Using indirect protein interactions for the prediction of Gene Ontology functions

Protein-protein interaction has been used to complement traditional sequence homology to elucidate protein function. Most existing approaches only make use of direct interactions to infer function, and some have studied the application of indirect interactions for functional inference but are unable to improve prediction performance. We have previously proposed an approach, FS-Weighted Averaging, which uses topological weighting and level-2 indirect interactions (protein pairs connected via two interactions) for predicting protein function from protein interactions and have found that it yields predictions with superior precision on yeast proteins over existing approaches. Here we study the use of this technique to predict functional annotations from the Gene Ontology for seven genomes: Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Rattus norvegicus, Mus musculus, and Homo sapiens.

Our analysis shows that protein-protein interactions provide supplementary coverage over sequence homology in the inference of protein function and is definitely a complement to sequence homology. We also find that FS-Weighted Averaging consistently outperforms two classical approaches, Neighbor Counting and Chi-Square, across the seven genomes for all three categories of the Gene Ontology. By randomly adding and removing interactions from the interactions, we find that Weighted Averaging is also rather robust against noisy interaction data.

We have conducted a comprehensive study over seven genomes. We conclude that FS-Weighted Averaging can effectively make use of indirect interactions to make the inference of protein functions from protein interactions more effective. Furthermore, the technique is general enough to work over a variety of genomes.