Extended: New functional views into the proteome using LC-MS and proteomic correlation profiling

Protein-protein interaction networks are dynamic systems that can rearrange to enable productive responses to environmental and developmental conditions. We have pursued the research goal of systems-level understanding of protein complex dynamics using Co-Fractionation Mass Spectrometry (CFMS) as known as protein correlation profiling. In this method, the input sample is fractionated by any biochemical method of choice. The reduced complexity of each fractionated sample leads to better proteome coverage and provides functional information on the proteins based on their separation profiles. This approach has been used to predict organelle localization based on co-purification with marker proteins. We have developed our label-free CFMS pipeline to accept soluble and membrane-associated cell fractions from agriculturally important cell types and use orthogonal chromatographic separations, reproducibility filters, and correlation analyses to predict localization and composition based on experimental data. Our open-ended CFMS pipeline has been adapted in our new evolution proteomics approach to measure the degree to which orthologous protein complexes display variability in multimerization state. Multimerization orthovariants provide insights into how multimerization drives neofunctionalization during plant evolution. A current project focuses on the protein multimerization and localization dynamics that occur during the development of unicellular cotton fibers. Protein complex prediction accuracies are evaluated using bona fide golden standards identified by our novel machine learning approach. Thus, our dataset includes reliability indices for thousands of interesting homo- and heteromeric protein complexes that assemble during fiber extension and/or secondary cell wall synthesis. This methodology has a significant value for the research community to broadly test hypotheses about protein complex function across species, cell types, and evolutionary time scales. We are currently organizing the data from our CFMS studies so that it is findable and useful to the community with the goal of accelerating the genetic engineering of plant traits.