Folliculin variants linked to Birt-Hogg-Dubé syndrome are targeted for proteasomal degradation.

Germline mutations in the folliculin (FLCN) tumor suppressor gene are linked to Birt-Hogg-Dubé (BHD) syndrome, a dominantly inherited genetic disease characterized by predisposition to fibrofolliculomas, lung cysts, and renal cancer. Most BHD-linked FLCN variants include large deletions and splice site aberrations predicted to cause loss of function. The mechanisms by which missense variants and short in-frame deletions in FLCN trigger disease are unknown. Here, we present an integrated computational and experimental study that reveals that the majority of such disease-causing FLCN variants cause loss of function due to proteasomal degradation of the encoded FLCN protein, rather than directly ablating FLCN function. Accordingly, several different single-site FLCN variants are present at strongly reduced levels in cells. In line with our finding that FLCN variants are protein quality control targets, several are also highly insoluble and fail to associate with the FLCN-binding partners FNIP1 and FNIP2. The lack of FLCN binding leads to rapid proteasomal degradation of FNIP1 and FNIP2. Half of the tested FLCN variants are mislocalized in cells, and one variant (ΔE510) forms perinuclear protein aggregates. A yeast-based stability screen revealed that the deubiquitylating enzyme Ubp15/USP7 and molecular chaperones regulate the turnover of the FLCN variants. Lowering the temperature led to a stabilization of two FLCN missense proteins, and for one (R362C), function was re-established at low temperature. In conclusion, we propose that most BHD-linked FLCN missense variants and small in-frame deletions operate by causing misfolding and degradation of the FLCN protein, and that stabilization and resulting restoration of function may hold therapeutic potential of certain disease-linked variants. Our computational saturation scan encompassing both missense variants and single site deletions in FLCN may allow classification of rare FLCN variants of uncertain clinical significance.

Accurate protein stability predictions from homology models.

Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.