Biology subjects

Moellering, R. E.

Publications and source records attributed to Moellering, R. E..

6 recordsLinked to original sources

bioRxiv2025

Molecular Dynamics-Guided Design and Chemoproteomic Profiling of Covalent Kinase Activity Probes

Covalent small molecule activity probes can be powerful tools to interrogate protein function in native cellular environments. The design of family-wide activity probes requires an understanding of the molecular sources of general targeting potential and specificity to enable broad targeting of protein family members. Here, we developed and applied a multifaceted docking and molecular dynamics (MD) simulation pipeline to design and test cell-permeable covalent kinase activity probes from a set of hinge-binding pharmacophores. This computationally-guided approach yielded a new cell-active probe, K60P, which targets around 114 kinases across distinct kinase classes in live cells. Chemoproteomic profiling of this probe and a clinical candidate sharing the same indazole core, KW-2449, identified kinase and non-kinase target profiles that differ from recombinant protein assay profiles, underscoring the utility of native kinase profiling in situ. Biochemical studies with a model target kinase, ABL1, confirmed covalent labeling of the active site lysine across several kinase probes with distinct kinetics, as well as covalent labeling of key tyrosines in trans between ABL1 monomers. Finally, focused proteomics, kinetic modeling, and molecular dynamics simulations revealed that K60P, as well as the comparator probe XO44, preferentially engage with target kinases in their active, DFG-in conformations, which is driven by increasing population of reaction-ready small molecule conformation. These results together establish a computational and kinetic modeling framework for designing covalent activity probes and highlight the balance of target selectivity and kinetic efficiency as a key factor in determining their proteome-wide reactivity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/683178v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1ea3e5forg.highwire.dtl.DTLVardef@1a4e999org.highwire.dtl.DTLVardef@1e468c3org.highwire.dtl.DTLVardef@99e04e_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Family-Wide Photoproximity Profiling of Integrin Protein Social Networks in Cancer

Integrin family transmembrane receptors mediate dynamic interactions between cells and their extracellular microenvironment. The heterogeneous interaction partners of integrins directly regulate cell adhesion, motility, proliferation, and intracellular signaling. Despite the recognized importance of protein-protein interactions and the formation of signaling hubs around integrins, the ability to detect and quantify these dynamic binding partners with high spatial and temporal resolution remains challenging. Here, we developed an integrin-family-directed quantitative photoproximity protein interaction (PhotoPPI) profiling method to detect and quantify native integrin-centered protein social networks on live cells and tissues without the need for genetic manipulation, antibodies, or non-physiologic cell culture conditions. We drafted quantitative maps of integrin-centered protein social networks, highlighting conserved and unique binding partners between different cell types and cellular microenvironments. Comparison of integrin social networks in cancer cell lines of diverse tissue of origin and disease state identified specific AND-gate binding partners involved cell migration, microenvironmental interactions and proliferation that serve as markers of tumor cell metastatic state. Finally, we identified unique combinations - or barcodes - of integrin-proximal proteins on the surface of pre- and post-metastatic triple negative breast cancer (TNBC) cells whose expression strongly correlate with both positive and negative disease progression and outcomes in TNBC patients. Taken together, these data provide the first family-wide high-resolution maps of native protein interactors on live cells and identify dynamic integrin-centered social networks as potential AND-gate markers of cell identity, microenvironmental context and disease state.

cancer biology↗

bioRxiv2021

SARS-CoV-2 diverges from other betacoronaviruses in only partially activating the IRE1α/XBP1 ER stress pathway in human lung-derived cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has killed over 6 million individuals worldwide and continues to spread in countries where vaccines are not yet widely available, or its citizens are hesitant to become vaccinated. Therefore, it is critical to unravel the molecular mechanisms that allow SARS-CoV-2 and other coronaviruses to infect and overtake the host machinery of human cells. Coronavirus replication triggers endoplasmic reticulum (ER) stress and activation of the unfolded protein response (UPR), a key host cell pathway widely believed essential for viral replication. We examined the master UPR sensor IRE1 kinase/RNase and its downstream transcription factor effector XBP1s, which is processed through an IRE1-mediated mRNA splicing event, in human lung-derived cells infected with betacoronaviruses. We found human respiratory coronavirus OC43 (HCoV-OC43), Middle East respiratory syndrome coronavirus (MERS-CoV), and murine coronavirus (MHV) all induce ER stress and strongly trigger the kinase and RNase activities of IRE1 as well as XBP1 splicing. In contrast, SARS-CoV-2 only partially activates IRE1 through autophosphorylation, but its RNase activity fails to splice XBP1. Moreover, while IRE1 was dispensable for replication in human cells for all coronaviruses tested, it was required for maximal expression of genes associated with several key cellular functions, including the interferon signaling pathway, during SARS-CoV-2 infection. Our data suggest that SARS-CoV-2 actively inhibits the RNase of autophosphorylated IRE1, perhaps as a strategy to eliminate detection by the host immune system. IMPORTANCESARS-CoV-2 is the third lethal respiratory coronavirus after MERS-CoV and SARS-CoV to emerge this century, causing millions of deaths world-wide. Other common coronaviruses such as HCoV-OC43 cause less severe respiratory disease. Thus, it is imperative to understand the similarities and differences among these viruses in how each interacts with host cells. We focused here on the inositol-requiring enzyme 1 (IRE1) pathway, part of the host unfolded protein response to virus-induced stress. We found that while MERS-CoV and HCoV-OC43 fully activate the IRE1 kinase and RNase activities, SARS-CoV-2 only partially activates IRE1, promoting its kinase activity but not RNase activity. Based on IRE1-dependent gene expression changes during infection, we propose that SARS-CoV-2 prevents IRE1 RNase activation as a strategy to limit detection by the host immune system.

