Intermolecular Interactions and Biological Pathways Chapter 10 Bioinformatics

1. Intermolecular Interactions and Biological Pathways Chapter 10 Bioinformatics A Practical Guide to the Analysis of Genes and Proteins Third Edition By Andreas Baxevanis B. F. Francis Ouellette

2. - Central Dogma of molecular biology: • “DNA makes RNA makes Protein” • - But proteins interact to make metabolism • In other words, we must understand protein interactions in order to understand gene function. • Since proteins do not interact just in pairs or in linear pathways, the interactions are described as networks • together with microarray studies (to quantify gene expression) this work is the foundation of systems biology

3. Interaction Networks are composed of pathways. • These pathways can be further divided into: • metabolic pathways • cell signaling pathways • gene regulation pathways • These pathway categories are not mutually exclusive

4. - There are several different interaction databases available. • - These databases are not as simple to construct as are the sequence databases • There is a tradeoff between complexity (completeness) and simplicity (user friendliness) • They may curated or automated (e.g., derived from text- mining of the literature). • Data updating is a big challenge, especially for the curated databases.

5. Algorithms for identifying protein-protein interactions take the following data into account • Direct experimental evidence • Indirect evidence such as: • - co-purification • - yeast two-hybrid assays • - molecular cross-linking

6. Methods for biological pathway reconstruction also depend on direct and indirect methods. • direct experimental evidence • “gene neighborhood” • gene fusion • phylogenetic profile • gene sequence similarity • similar patterns of gene expression in microarray expts • “interlogs” (orthologous interactions)

7. The KEGG database focuses primarily on biochemical pathways.

8. The KEGG database builds pathways based on consensus data from many organisms (although the data are mainly from a relatively few “model” organisms). It includes enzymes, substrates and products. It also provides links to the genes encoding the enzymes.

10. TCA cycle in KEGG

17. The STRING database contains pre-computed results for protein interactions and gene interactions. These results are based on: - gene fusion data - phylogenetic profiles - co-expression patterns - co-mentioned in PubMed abstract (text mining)

19. STRING database – useful when you start from a single gene *

22. Summary Network view