Mapping protein–protein interaction networks
There have been marked improvements in experimental protocols in affinity purification, as well as statistical and data science methods to filter out false positives in pulldown experiments. Chiang et al. recently elucidated the interactome of the protein phosphtase 1 catalytic subunit (PP1c), identifying 78 interacting partners in human heart. The proteomics results found increased binding to PDE5A in paroxysmal atrial fibrillation patients to impair proteins involved in electrical and calcium remodeling, a result that has implications in the understanding and treatment of atrial fibrillation [80]. Waldron et al. identified the TBX5 interactome in the developing heart to discover its interactions with the repressor complex NuRD, elucidating the mechanisms by which TBX5 mutations can influence cardiac development and confer congenital heart diseases. The accretion of public-domain protein–protein interactome data are also serving as a permanent resource that benefits other investigators outside the proteomics field, and in one but many recent examples provided important context to systems genetics experimental data to evidence the involvement of an interacting cilia protein network in congenital heart diseases [81]. More recently, the CoPIT method extends the scope of comparison to degrees of interactions among samples across cell states with more rigorous statistics, and is particularly notable in its suitability for quantifying differential interactomes of membrane proteins in human diseases [47]. Potential protein–protein interactions can now also be predicted in silico and de novo using machine learning algorithms that take in experimental data and auxiliary information [82].
At the same time, there is renewed interest to perform crosslinker studies on a large scale, which in addition to identifying protein–protein interaction partners, can provide information on the topology and protein domains involved in the interactions. Again we note that the development of new proteomics methodologies now necessitates hand-in-hand advances of novel data science solutions almost without exception. An example is the application of chemical cross-linkers in proteomics, which allows the linking of proximal proteins to quantify the degrees and likelihood of protein–protein interactions in their native cellular environment. Cross-linking proteomics experiments are however infeasible without specialized search engines that can consider the combinatorics of crosslinked peptide sequences, and identify interacting proteins whilst controlling for the FDR that result from the quadratic increase of search space [83, 84].