics: The case for metabolic enzymes
All || By Area || By Year| Title | Network-based function prediction and interactomics: The case for metabolic enzymes | Authors/Editors* | Sarath C. Janga, Juan Javier DÃaz-MejÃa, Gabriel Moreno-Hagelsieb |
|---|---|
| Where published* | Metabolic Engineering |
| How published* | Journal |
| Year* | 2010 |
| Volume | |
| Number | |
| Pages | |
| Publisher | Elsevier |
| Keywords | |
| Link | http://www.ncbi.nlm.nih.gov/pubmed/20654726 |
| Abstract |
As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important for a protein's three dimensional folding and function. Nevertheless, prediction of protein function from homology-driven approaches is not without problems. Homologous proteins might evolve different functions and the power of homology detection has already started to reach its maximum. Computational methods for inferring protein function, which exploit the context of a protein in cellular networks, have come to be built on top of homology-based approaches. These network-based functional inference techniques provide both a first hand hint into a proteins' functional role and offer complementary insights to traditional methods for understanding the function of uncharacterized proteins. Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level. These techniques not only promise to solve the moonlighting aspect of proteins by annotating proteins with multiple functions, but also increase our understanding on the interplay between different functional classes in a cell. In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes. Given the volume of high-throughput data that is being reported the time is ripe to employ these network-based approaches, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases. |
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