No abstract

SummaryDss1 (also known as Sem1) is a conserved, intrinsically disordered protein with a remarkably broad functional diversity. It is a proteasome subunit but also associates with the BRCA2, RPA, Csn12-Thp1, and TREX-2 complexes. Accordingly, Dss1 functions in protein degradation, DNA repair, transcription, and mRNA export. Here in Schizosaccharomyces pombe, we expand its interactome further to include eIF3, the COP9 signalosome, and the mitotic septins. Within its intrinsically disordered ensemble, Dss1 forms a transiently populated C-terminal helix that dynamically interacts with and shields a central binding region. The helix interfered with the interaction to ATP-citrate lyase but was required for septin binding, and in strains lacking Dss1, ATP-citrate lyase solubility was reduced and septin rings were more persistent. Thus, even weak, transient interactions within Dss1 may dynamically rewire its interactome.

Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by predicting and validating the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.

Exocytosis involves fusion of secretory vesicles with the plasma membrane, thereby delivering membrane proteins to the cell surface and releasing material into the extracellular space. The tethering of the secretory vesicles before membrane fusion is mediated by the exocyst, an essential phylogenetically conserved octameric protein complex. Exocyst biogenesis is regulated by several processes, but the mechanisms by which the exocyst is degraded are unknown. Here, to unravel the components of the exocyst degradation pathway, we screened for extragenic suppressors of a temperature-sensitive fission yeast strain mutated in the exocyst subunit Sec3 (). One of the suppressing DNAs encoded a truncated dominant-negative variant of the 26S proteasome subunit, Rpt2, indicating that exocyst degradation is controlled by the ubiquitin-proteasome system. The temperature-dependent growth defect of the strain was gene dosage-dependent and suppressed by blocking the proteasome, Hsp70-type molecular chaperones, the Pib1 E3 ubiquitin-protein ligase, and the deubiquitylating enzyme Ubp3. Moreover, defects in cell septation, exocytosis, and endocytosis in mutant strains were similarly alleviated by mutation of components in this pathway. We also found that, particularly under stress conditions, wild-type Sec3 degradation is regulated by Pib1 and the 26S proteasome. In conclusion, our results suggest that a cytosolic protein quality control pathway monitors folding and proteasome-dependent turnover of an exocyst subunit and, thereby, controls exocytosis in fission yeast.

Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.