Many genes required for cell polarity development in budding yeast have been identified and arranged into a functional hierarchy. Core elements of the hierarchy are widely conserved, underlying cell polarity development in diverse eukaryotes. To enumerate more fully the protein–protein interactions that mediate cell polarity development, and to uncover novel mechanisms that coordinate the numerous events involved, we carried out a large-scale two-hybrid experiment. 68 Gal4 DNA binding domain fusions of yeast proteins associated with the actin cytoskeleton, septins, the secretory apparatus, and Rho-type GTPases were used to screen an array of yeast transformants that express ∼90% of the predicted Saccharomyces cerevisiae open reading frames as Gal4 activation domain fusions. 191 protein–protein interactions were detected, of which 128 had not been described previously. 44 interactions implicated 20 previously uncharacterized proteins in cell polarity development. Further insights into possible roles of 13 of these proteins were revealed by their multiple two-hybrid interactions and by subcellular localization. Included in the interaction network were associations of Cdc42 and Rho1 pathways with proteins involved in exocytosis, septin organization, actin assembly, microtubule organization, autophagy, cytokinesis, and cell wall synthesis. Other interactions suggested direct connections between Rho1- and Cdc42-regulated pathways; the secretory apparatus and regulators of polarity establishment; actin assembly and the morphogenesis checkpoint; and the exocytic and endocytic machinery. In total, a network of interactions that provide an integrated response of signaling proteins, the cytoskeleton, and organelles to the spatial cues that direct polarity development was revealed.

Interpreting genome sequences requires the functional analysis of thousands of predicted proteins, many of which are uncharacterized and without obvious homologs. To assess whether the roles of large sets of uncharacterized genes can be assigned by targeted application of a suite of technologies, we used four complementary protein-based methods to analyze a set of 100 uncharacterized but essential open reading frames (ORFs) of the yeast Saccharomyces cerevisiae. These proteins were subjected to affinity purification and mass spectrometry analysis to identify copurifying proteins, two-hybrid analysis to identify interacting proteins, fluorescence microscopy to localize the proteins, and structure prediction methodology to predict structural domains or identify remote homologies. Integration of the data assigned function to 48 ORFs using at least two of the Gene Ontology (GO) categories of biological process, molecular function, and cellular component; 77 ORFs were annotated by at least one method. This combination of technologies, coupled with annotation using GO, is a powerful approach to classifying genes.

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The use of in vivo Fö rster resonance energy transfer (FRET) data to determine the molecular architecture of a protein complex in living cells is challenging due to data sparseness, sample heterogeneity, signal contributions from multiple donors and acceptors, unequal fluorophore brightness, photobleaching, flexibility of the linker connecting the fluorophore to the tagged protein, and spectral cross-talk. We addressed these challenges by using a Bayesian approach that produces the posterior probability of a model, given the input data. The posterior probability is defined as a function of the dependence of our FRET metric FRET R on a structure (forward model), a model of noise in the data, as well as prior information about the structure, relative populations of distinct states in the sample, forward model parameters, and data noise. The forward model was validated against kinetic Monte Carlo simulations and in vivo experimental data collected on nine systems of known structure. In addition, our Bayesian approach was validated by a benchmark of 16 protein complexes of known structure. Given the structures of each subunit of the complexes, models were computed from synthetic FRET R data with a distance root-mean-squared deviation error of 14 to 17 Å . The approach is implemented in the open-source Integrative Modeling Platform, allowing us to determine macromolecular structures through a combination of in vivo FRET R data and data from other sources, such as electron microscopy and chemical cross-linking. Molecular & Cellular

The localization of proteins can give important clues about their function and help sort data from large-scale proteomic screens. Forty-five proteins were tagged with the GFP variant YFP. These proteins were chosen because they are encoded by genes that display strong cell cycle-dependent expression that peaks in G 1 . Most of these proteins localize to either the nucleus or to sites of cell growth. We are able to assign new cellular component GO terms to ASF2, TOS4, RTT109, YBR070C, YKR090W, YOL007C, YOL019W and YPR174C. We also have localization data for 21 other proteins. Noteworthy localizations were found for Rfa1p, a member of the DNA replication A complex, and Pri2p and Pol12p, subunits of the α-DNA polymerase : primase complex. In addition to its nuclear localization, Rfa1p assembled into cytoplasmic foci adjacent to the nucleus in cells during the G 1 -S phase transition of the cell cycle. Pri2 and Pol12 took on a beaded appearance at the G 1 -S transition and later in the cell cycle were enriched in the nuclear envelope. A new spindle pole body/nuclear envelope component encoded by YPR174 was identified. The cell cycle-dependent abundance of Tos4p mirrored Yox1p and these two proteins were the only proteins that were found exclusively at the G 1 -S phase of the cell cycle. A complete list of localizations, along with images, can be found at our website (http://www.yeastrc.org/cln2/).