Abstract
Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology (‘MCAM’) employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems.
Highlights
Large and complex high-throughput proteomic experimental studies are becoming more accessible through the use of powerful, swiftly developing platforms such as mass spectrometry (MS), flow cytometry (FC), and various kinds of protein microarrays (PMA) [1,2,3]
As one particular example of increasing attention, there has been an explosion in large-scale datasets for receptor tyrosine kinase (RTK) network signaling by the combination of protein post-translational modification enrichment followed by quantitative MS methods [4]
Multiple clustering analysis methodology (MCAM) was developed to capitalize on the success unsupervised learning has had on biological inference in the past and apply it to a new challenge in the field, that of understanding the function and regulation of phosphorylation in the ERBB network
Summary
Large and complex high-throughput proteomic experimental studies are becoming more accessible through the use of powerful, swiftly developing platforms such as mass spectrometry (MS), flow cytometry (FC), and various kinds of protein microarrays (PMA) [1,2,3]. As one particular example of increasing attention, there has been an explosion in large-scale datasets for receptor tyrosine kinase (RTK) network signaling by the combination of protein post-translational modification enrichment followed by quantitative MS methods [4]. In receptor tyrosine kinase (RTK) networks, such as those activated by the ERBB family of receptors, phosphorylation plays a central role in the translation of extracellular cues into phenotypic changes, such as differentiation, proliferation, and migration [5]. Mass spectrometry measurement of phosphorylation events in cellular signaling networks is greatly increasing our understanding of the specific modifications occurring in the cell as well as their relative changes in response to network perturbations, such as ligand stimulation or kinase inhibition. Unsupervised computational learning methods, applied to quantitative phosphoproteomic data, provides one method by Author Summary
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
