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A long-standing effort in biology is to precisely define and group phenotypes that characterize a biological process, and the genes that underpin them. In Saccharomyces cerevisiae and other organisms, functional screens have generated rich lists of phenotypes associated with individual genes. However, it is often challenging to identify sets of phenotypes and genes that are most closely associated with a given biological process. Here, we focused on the 166 phenotypes arising from loss-of-function and the 86 phenotypes from gain-of-function mutations in 571 genes currently assigned to cell cycle-related ontologies in S. cerevisiae To reduce this complexity, we applied unbiased, computational approaches of correspondence analysis to identify a minimum set of phenotypic variables that accounts for as much of the variability in the data as possible. Loss-of-function phenotypes can be reduced to 20 dimensions, while gain-of-function ones to 14 dimensions. We also pinpoint the contributions of phenotypes and genes in each set. The approach we describe not only simplifies the categorization of phenotypes associated with cell cycle progression but might also potentially serve as a discovery tool for gene function., (Copyright © 2020 Bermudez et al.)

Understanding how proteins are able to form stable complexes is of fundamental interest from the perspective of protein structure and function. Here we show that lambda repressor fusions can be used to identify and characterize homotypic interaction domains encoded by the genome of Saccharomyces cerevisiae, using a selection for polypeptides that can drive the assembly of the DNA binding domain of bacteriophage lambda repressor. Three high complexity libraries were constructed by cloning random fragments of S. cerevisiae DNA as lambda repressor fusions. Repressor fusions encoding homotypic interactions were recovered, identifying oligomerization units in 35 yeast proteins. Seventeen of these interaction domains have not been previously reported, while the other 18 represent homotypic interactions that have been characterized at varying levels of detail. The novel interactions include several predicted coiled-coils as well as domains of unknown structure. With the availability of genomic sequences it should be possible to apply this approach, which provides information about protein-protein interactions that is complementary to that obtained from yeast two-hybrid screens, on a genome-wide scale in yeast or other organisms where large-scale protein-protein interaction data is not available., (Copyright 2002 John Wiley & Sons, Ltd.)

Comments on the research conducted by researchers John R.S. Newman and colleagues which developed a method for identifying protein-protein interactions in budding yeast Saccharomyces cerevisiae. Estimation of coiled-coils from the yeast genome using the MULTICOIL program; Percentage of yeast proteins predicted to contain a coiled coil.