From proteomic data to networks: statistics and methods reveal ciliary protein interaction landscape

Objective

The assembly of protein interaction networks (PIN) is an important step to understand the biological function of proteins. Affinity purification coupled to mass spectrometry (AP-MS) has become the technique of choice for the assembly and analysis of PINs. However, most current studies, especially in human cells, are focused on specific biological systems (e.g. the cilium) resulting in datasets of a small to intermediate scale. In such cases, methods that developed for genome-scale datasets are of limited utility. We propose here a framework that is specifically designed for the analysis of incomplete proteomic data focused on ciliary function and ciliopathies.

Methods

The proposed framework consists of three steps. Initially, a revised Socio-Affinity algorithm [1] is applied to quantify the pairwise protein interaction affinities. After filtering hits from noise the constructed PIN is mined for protein clusters using a novel graph-clustering algorithm. Finally, Principle Component Analysis (PCA) is used to assess the quality of detected complexes.

Results

We applied our algorithm to data from more than 400 TAP-MS experiments, using over 200 ciliary genes as baits. Weighted PINs consisting of low, medium and high confidence interactions were extracted from the data, and for each network a set of protein complexes is reported. Several known ciliary complexes have been successfully identified, while novel ciliary complexes are predicted.

Conclusion

We demonstrate a computational framework that can deal with context specific proteomic data. Application to experimental data focusing on cilia provides a ciliary protein interaction landscape with the ciliary biological processes/functions in the centre.

Equal contribution by Q. Lu and K. Koutroumpas