microbiology↗

bioRxiv2021

Non-conserved metabolic regulation by LKB1 distinguishes human and mouse lung adenocarcinoma

KRAS is the most frequently mutated oncogene in human lung adenocarcinomas (hLUAD) and activating mutations in KRAS frequently co-occur with loss-of-function mutations in the tumor suppressor genes, TP53 or STK11/LKB1. However, mutation of all three genes is rarely observed in hLUAD, even though engineered mutations of all three genes produces a highly aggressive lung adenocarcinoma in mice (mLUAD). Here we provide an explanation of this difference between hLUAD and mLUAD by uncovering an evolutionary divergence in regulation of the glycolytic enzyme triosephosphate isomerase (TPI1). Using KRAS/TP53 mutant hLUAD cell lines, we show that TPI1 enzymatic activity can be altered via phosphorylation at Ser21 by the Salt Inducible Kinases (SIKs) in an LKB1-dependent manner; this allows modulation of glycolytic flux between completion of glycolysis and production of glycerol lipids. This metabolic flexibility appears to be critical in rapidly growing cells with KRAS and TP53 mutations, explaining why loss of LKB1 creates a metabolic liability in these tumors. In mice, the amino acid at position 21 of TPI1 is a Cys residue which can be oxidized to alter TPI1 activity, allowing regulation of glycolytic flux balance without a need for SIK kinases or LKB1. Our findings reveal an unexpected role for TPI1 in metabolic reprogramming and suggest that LKB1 and SIK family kinases are potential targets for treating KRAS/TP53 mutant hLUAD. Our data also provide a cautionary example of the limits of genetically engineered murine models as tools to study human diseases such as cancers.

cancer biology↗

bioRxiv2021

Diverse Hotspot Thermal Profiling Methods Detect Phosphorylation-Dependent Changes in Protein Stability

Hotspot thermal profiling (HTP) methods utilize modified-peptide level information in order to interrogate proteoform-specific stability inside of live cells. The first demonstration of HTP involved the integration of phosphopeptide enrichment into a TMT-based, single-LC separation thermal profiling workflow1. Here we present a new label-fractionate-enrich (LFE)-HTP method that involves high-pH reverse phase fractionation of TMT-labeled peptides prior to phosphopeptide enrichment, followed by peptide detection and quantitation using multi-notch LC-MS3. We find that LFE-HTP, while more resource intensive, improves the depth and precision of (phospho)proteoform coverage relative to the initial published HTP workflow. The fraction of detected phosphorylation sites that are significantly perturbed in this new dataset are consistent with those seen in our previous study, as well as those published by others, when compared head-to-head with the same analysis pipelines. Likewise, many hotspot phosphorylation sites identified in our paper are consistently reproduced by LFE-HTP as well as other modified HTP methods. The LFE-HTP dataset contains many novel hotspot phosphorylation sites that regulate the stability of diverse proteins, including phosphosites in the central glycolytic enzyme Aldolase A that are associated with monomer-to-oligomer formation, enzymatic activity and metabolic regulation in cancer cells. Our comparative analyses confirm that several variants of the HTP method can track modified proteoforms in live cells to detect and prioritize PTM-dependent changes in protein stability that may be associated with function.

biochemistry↗

bioRxiv2019

Photoproximity profiling of protein-protein interactions in cells

We report a novel photoproximity protein interaction (PhotoPPI) profiling method to map protein-protein interactions in vitro and in live cells. This approach utilizes a bioorthogonal, multifunctional chemical probe that can be targeted to a genetically encoded protein of interest (POI) through a modular SNAP-Tag/benzylguanine covalent interaction. A first generation photoproximity probe, PP1, responds to 365 nm light to simultaneously cleave a central nitroveratryl linker and a peripheral diazirine group, resulting in diffusion of a highly reactive carbene nucleophile away from the POI. We demonstrate facile probe loading, and subsequent interaction- and light-dependent proximal labeling of a model protein-protein interaction (PPI) in vitro. Integration of the PhotoPPI workflow with quantitative LC-MS/MS enabled un-biased interaction mapping for the redox regulated sensor protein, KEAP1, for the first time in live cells. We validated known and novel interactions between KEAP1 and the proteins PGAM5 and HK2, among others, under basal cellular conditions. By contrast, comparison of PhotoPPI profiles in cells experiencing metabolic or redox stress confirmed that KEAP1 sheds many basal interactions and becomes associated with known lysosomal trafficking and proteolytic proteins like SQSTM1, CTSD and LGMN. Together, these data establish PhotoPPI as a method capable of tracking the dynamic sub-cellular and protein interaction "social network" of a redox-sensitive protein in cells with high temporal resolution. SYNOPSIS TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=51 SRC="FIGDIR/small/833384v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@5319bborg.highwire.dtl.DTLVardef@1be7ea0org.highwire.dtl.DTLVardef@16b51d6org.highwire.dtl.DTLVardef@c9d400_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