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Comparative Study
. 2005 Aug 23;102(34):12123-8.
doi: 10.1073/pnas.0505482102. Epub 2005 Aug 10.

Large-scale identification of yeast integral membrane protein interactions

John P Miller  1 , Russell S Lo, Asa Ben-Hur, Cynthia Desmarais, Igor Stagljar, William Stafford Noble, Stanley Fields
Affiliations
Comparative Study

Large-scale identification of yeast integral membrane protein interactions

John P Miller et al. Proc Natl Acad Sci U S A. .

Abstract

We carried out a large-scale screen to identify interactions between integral membrane proteins of Saccharomyces cerevisiae by using a modified split-ubiquitin technique. Among 705 proteins annotated as integral membrane, we identified 1,985 putative interactions involving 536 proteins. To ascribe confidence levels to the interactions, we used a support vector machine algorithm to classify interactions based on the assay results and protein data derived from the literature. Previously identified and computationally supported interactions were used to train the support vector machine, which identified 131 interactions of highest confidence, 209 of the next highest confidence, 468 of the next highest, and the remaining 1,085 of low confidence. This study provides numerous putative interactions among a class of proteins that have been difficult to analyze on a high-throughput basis by other approaches. The results identify potential previously undescribed components of established biological processes and roles for integral membrane proteins of ascribed functions.

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Figures

Fig. 1.
Fig. 1.
The split-ubiquitin membrane yeast two-hybrid system. (A) Two integral membrane proteins (blue and green) are fused to the two halves of ubiquitin and expressed in cells of opposite mating type. Upon mating, the diploid coexpresses the proteins. An interaction brings the two halves of ubiquitin into close proximity, forming a reconstituted molecule that is cleaved by ubiquitin-specific proteases, releasing the transcription factor to enter the nucleus and activate HIS3 gene transcription. (B) Yeast expressing YJR015w as a Cub-PLV fusion were mated to an array of yeast expressing the ORF-HA2-NubGs. Upper shows the diploids containing YJR015w-Cub-PLV and each of the HA2-NubG fusions or controls on media that selects for the plasmids. Lower shows the growth on media selective for reporter gene activation. Putative YJR015w interactors are labeled by the name of the protein fused to the HA2-NubG moiety. Black boxes show positive controls (NubI fusions onto the amino terminus of Ste14 and Alg5 and free NubI) and negative controls (NubG alone, empty vector).
Fig. 2.
Fig. 2.
The integral membrane protein interaction data set. (A) The number of Cub-PLV and HA2-NubG fusions that identified interactions. (B) Reproducibility of the interactions. (C) Overlap of the interaction data with that found in the General Repository for Interaction Data and Database of Interacting Proteins databases of protein interactions. Two additional interactions, found in the literature but absent in the databases, were also included.
Fig. 3.
Fig. 3.
Heat maps of SVM classified interactions. The 131 interactions classified as true by all of the SVM runs are compared with 131 (of 1,085) interactions not classified as true by any SVM run. The latter were selected based on the two proteins' GO biological process annotations being the most closely related. Interactions are presented as rows and features as columns (in the order presented in Table 1) in descending order of ranking by related GO annotation. The heat maps are normalized on a scale of zero to four, with the more desirable features for an interaction indicated by a four (red). Yellow boxes highlight: (1) the mutual clustering coefficients versus the whole yeast network, (2) “related GO,” and (3) “Ihmels transcriptional modules,” which are strongly enriched in the high confidence group relative to the low confidence group. The heat maps were generated by using matrix2png (33).
Fig. 4.
Fig. 4.
Interactions validated by the SVM runs. Purple lines indicate interactions used in the positive training set. (A) Interactions of translocation channel proteins. (B) Interactions of Emp47 and Emp46. (C) Interactions of Pho88 and Pho84. For clarity, only the direct interaction partners of Pho84 and Pho88 that are identified as true by >50% of the SVM runs are presented; interconnections among proteins in the cluster of less confidence are included. (D) Interactions of sterol metabolism proteins. Only direct interactions of Erg11 found to be true by at least one SVM run are shown. (E) Interactions involved in HDEL-mediated ER retention. Only interactions of Erd1 and Erd2 identified as true by at least one SVM run are presented. (F) Interactions of COPII vesicle proteins. Only interactions of Emp24 supported by all SVM runs are shown. (G) Interactions of Shr3. Only interactions predicted as true by at least half of the SVM runs are shown.
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