Predicting the interactome from proteomics and genomics data

by Mr Gabriel Östlund (SBC. Stockholm University)

Wednesday 9 Jun 2010, 12:15 → 13:00 Europe/Stockholm

RB35 (RB35)

Two of the major tasks of molecular biology is finding both the biochemical function of all proteins as well as finding which proteins interact with each other. Due to the large number of protein pairs that could potentially interact, experimentally determining all interactions is somewhat infeasible. There are several ongoing efforts at reconstructing the human interactome through computational integration of proteomics and genomics data. The reconstructed networks contains predictions as to how likely protein pairs are to interact, however, due to sparseness of input data, the networks are far from complete.

Our work has been towards better understanding protein and gene interactions, both by generating data for reconstructing interaction networks and by analyzing the reconstructed networks to find novel and biologically relevant pathway connections. One way to deal with sparseness of data is to use orthology transfer, we have continued work with InParanoid, deemed one of the top ranked orthology predictors, by adding additional species, improving accuracy and by streamlining the pipeline, in order to better handle the rapidly increasing number of sequenced species. Prediction accuracy of the protein interactions are contingent on proper handling of input data. We are currently working on how mRNA expression corresponds with relative protein amounts, this could be of utmost importance e.g. when using mRNA co-expression as an indicator of protein functional coupling.

Analyzing protein interaction networks in the context of disease can help elucidate disease mechanisms as well as potential markers or drug targets. We have constructed a new generic network-based approach, MaxLink, for predicting novel candidate members to known biomolecular processes and pathways. A typical application is the identification of new disease genes based on a set of known disease genes. Finally, looking at how patterns of expression, coexpression, higher order correlation of expression change between normal and disease states, guided by a protein interaction network, could potentially give insights into disease mechanisms.