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The E571K mutation of CRM1 is highly prevalent in some cancers, but its mechanism of tumorigenesis is unclear. Glu571 of CRM1 is located in its nuclear export signal (NES)-binding groove, suggesting that binding of select NESs may be altered. We generated HEK 293 cells with either monoallelic CRM1WT/E571K or biallelic CRM1E571K/E571K using CRISPR/Cas9. We also combined analysis of binding affinities and structures of 27 diverse NESs for wild-type and E571K CRM1 with structure-based bioinformatics. While most NESs bind the two CRM1 similarly, NESs from Mek1, eIF4E-transporter, and RPS2 showed >10-fold affinity differences. These NESs have multiple charged side chains binding close to CRM1 position 571, but this feature alone was not sufficient to predict different binding to CRM1(E571K). Consistent with eIF4E-transporter NES binding weaker to CRM1(E571K), eIF4E-transporter was mislocalized in tumor cells carrying CRM1(E571K). This serves as proof of concept that understanding how CRM1(E571K) affects NES binding provides a platform for identifying cargoes that are mislocalized in cancer upon CRM1 mutation. Finally, we showed that large affinity changes seen with some NES peptides (of Mek1 and RPS2) do not always translate to the full-length cargoes, suggesting limitations with current NES prediction methods. Therefore, comprehensive studies like ours are imperative to identify CRM1 cargoes with real pathogenic potential.

Motivation: The consensus pattern of Nuclear Export Signal (NES) is a short sequence motif that is commonly identified in protein sequences, whether the motif acts as an NES (true positive) or not (false positive). Finding more plausible NES functioning regions among the vast array of consensus-matching segments would provide an interesting resource for further experimental validation. Better defined NES should also allow meaningful mapping of cancer-related mutation positions, leading to plausible explanations for the relationship between nuclear export and disease., Results: Possible NES candidate regions are extracted from the cancer-related human reference proteome. Extracted NES are scored for reliability by combining sequence-based and structure-based approaches. The confidently identified NES candidate motifs were checked for overlap with cancer-related mutation positions annotated in the COSMIC database. Among the ∼700 cancer-related sequences in the COSMIC Cancer Gene Census, 178 sequences are predicted to have possible NES motifs containing cancer-related mutations at their key positions. These lists are organized into our database (pCRM1exportome), and other protein sequences in the human reference proteome can also be retrieved by their UniProt IDs., Availability and Implementation: The database is freely available at http://prodata.swmed.edu/pCRM1exportome., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Nuclear export signal (NES) motifs function as essential regulators of the subcellular location of proteins by interacting with the major nuclear exporter protein, CRM1. Prediction of NES is of great interest in many aspects of research including cancer, but currently available methods, which are mostly based on the sequence-based approaches, have been suffered from high false positive rates since the NES consensus patterns are quite commonly observed in protein sequences. Therefore, finding a feature that can distinguish real NES motifs from false positives is desired to improve the prediction power, but it is quite challenging when only using the sequence. Here, we provide a comprehensive table for the validated cargo proteins, containing the location of the NES consensus patterns with the disordered propensity plots, known protein domain information, and the predicted secondary structures. It could be useful for determining the most plausible NES region in the context of the whole protein sequence and suggests possibilities for some non-binders of the annotated regions. In addition, using the currently available crystal structures of CRM1 bound to various classes of NES peptides, we adopted, for the first time, the structure-based prediction of the NES motifs bound to the CRM1's binding groove. Combining sequence-based and structure-based predictions, we suggest a novel and more straight-forward approach to identify CRM1-binding NES sequences by analysis of their structural prerequisites and energetic evaluation of the stability at the CRM1's binding site.

Liquid-liquid phase separation (LLPS) is believed to underlie formation of biomolecular condensates, cellular compartments that concentrate macromolecules without surrounding membranes. Physical mechanisms that control condensate formation/dissolution are poorly understood. The RNA-binding protein fused in sarcoma (FUS) undergoes LLPS in vitro and associates with condensates in cells. We show that the importin karyopherin-β2/transportin-1 inhibits LLPS of FUS. This activity depends on tight binding of karyopherin-β2 to the C-terminal proline-tyrosine nuclear localization signal (PY-NLS) of FUS. Nuclear magnetic resonance (NMR) analyses reveal weak interactions of karyopherin-β2 with sequence elements and structural domains distributed throughout the entirety of FUS. Biochemical analyses demonstrate that most of these same regions also contribute to LLPS of FUS. The data lead to a model where high-affinity binding of karyopherin-β2 to the FUS PY-NLS tethers the proteins together, allowing multiple, distributed weak intermolecular contacts to disrupt FUS self-association, blocking LLPS. Karyopherin-β2 may act analogously to control condensates in diverse cellular contexts., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Using wheat germ acid phosphatase and sodium orthovanadate as a competitive inhibitor, a novel method for analyzing reversible inhibition was carried out. Our alternative approach involves plotting the initial velocity at which product is formed as a function of the ratio of substrate concentration to inhibitor concentration at a constant enzyme concentration and constant assay conditions. The concept of initial concentrations driving equilibrium leads to the chosen axes. Three apparent constants can be derived from this plot: K max , K min , and K inflect . K max and K min represent the substrate to inhibitor concentration ratio for complete inhibition and minimal inhibition, respectively. K inflect represents the substrate to inhibitor concentration ratio at which the enzyme-substrate complex is equal to the inhibitory complex. These constants can be interpolated from the graph or calculated using the first and second derivative of the plot. We conclude that a steeper slope and a shift of the line to the right (increased x-axis values) would indicate a better inhibitor. Since initial velocity is not a linear function of the substrate/inhibitor ratio, this means that inhibition changes more quickly with the change in the [S]/ [I] ratio. When preincubating the enzyme with substrate before the addition of inhibitor, preincubating the enzyme with inhibitor before the addition of substrate or with concurrent addition of both substrate and inhibitor, modest changes in the slopes and y-intercepts were obtained. This plot appears useful for known competitive and non-competitive inhibitors and may have general applicability.