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Functional genomics

A cracking combination

Nature Reviews Genetics volume 2, page 915 (2001)Cite this article

Ge et al. began their study by investigating whether genes that are co-expressed at the transcriptional level are more likely to encode proteins that interact with each other. They tested this in yeast, using transcriptional microarray data and a combination of genome-wide two-hybrid data, as well as individual protein data from literature searches. In general, clusters of co-expressed genes do indeed have a higher chance of giving a positive score in the two-hybrid assays and in experimental data derived from the literature. This lends weight to the idea that co-expression and protein interaction are both useful predictors of functional interaction. More importantly, the combination of these two types of data can also provide new insights into functionally related groups of genes. The authors provide an example in which a group of genes that is involved in a network of stress-response protein–protein interactions is divided into two co-expression clusters. This refines the view of how the genes interact and leads to the development of new, testable hypotheses.

Important clues about function can come from protein structure, especially if the proteins in question are distantly related and their amino-acid sequences have diverged. Dietmann and Holm combine sequence, structural and functional information to create a tree of protein structures, in which the most structurally similar proteins lie on the same branch of the tree. This approach overcomes some of the shortcomings of other bioinformatics tools. Their analysis can also infer homology relationships between proteins and group them into 'superfamilies'. The results compare very favourably with a protein classification system that is manually curated by experts (SCOP). Finally, the authors show that their automated system could be used to predict the superfamily membership, and therefore a putative function, of a new protein structure that might be determined as part of a structural genomics project.

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References

  1. ORIGINAL RESEARCH PAPER Ge, H. et al. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nature Genet. 10.1038/ng776 (2001)

  2. Dietmann, S. & Holm, L. Identification of homology in protein structure classification. Nature Struct. Biol. 8, 953–957 (2001)

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  3. FURTHER READING Lichtarge, O. Getting past appearances: the many-fold consequences of remote homology. Nature Struct. Biol. 8, 918–920 (2001)

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  4. Brenner, S. E. A tour of structural genomics. Nature Rev. Genet. 2, 801–809 (2001)

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Authors
  1. Magdalena Skipper
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Skipper, M. A cracking combination. Nat Rev Genet 2, 915 (2001). https://doi.org/10.1038/35103527

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